Utopia, Inc.

EAM – Anyone for Mining “Hidden” Dollars?

2011-12-13T06:47:00.005-06:00

Surely you’re thinking, “How can dollars be ‘hidden’ in enterprise asset management (EAM) systems, waiting to be mined or uncovered?”

Indeed, there are vast treasure troves of value waiting to be mined and liberated in EAM systems! In order to access these untapped veins of ore in your company’s EAM implementation you’ll need to know the best practices to apply.

EAM systems are used by companies in capital-intensive sectors, such as oil and gas, utilities, and transportation, for a variety of purposes, such as:

Tracking capital equipment and assets in far-flung areas in their global operations
Managing preventive maintenance schedules and requirements of expensive assets
Monitoring and logging emissions from equipment for regulatory reporting and compliance
Managing spare parts inventories that themselves can be capital items on a balance sheet

Each item of capital equipment has many data attributes that need to be accurately collected, updated, and maintained within a company’s EAM system.

Data can be as basic as a manufacturer’s name, equipment model, and serial number, or as detailed as the bill of materials for that item, maintenance schedules and diagrams, spare parts inventory requirements, and everything else that is worth tracking over the equipment’s lifecycle.

Starting from data collection, there are many ways in which equipment data can be of poor quality, whether by omission of key elements, or by inconsistent and incomplete data entry into legacy databases. Sometimes the omissions are due to the very simple fact that when complex equipment is procured, its constituent parts are not all called out comprehensively.

At other times, such as when one company acquires another, diverse sources of equipment data, in very different formats, need to be merged into one database.

Not having accurate or complete equipment data can lead to a lot of headaches, such as unexpected equipment downtime due to missed maintenance, or spare parts not being at hand to recover from a malfunction. A single event of this type can costs tens of millions of dollars in lost revenue alone.

Preventing problems resulting from poor data quality in EAM systems thus becomes a critical step in mining the hidden dollars that are otherwise squandered away in completely avoidable operational crises.

Recently a project that I was actively involved with was able to mine hidden dollars with EAM utilizing data quality in order to maximize returns. This method allowed the large petrochemical company to avoid a large cost and was able to mine one million hidden dollars for the company. So what are you waiting for? Put your miners’ hat on and let’s get that treasure!

Herb Hadley, Director, Solutions Development - Global EAM Practice Lead, Utopia, Inc.

EAM: Equipment Data Collection – What and Why

2011-09-21T08:15:00.000-05:00

A recent customer discussion reminded me that even the guys who have been in database management and/or equipment maintenance for decades may need a refresher on what equipment data is important and why. The perceived requirements for equipment data collection typically vary from “we only need the basics” to “give us all the information we could possibly need – just in case we need it.” Both ends of this spectrum of opinion can be correct . . . but not at the same time!

The “just give me the basics” guys will probably get less information than will be needed to accurately identify a specific equipment. The “give me everything” guys will probably generate more information than their EAM software system allows them to enter. Likewise, some of the “everything” equipment information may be of little value to the maintenance department.

So how should a manager decide what equipment information to include in the EAM system?

In future blog entries, I’ll discuss the how equipment criticality, spares strategy, development of bills of materials (BOMs), purchasing and inventory management all affect this process . . . but for now let’s look at some basics.

The What and Why of Data Collection

Equipment Class: needed to set up maintenance strategies and cost centers. Also important in setting up equipment hierarchies and functional locations.

Equipment Description: provides a standard way to describe what you plan to maintain. Establishing noun/modifier and taxonomy are part of this process.

Unique Identifier: a RFID or barcode number allows the maintenance group to find and maintain specific equipment, allocate the costs associated with that specific equipment and track the location/status of that specific equipment – in operation, in repair, in inventory, etc.

Manufacturer: needed for cost of ownership/maintenance tracking

Model: needed to build BOMs and track costs

Serial Number: another unique identifier that ties the specific equipment to a purchase order and/or installation date. Use of the serial number as the sole unique identifier is problematic since reading the equipment name plate may be more difficult than reading a bar code or RFID.

Date of Manufacture: important for establishing warranty claims, especially if the date of installation is in doubt

Do you need all this information? Probably. Do you need to collect more equipment information? Maybe. The decision on what data to collect should be made based on how you expect to use the information.

Herb Hadley, Director of Solutions Consulting, Utopia, Inc.

What’s Next After You Get the Customer Right?

2011-08-24T14:34:00.005-05:00

In our efforts to identify unique customers, so we can target and cross-sell, we ultimately come to the next big step in the retail information supply chain: product data. Then of course, right after product data is vendor data. To excel at direct marketing you need to have cleansed and consolidated customer data, such as names, titles, addresses, and other contact info. But what about the product or service you are offering?

If you are an on-line cataloger or large manufacturer of a large retail product line you know that matching the right product to the right customer can be just as challenging as matching one customer to another across disparate data sources. The challenge is compounded when the manufacturer has five separate but similar product lines, like sporting goods lines, eye glass frame brands, or consumer electronics. Added to the compounding is product portfolio rationalization after a merger and acquisition. Simply said, your marketing department may have a good handle on a single view of the customer in the CRM system, but your view of product data across the disparate ERP and catalog systems is a mess.

Typically at this point the focus of the data management effort shifts from customer to product, and then in an effort to reconcile competing, opposing, or seemingly similar products, vendor data is added to the equation. Vendor data, depending on the business, can range from name and address of the supplier to chemicals used in production process and parts used in the component assembly. The data cleansing and consolidation operation has now decidedly shifted from attributes about human beings to attributes about products such as color, size, features, variants, classes, categories, prices, etc.

Because optimizing the retail information supply chain can be daunting, SAP and Utopia have decided to offer a Webinar on September 7 that delves into how to manage and cleanse the data in this chain. Join us in learning about the issues and techniques in solving this challenge, specifically:

Key retail-industry trends and opportunities
How consolidating product and vendor data is key to success in retailing
How to overcome obstacles in data quality to enable data governance

We’ll be able to cover much more ground than in this blog post, and carry the discussion to the next step.

Find further information on the Webinar here.

Frank Dravis, Marketing Leader, Utopia, Inc.

Data – It Can Make You or Break You

2011-06-15T06:59:00.007-05:00

Data is crucial to run a business. Analyzing the business data and taking the appropriate actions accordingly are key elements to better business practices. A friend of mine had a very lucrative digital printing and computer business where he used to repair/troubleshoot customer computers and had a big digital printing plant. The business was running well, but the main problem was he never preserved or archived the sales data and never bothered to look or analyze it. He didn’t realize the importance of archiving data, tracking trends e.g. best selling services, where the weaknesses laid, which products or services needed to be reconsidered or discontinued in order to increase profitability. A few years later he was at a point where he thought he might be losing his business but he never dared to look at the data and analyze it. His hunch was that the overall costs of goods were going over the overall sales.

For example…

Sales
	Items	Total Sales
	Laptop Repair	$220.00

COG
	Labor	Parts	Others	Total
	$100.00	$120.00	$50.00	$270.00

Revenue
	($50.00)

On one instance, he lost $50 for one laptop repair. This one instance lead to a number of repeatable instances which never were realized until later on when one day he was unable to buy materials to run his business because there was no money in the bank.

But since he never looked at the sales data, he couldn’t control this reoccurring troublesome trend brewing without his knowledge. In the end he had no money to pay the rent and his bank had a negative balance. To run a business, data is very important and he realized that right after he had lost the business.

To run any kind of business, everyone should have a good understanding of the sales data, so he/she can gain more control on the overall business. Then they would not hesitate to make the right decision because they would have the right information at hand. Due to my friends lack of knowledge regarding his businesses operational data hindered his ability to stay in business. There always is a proper way to handle data and slice and dice it correctly to suit your business needs. Being naive towards enterprise data can make our lives miserable.

Afm Rahman, Data Migration Consultant, Utopia, Inc.

Data Can Add 20% to a Plant’s Value

2011-04-05T07:18:00.002-05:00

A recent conversation with a venture capitalist brought home the financial benefits of having accurate and complete asset information. We discussed various ways to value asset-intensive businesses like refineries, power plants, and manufacturing facilities. Obvious valuation measurements include operational productivity, cash flow and a comparison with similar plants’ sales price. Two additional methods that caught my attention were asset (equipment) value and projected maintenance cost.

“Not having an accurate list of equipment, the equipment’s condition and an accurate history of maintenance cost typically causes me to discount the value of a plant by at least 20%,” stated the investor. “Lack of this information increases the risk of the transaction. Seller’s claims can only be taken seriously when those claims are documented in the maintenance software (SAP, Maximo, etc.).”

Buying or selling a plant doesn’t happen very often. Justifying a plant’s maintenance and IT budgets happen at least once per year! Use the “adds 20% to the value of the plant” comment the next time you have to justify the cost of cleansing your historical data or upgrading your (enterprise asset management) maintenance software.

Steps you can take now:

Perform a “Data Health Assessment™” to determine your data’s completeness, consistency, conformity and number of duplicates.
Check the accuracy of your spend reports. Are incomplete or confusing Bill of Materials causing costs to fall into too few accounting “buckets?”
Each piece of equipment you maintain should have a unique identifying number imprinted on a bar code label, RFID, or metal tag that is attached to the asset.

This number should be consistent with the identifying number in the maintenance software (SAP, Maximo, etc.).

At minimum; define your critical equipment and make sure the equipment information is correct and complete. Be consistent. Develop and maintain an equipment classification methodology and taxonomy.

Herb Hadley, Director of Solutions Consulting, Utopia, Inc.

HANA/BI and Data: Teaming to Manage in Real-Time

2011-03-15T07:13:00.003-05:00

Do you know HANA (High-performance ANalytic Appliance)? HANA-enabled BI (Business Intelligence) can deliver hundreds of billions of records of data literally in an instant, even remotely; hence enabling real-time business decisions (action) and thus helping businesses run more effectively. Still, this requires a human and the machine (software, applications, networks, infrastructure, computers, databases and the HANA/BI), a 2-system system, for optimal performance.

Immediately, what comes to mind are millions or nerve endings in a biological system (a human being) sending electrical impulses through a network of nerves, to a central neural network processing unit (the brain), instantaneously, where mission-critical decisions are made in real-time for survival.

Driving a car, your eyes sense the road ahead, your ears hear a loud horn, your senses are heightened as your brain tenses the muscles in your arms and legs poised to take evasive action, and in the blink of an eye you hit the brakes to avoid a child who just ran into the roadway.

The car computer senses these inputs and the Automatic Braking System (ABS) springs into action to provide you the shortest possible stopping distance with control over direction. This is a 2-system system, the human and the vehicle, working together to provide transport and safety – survival!

If the nerve pathways, senses, signals and neural processing unit had bad data (didn’t see the child, didn’t hear the sound) or had incorrect perceptions (again bad data, out-of-date signals too slow, etc.) then the appropriate and proper response (braking and steering) would not have happened at the right time, and someone would have been hurt – no survival.

Similarly, in business, when the right amount of response (appropriate) is executed in the right direction (steering) at the right time, then the business continues. Successive correct intelligence in business practically assures survival.

If any of that is wrong, it could mean disaster – missed shipping deadlines, stock-outs, missed marketing opportunities, missed vendor payments, missed collections … all contributing to lower ratios and poor stock performance – survival of the CEO? Survival of employee security? Survival of shareholders?

So the sensors create data. Other data models the real world so the onboard car computer knows what to do – that could be wrong too, or become corrupted. The infrastructure propagates the data and signals to a processing unit which aggregates all the signals and data into a picture, or a status of what’s happening in real-time, so business can be managed with appropriate and proper responses.

How does all of this depend on the quality, correctness, completeness, continuity, conformity, currency (timeliness) and construction of the underlying data in the system? How does the configuration of the data affect performance? It depends on many factors related to your particular business and industry. So how do you figure that out?

I’ll let you think about it. Here are a few clues…define, build, maintain, and govern HANA/BI data, a.k.a. your enterprise data. Until next time!

David Kuketz, Solutions Consultant, Utopia, Inc.

Data Consultant: Do you have the aptitude and attitude?

2011-03-09T07:13:00.002-06:00

Reflecting on my first blog post, “Data Consultant Etiquette - What’s Your Attitude?,” the situation presented in that scenario taught me that one can have lots of technical capabilities and can be rich in aptitude but without a proper attitude a person will gain nothing. I think it is important as a data consultant to have the appropriate balance of aptitude and attitude.

ATTITUDE can be described as:

How we communicate with the customers
Our overall behavior is also key part of attitude, like if you are working on-site with a customer your overall etiquette and ethics matter a lot.

The second characteristic is APTITUDE which has an equal importance in terms of customer relationship management from the business perspective.

Companies are hiring some pretty extraordinary professionals who have wonderful aptitude skills. These people devise innovative ideas which provide valid and refined approaches to solve client problems.

Having the right mixture of attitude and aptitude in turn makes our customers happy and satisfied which obviously at the end of the day will contribute to a company’s overall business success and retaining clients.

As you know in this crazy business world client’s must be treated a royalty. Remember the phrase “the customer is always right.” Their needs need to be satisfied with your wonderful and effective services and perfect solutions.

This all leads to the development of better relationships with new and old customers, and a number of benefits such as…

increased sales due past relationships and how you treated the customer
the ability to understand your customer and their environment more effectively
increased ability to cross-sell due to customer’s increased trust in you

Your attitude and aptitude highly affect the success of not only the project you’re deployed on, but also the company you work for. As you mature into a seasoned professional remember to work on your mix aptitude and attitude to get the right balance.

Shwetesh Deepak, Data Migration Consultant, Utopia, Inc.

Top 10 Reasons Why Data Quality Projects Fail

2011-03-01T06:46:00.002-06:00

Over the years of working in the data space I still am learning something new every day from my peers. They have provided me with a lot of their insights as to why data quality projects fail and I’ve shared with them my experiences as well. Below is the list that I’ve accumulated throughout the years. Let’s take a look.

Lack of sufficient time and investment in requirements gathering. Especially for development and validation of business rules and project plans.
Data quality is introduced too late into the project where best practices are compromised to attain aggressive project milestones.
A data profile is not conducted so the starting condition of the data is not fully understood.
The source of a data quality problem is identified but not fixed.
Users elect to forgo formal education, training, and/or professional implementation services.
Making file format and/or business rule changes on the fly during implementation.
Underestimating the impact of working with multiple data sources and inconsistent data formats.
Ignoring the complexity and additional time required to work with international data.
Not planning additional time to identify, plan, test, and implement advanced match strategies.
Lack of recognition that data cleansing, matching, and consolidation is an iterative and ongoing process that requires repetitive testing, optimization, and tuning to achieve the desired results.

I hope you find these reasons insightful and things to look out for when you are planning your next data quality project. Please feel free to contribute some of your top reasons. Would love to hear them.

Mike Umoleale, Senior Consultant, Utopia, Inc.

Keeping the MDM Dentists Away

2011-02-22T07:25:00.002-06:00

Starting a Master Data Management project is a lot like an emergency visit to the dentist’s office. Let’s look at some of the common traits:

Typically the issue incubates over time and is often neglected because it’s deemed as a minor annoyance or non-critical issue
The issue then escalates into something with a larger impact and the pain it causes become unbearable
The damage is usually irreversible and potentially becomes a very lengthy and/or costly exercise to fix
Post mortem reveals it’s probably attributed to a lack of discipline in ongoing maintenance

As such, both dentists and MDM professionals are generally not very well-liked by their clients. The main reasons are that they are always preaching the most commonsensical rhetoric about good dental (=data) health, importance of flossing and brushing (=data cleansing and quality assurance), and inevitably causing pain and distress during the consultation process as decay and cavities (=data inconsistencies and discrepancies) are being treated. And to add insult to injury, the clients have to then fork out tidy sums of money to get the advice they already know in the back of their minds, but are ill-equipped to clear up the mess that they made, because they haven’t exercise the discipline and vigilance required of them.

The point though, is that data health, as well as dental health, should never be an afterthought and deserves much more attention than it is normally given. Data health is a fundamental indicator to the overall well-being of the enterprise and is a key enabler of other critical arterial functions of the organization. It is thus important that there is strong emphasis in enforcement of discipline in data quality management and data integrity maintenance. Even as master data projects are being commissioned to address issues in master data management, the crux of the matter lies in the hands of the organization that will ultimately sustain and execute master data processes to facilitate smooth operations of the business. The more stringent the regimes for master data processes are, the better the chances are for avoiding pitfalls due to master data issues.

Just as individuals are responsible for their own dental upkeep, the organization needs to be accountable for data governance and stewardship. To accomplish this, professional services can be employed to tackle existing issues and tools can be leveraged to support ongoing maintenance, but in spite of the sophistication of master data management technologies, the success of a master data program really depends on the mindset of the people in front of the technology, not behind it.

Danny Thien Senior Manager, Solutions Consulting, Utopia, Inc.

Let’s Have the Data Conversation

2011-02-15T06:52:00.003-06:00

I have always said that good quality data is like money in the bank. However, while communicating the data value proposition to an organization, data practitioners sometimes speak in a language that can cause an immediate glaze to form over the eyes of the most seasoned executives. Schemas, taxonomies, and count nouns are important elements of data management but these terms will at best get a nodding head and if you are lucky a pat on the back with a “keep up the good work.”

If you really want to make an impact at your organization I suggest you have “the data conversation” as this conversation is all about business value and the health of your company in the long term. This is a strategic business conversation about your organizations data roadmap. The conversation speaks in the business language that your executives understand.

Your company has a strategic business vision with specific goals and objectives for the organization. These strategic plans serve as the business roadmap for the company and drive business decisions accordingly. In today’s business world information technology is increasingly becoming a critical driver for enabling the strategic business roadmap for your organization. The speed of change in the theater your company conducts business has pushed information technology groups to be innovative or run risk of losing your competitive position. So IT needs to be tightly aligned to the business roadmap and be increasingly innovative. This is a tall order but it is the reality that is shared in our industry.

So when do we have “the data conversation?” All the time! Data is foundational and it is the fuel that gives power to the application engines, reporting dashboards, and acts in a systemic way across all functions of your business. Data is the critical piece that your company needs to get right and be managed properly to have the opportunity to help drive your business’ roadmap to success. So I ask you… What does your data roadmap look like and is it aligned to your business? It’s a great conversation!

Dave Lepa, Director Canadian Sales, Utopia, Inc.

Why Data Matters to the CFO

2011-02-08T06:43:00.002-06:00

In my experience most CIO’s report up to the CFO. This often means IT innovation is sidelined by initiatives for operational excellence and cost savings through IT.

Smart CIO’s understand the hard line pressure and enforce the reality that projects not directly tied to the business goals of an organization are dead on arrival. Hence the political landscape is littered with those who don’t have enough clout to get their projects off the ground. But we know this already, don’t we? Is it clout? Or is something else that’s missing from the equation? How do you get the CFO to care? To pay attention to foundation? To FUND the DATA piece.

Want to improve Days Sales Outstanding Mr. CFO? Optimize Order to Cash? Then let’s look at the data integrity of the invoices: Bill to / Ship to; Short Text Description; Pricing discounts, Tax ID’s, etc. Give a customer a reason, and they’ll delay the pay. Fix the problem at root cause and you just got your CFO to smile a lot more.

IMHO, if your CFO is looking at driving strategic objectives, cutting cost, gaining transparency and business insight, etc…– then without a solid data foundation your ability to execute is limited and certainly corruptible. Yes, Garbage in, Garbage out is easy enough to get. But to the CFO the data must be the linchpin that they care about the most. The foundation. Invest in data as part of every project – not an afterthought…and that CFO will get the business to rush to help.

If your CFO or other business lead doesn’t get it - well you know who to call ;).

John Ferraioli, SVP Data Lifecycle Management, Utopia, Inc.

Data Consultant Etiquette - What’s Your Attitude?

2011-02-01T07:01:00.003-06:00

In my experience I’ve found that Customer Relationship Management (CRM) goes beyond data. It is a key strategy for improving and building your business. In this post I’m taking a different approach to CRM. I’m looking it at from the perspective of you, the data consultant, being a product of your company.

As you know the world is changing and businesses are becoming more service based. On Quagnitia’s Web site they state, “customer relationship management (CRM) is not just the application of technology, but is a strategy to learn more about customers' needs and behaviors in order to develop stronger relationships with them. As such, it is more of a business philosophy than a technical solution to assist in dealing with customers effectively and efficiently. ” I like this.

I believe that there are two important characteristics that a data consultant should possess when it comes to customer relationships... Attitude and Aptitude.

Let me share with you a personal experience which shows the importance of attitude as it relates to managing a customer relationship.

It was almost 5:30 p.m. on a Friday when Bill, a technical consultant, received a call from his client Ken. Bill had been working with Ken for almost a year. Ken was stuck with a problem he could not solve. The reply that Ken received from Bill very surprising as it is not a reply you would expect from a professional with many years of experience underneath his belt in the industry. Bill responded with, “it’s almost the time to go home, please shoot us an e-mail and we will look into the issue on Monday.” Ken was shocked and found himself helpless. So what does Ken do the same thing any of us would do, of course. He contacted another vendor Sam who he had done business with in the past as well. Sam responded to the problem immediately and assured Ken that they would start working on the issue now and would get back to him with a solution or approach to the problem. Ken was happy and found himself some relief.

In the example above you can clearly see the difference between the two vendors, how one loses the business and the other gains it. The above incident describes the two different approaches in the ATTITUDE of the consultants. At the end of the day the satisfaction of the customer is important, no matter how it has to be achieved.

Shwetesh Deepak, Data Migration Consultant, Utopia, Inc.

Building Blocks for the Data Business Case

2011-01-25T06:35:00.006-06:00

YOU NEED A FOUNDATION

Anything of significant capital investment needs a plan, a blueprint, and a foundation; houses, an office building, even an IT or ERP system. You can’t build a skyscraper on a sandy hill – it needs a solid footing. So, after proper ROI analysis and funding, owners retain engineers and architects who work together to evaluate, analyze and maximize the potentials of a site, and the kind of building the site will sustain.

Some potentials include assuring the bedrock is solid enough to support the structure (scoping), and looking at proper ingress, egress and utility, that the foundation promotes optimal functioning of the building structure, the systems and people within it, as well as the environment (overall design). Finally, after design reviews and revision, you have construction (realization), testing and sign-off.

DATA IS THE FOUNDATION

From my perspective, data is the foundation of business. Companies may outlive or outgrow their facilities, and build new ones, but they never stop needing good foundations. In this respect, relating to data, some businesses run two or more flavors or versions of foundations, virtual copies, often modified, locally optimized or tuned for specific business functions or silos, but you really have one, a “single version of the truth”, inherent in your business DNA – the building blocks of business processes.

Companies need to know if their foundation of data is bedrock or sand. They need to understand if it requires shoring up because, in the figurative sense if the foundation is aged, the building structure is stressed, the mortar is cracking, or if the weather is getting worse, and let’s say there is a threat of seismic activity (analogy for a business shock) – a high risk event could shake the building (business) to the ground. Preventative maintenance and proper attention can help avoid this.

ENTERPRISE DATA LIFECYCLE MANAGEMENT

To help companies firm up their data footing, fortify it, elevate its performance potential, and to keep it in high performance, a sophisticated end-to-end, cradle-to-grave strategies around this concept has been developed called Enterprise Data Lifecycle Management (EDLM). So whether you are upgrading an existing ERP, implementing a new one and migrating to it, adding Business Intelligence or other business process applications, or consolidating or divesting a business, EDLM is one solution you don’t want to over look.

David Kuketz, Senior Consultant, Utopia, Inc.

Check the Data! Has Your Yogurt Expired?

2011-01-20T06:50:00.002-06:00

Some say breakfast is the most important meal of the day. So, fresh fruits and other essentials are critical to making a great start to your day. This morning I opened my yogurt provided by the hotel only to see something that did not look right. I could smell something but I did not taste it. My stomach turned a bit and then I noticed the yogurt had passed its expiration date by over seven days.

Now, you may say no big deal and most of you would be right. However, maybe another guest would have eaten this yogurt and gotten sick.

The lawsuit or embarrassment on behalf of the hotel chain would not be worth the 25 cents it costs to simply toss the yogurt. This is a good example of a situation where either a new business process needs to be established or the current one has failed. Ideally if the hotel had purchased SAP BusinessObjects maybe, this type of incident could have been avoided. Their inventory would have been maintained and tracked with better oversight. In conclusion, next time start your day off right and work with SAP’s Data Insight. It’s a great way to fuel your data and keep it healthy every day.

Angelo Kenel, Data Migration Consultant, Utopia, Inc.

Data Woe’s – I’m Ashamed…

2011-01-13T06:50:00.002-06:00

So here we are a data management company working with some of the most interesting challenges all year long. I believe we completed 35 to 40 data migrations to ERP’s, another five to 10 CRM implementations, some supply chain management migrations, data governance, and strategy work and thought we were doing quite nicely.

We were brash enough to start blogging on the merits of data, data quality, master data management, data migration, data governance, and about every variant in this mission critical space. We pointed out examples, sited case studies and shared the embarrassing faux pas that occur in our client’s shops all around the globe.

Then….it happened. And it happened in a big ugly way… It was the Tuesday after the New Year and while sharing some new reporting dashboards with our CEO – I was quick to show him how the bookings and revenues turned out – the final tally’s – the high five moment for anyone running sales in a business. The new dashboard was outstanding of course. The new segmentation was spot on, the view by quarter, by opportunity, by channel was the long sought after vision of where we were – where we are going….and then….then it happened.

As our CEO is known to do, he asked a few questions, and then he dug into the data in the report. No issues there – I knew the data was solid, heck – I had been using it to forecast the business for the past two years. Bulletproof! Oy Vey – with that my confidence was shattered – he dug into the one account that had a duplicate. Two word company name, 4 transactions, we couldn’t find one of the transactions…?? Hmmm, mystery. Well, somehow, someone, somewhere (sales guy whistles and looks at his shoes) a duplicate was created and the name of the account was a one word company – no space between the two names.

Okay – not a crushing blow – but I can tell you it was within the hour we began to have our own consulting and delivery team begin the process of profiling, providing some analysis and putting in the process for taking our own medicine. We’ll have another round of self learned lessons as we are about to experience our own ERP project in house. Yep – it’s time – can’t wait to see what we find. :)

Doug Jones, Global Vice President of Sales, Utopia, Inc.

Finding Your Data’s “Mr. Clean” Solution

2011-01-04T06:47:00.003-06:00

I know it's not right - but what can I do about it? Only the business can clean the data, isn't that true?

So you did all the right things, profiled the data, found some issues and now the only way to fix it is to give it to the business to update – that is what a lot of people think, but it is not always the case.

There are tools like SAP BusinessObjects Data Services that have been developed to assist people in cleaning up their data. These tools have standard functions to help in cleaning and standardizing data. They know, for instance, that Betsy and Elizabeth are often used interchangeably as first names – and normally for females (all though there might be a few exceptions – remember that famous song – by Johnny Cash - A Boy Named Sue). These tools can use reference databases like the US Postal Services address data to check and verify addresses etc. No longer does it have to be a big XLS spreadsheet that someone has to go through line by line.

It has been found over many projects that if the issues that are identified during profiling are discussed with the business it is often possible to deal with up to 85% of the issues programmatically (sometimes it can even be higher – like in the high 90s). The remaining ones then require that human touch to decide the best way to deal with them. In anybody’s language, 15% is a lot less than 100% :0)

So when you are faced with the situation of having to clean or transform a lot of data – there are alternatives to just throwing a lot of people at it – consider the use of tools like SAP BusinessObjects to make your life a bit easier.

Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.

Why doesn't my system work? Your data is rubbish!

2010-12-28T06:48:00.003-06:00

How many times have you heard the users complain that the system doesn’t work? Then when you investigate you find that a key piece of data is missing and the whole process failed as a result. This is a common occurrence; many times the system gets blamed for what is a data problem.

How do you fix this? The answer is multi-faceted – with four major steps.

Make sure there is an owner for the data, someone who takes responsibility for the completeness, consistency, conformity and accuracy of the data.
Make sure it is clear what the standards and business rules are that the data needs to conform too. Ensure the people who are entering the data know what is required of them.
As far as practicable, get the system to enforce these standards and rules at time of data entry.
Set up the appropriate monitoring, reporting to identify problems with the data and then take the actions required to fix the problem and prevent the problems from occurring again.

Sounds easy, doesn’t it? However, as people know it is far from easy and requires people who are dedicated to getting the data right. Part of the answer is to ensure DATA has its own stream in the ERP implementation and that it is handled by people who are experts in data. They need to understand data migration, data quality and how the data is going to get used in the new ERP system.

Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.

Valuing Data as an Asset

2010-12-21T07:01:00.003-06:00

If Data Is an Asset then Why Isn’t It On The Balance Sheet?

There are many kinds of “off-balance-sheet” assets, proprietary and intangible items that have value and help drive operational efficiencies in our businesses; higher revenue, lower COGS, lower costs of running the business, and drive integrity of reporting and decision support systems.

It’s difficult to put a dollar figure on the value of formulas (drugs, chemicals, and so on), recipes (foods, perfumes, and so forth), processes (microprocessor design), or data. We have to define what data is too.

You can measure the costs of research and development to build it and you can try to value the asset based on what others would pay for it, or based on the value of your business without it compared to the value with it.

Note the operative words “costs of research and development” – some companies value research and development as their source of innovation while others try to reduce costs, especially during a recession, cutting research and development and IT budgets. Sharing the costs via centers of excellence or shared services helps – as data permeates the IT landscape and fuels all business processes and functions.

One method of valuing data as an asset is to subtract the values of the company’s tangibles from the market capitalization of the company (if publicly traded). The demand for the stock would represent a multiple over the book value of the assets – that value is created by the operation of data within the eco-system of that company.

An internet company’s stock valuation would have a lot to do with (a) customer lists, (b) vendor lists, (c) volumes of transactions per year, the ASP (Ave Sales Price), and the profit of each transaction, (d) new products in the research and development pipeline, (e) other factors and (f) the product / services catalog. All of these things are data. Think of iTunes – nothing tangible – all data driven – worth $BB’s.

Brick-n-mortar companies have hard assets. Click-n-order companies have soft assets. Click-n-order tend to have higher multiples, because they create more value for a given level of investment. They can also change business models and change course when markets shift, whereas companies heavily invested in equipment, facilities and buildings cannot, or not as easily.

Banks buy other banks for the value of the “assets” (i.e., deposits on account), the customer base and the retail locations / outlets. Lots of data here too – almost entirely run on data.

So, although data doesn’t show up on the balance sheet, you can bet your assets that your business would not run well without it.

David Kuketz, Senior Consultant, Utopia, Inc.

Your Data Resume…Five Famous Mistakes

2010-12-16T06:53:00.003-06:00

As an executive on our human resources team I see a number of resumes coming through our doors every day. There are three distinctive pieces of data that I find are essential “Ps” for my organization. Those three “Ps” are - product, packaging, and presentation. The raw product is the resume which needs thorough attention (e.g. database) and the packaging (e.g. tools, fields, and content) is the formatting done to make it ready to sell (e.g. business users/end users), which I do on a regular basis (data evangelist). The presentation is selling the product to the sales and solutions folks (BI, reporting, building the business case). So, the work I do is a key component to generating major revenue (e.g. optimizing spare parts, spend analysis, etc.) for my organization (information used to make decisions).

Thus, in my procession of work, I see hundreds of resumes, and I often see the mistakes repeated over and over again. Sometimes I review resume propensities that are not essentially mistakes, yet those resumes could have been improved, cleaner, and more acceptable if they just paid attention to some of the small details. The following are the most critical and common mistakes I have come across…

Laid out cleanly utilizing a bullet style for ease of reading and deterring the fears of adhering to the one-page resume rule
Not following the “resume ingredients rule”
Over use of unnecessary use of articles
Using concrete and strong verbs to describe your jobs, and mixing noun and verb phrases
Lack of focus on describing past job activities that highlight the skills you’d apply to your new position

I hope this piece of information gives you a new perspective on how you view data. Keep it simple and keep it clean. What does your company’s data resume say about where it’s been, where it’s going, and what it wants to be?

Pratiti De, HR Executive, Utopia, Inc.

Data Issues: Why put them off another day?

2010-12-14T06:46:00.007-06:00

Let’s get it right…right NOW. We can't afford problems later. You can fix your data problem…right now.

Leaving data issues outstanding during a data migration is not a good idea. It is very rare that anyone will have the time to fix problems after the new system goes live. People have enough to do learning a new system and sometimes completely new processes to also be trying to clean up poor data.

Many times when the data conversion stream is not given the attention it deserves during an ERP implementation the data is just forced through at the last minute and only just enough effort is put into ensure it loads but not necessarily ensure it is business ready.

One of the issues with this is that with modern ERPs (like SAP) once you get poor data in the system and start using it, it is very difficult to change it. For instance if you load duplicate vendors and then raise POs for them it is difficult (though not impossible :)) to remove the duplicate vendors once POs have been created. Similarly if the address on the vendor master is incorrect then even after you update the vendor master with the correct address any existing POs need to have the address updated individually – which could be a lot of work. Better to load the right data upfront than try to clean it up afterwards.

So how can we do that? From my experience the most effective way is to ensure there is a team dedicated to ensuring business ready data in prepared for loading during the ERP implementation. This requires the right combination of people, process and technology. The people need to be data experts, with knowledge of ERP systems and the data they use, the process needs to be specific for data migration and conversion and the technology needs to be geared towards data ETL and also improving data quality. It is of little use to simply move data from one place to another and not improve its quality. The users expect the new system to be better than the old (hopefully :)) and that includes the data that they see in the new system.

Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.

Now even my Mother-in-Law Understands Data Migration!!

2010-12-09T06:48:00.004-06:00

How many times have you tried to explain what data migration is? From a laymen’s perspective it poses a bit of a challenge. When I joined Utopia after completing my tenure in the army, the biggest challenge I faced was explaining to my relatives what my company did and how I fitted in the entire gamut of activities. Imagine my plight trying to explain what data migration is and how we do it when I myself was struggling to get friendly with the terms as BODI, BOBJ, FICO, AIO, ETL, EDLM, EAM……the list seemed endless. I suddenly seemed to be taken back in time when I had faced a similar situation trying to explain them what I did as an Ammunition Officer in the Army.

Meanwhile my mother-in-law came to visit me to make me “comfortable and help me adjust” to my new environment. Even after a number of futile efforts I could not make her understand why and how we transform data from a legacy system to an ERP. All I got at the end of each session was quizzical looks and a big glass of milk declaring I was overworked and it was the best decision taken for her to come and take care of me!

One day on returning back from office I saw her watching my marriage video AGAIN (she never seems to get bored watching it). She sighed and said how she wished she could see her marriage video and revive those old memories and show me how she looked as a young bride. That’s it! Eureka!!! I knew what I had to do. I took that old big VHS cassette to the local video store and asked him to convert it into a DVD. Beautifully wrapping it in shinny paper I handed her the DVD, but not before I had explained to her that the VHS was the legacy system, the DVD was the ERP and getting it transformed was what we did. She smiled and hugged me and the whole night passed away watching the DVD. I kept wondering whether she understood what I had tried to convey!

Next day while getting ready for work, I heard her talking to her sister on the phone and quoting the VHS-DVD story and explaining to her like a pro what my company did. It seemed as though my mother-in-law just joined the Utopia fan club!!

Rashmi Anand, Training Manager, Utopia, Inc.

Data…I Scream, You Scream, We All Scream…..DATA!!

2010-12-07T07:11:00.005-06:00

As data migration experts, we cannot stress more on the fact that data is an important aspect of any ERP implementation. In the fast moving technology world, businesses today are continuously deciding to opt or upgrade to newer ERP systems / solutions. In such moves, data migration becomes a necessity as well as a core activity to ensure businesses continue to operate successfully in the new system as well.

Keep in mind, a data migration project does not originate as a standalone independent thought for any business as such. The requirement of a data migration project is a result of choosing to implement a new software application or an ERP system to meet the requirements of the business and industry standards. And by way of upgrading to a newer, sophisticated ERP system, ultimately, the aim is also to move towards executing the business processes in an improved and efficient manner.

Yes, any ERP implementation will focus on the business gap-analysis leading to a blueprint design, processes and procedures to be implemented in the new system. But the business can only run if the data exists to run the business processes. There comes in the need of the “data”. Now, obviously, the data cannot just be “moved” from one spot to the other. There has to be a detailed data migration strategy that scopes out the conversion specifications for various business objects including the data mappings. Most critical part of the scoping being the identification of various business rules for data extraction and data enrichment; as well the data testing and validation approach. If any of these elements are missing in the planning and implementing of the data migration strategy, the data may get “converted” over to the new system, but will it be “usable” is the key consideration that will reflect on the success of the data migration or the ERP implementation project on the whole.

Therefore, it is no surprise that the data migration, data management folks cannot scream enough of “data…data…data” - right from the start of the project through the project Go-live date!!

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

How does cleansing data affect us in everyday life?

2010-12-02T06:49:00.003-06:00

It’s getting to be that time of year – everyone is rushing around – the hustle and bustle of the holiday season is just beginning. Sending Christmas cards is a great way to stay in touch with friends and family…the biggest challenge is making sure you have all of the correct and updated addresses? Who moved during the year? Who got married, changed their last name? Who had babies? With the increasing price of postage, it’s a good idea to send them to the correct address the first time. It’s embarrassing sending the same card to the same person twice and no one likes to have their cards returned to sender.

You have all of the extracted data from last year’s contact list and can put it in a format that can be cleansed, validated, and ensure that there aren’t any duplicates. Then add all of your new addresses and contacts that you’ve received throughout the year. This process can be time consuming, so make your card sending easier by keeping your data cleansed and updated throughout the year…Get it clean and keep it clean….Isn’t that what we want to do with all our data! :)

Happy Cleansing and Sending ~ Happy Holidays!!

Sondra Ruhman, EIM Client Partner - Southwest

Don't Rush at the End to Load Data - Plan Ahead!

2010-11-30T06:51:00.003-06:00

One of the things we see all too often is that the data stream of a project is left to the last moment. This causes all sorts of problems – when people are busy and stressed trying to do system testing they find they have no REAL data to test with. They get a few records of made up data that bears no resemblance to the actual data users will see in the production system – and means nothing to them in the context of their legacy system.

Is it any wonder then that the users have trouble validating that the system works the way that it should, they can’t tell because the data that was used didn’t mean anything to them. It is difficult to tell if it is right or wrong.

The best way to overcome this problem is to plan the data migration and cleansing tasks early, align this with the ERP implementation plan and schedule the first Trial Data Conversion (TDC) to happen just before the first round of system testing. This way the users will have real data to use for testing and can focus on the functionality they are getting from their brand new system rather than complaining about the bad data they saw in the system that didn’t mean anything to them.

Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.

Data: Practice Makes Perfect!

2010-11-23T06:43:00.003-06:00

I show horses and in the horse show world you know that practice makes perfect. So why don’t more company’s train their staff to practice safe and good data habits. During show season I practice my horse just about every day of the week in preparation for shows on the weekend. It literally becomes a habit and I do get withdrawals when I don’t ride.

Throughout the horse show season I will practice on my own and practice with a trainer. My trainer (data guru) will instruct me on how to optimize the performance of my horse (your data). If she’s dropping her shoulder in a corner I know to draw back and hold my inside rein and bump while my outside rein is also drawn back and held wide. In the world of data simplistically looking at it you know that when a shipment of new ball bearings arrive your trainer has properly shown you how to enter it into the system by following a defined pattern.

Another thought that comes to mind is sometimes you’re the one who is always riding your horse (entering the data). But there are moments when your horse needs a tune up. A refresher course so to speak. That’s when she goes to the trainers to get reminded hey you’re slacking here you need to use your tush some more and drive deeper in your hock at the lope. So those muscles need to be built up. I would say this is equivalent to folks in the company every once in awhile maybe every quarter gathering together and being reminded of the rules and guidelines put into place for governing your data.

By having routine training sessions with a trainer (data guru manager) it instills what you’re doing right and what you’re doing wrong with your data. Routine training sessions will only aid in making positive data habits and eliminating the old. Practice safe and good data habits every day so when it is show time for your reporting you’ll knock their socks off and bring home a blue ribbon! Happy trails!

JoDee Hale-Schmid, Global Marketing Manager, Utopia, Inc.

Upgrade to High Def Data, and See the Difference!

2010-11-18T07:27:00.003-06:00

Isn't it funny how you get so used to something that's negative or ineffective that you simply don't notice anymore how annoying or useless it is? Unfortunately for most of us, this applies to the data at our companies that we try to use every day. Like it or not, data is at the cornerstone of everything we do. Data is the engine that drives our business success. We use data to help us justify our jobs, track metrics, communicate with our customers, vendors, and partners, raise or lower prices, and even to choose what we want to communicate, and when we should. (Yes, we looked at our data to decide when to post to this blog!)

Think about your next performance review – you want some decent metrics to show your bosses what a phenomenal job you've been doing, but can you really rely on the information at your fingertips to prove how fabulous you are?

Every single decision we make in business comes down to data we have recorded, gathered, assessed, and reported on. What are you supposed to do if the information at your fingertips is misleading? Would you even know if it's incorrect, incomplete or inaccurate? How can you possibly make sensible decisions if the data you base them on isn't even correct?

Sometimes you just don't know how great something can be until someone makes a dramatic change for you. To me, the difference between poor data and great data, is a bit like the difference between a black and white TV, versus a high-def HD color flat screen. Don’t you want to have high-def data? Don’t you want to use your system and go “Yeah, this is amazing”, or “That is exactly what I was looking for.” Luckily for you, a data utopia exists. It is a place where the upgrading and improving of your data quality, usability, and reliability happens. My suggestion to you? Don't settle for regular data any longer, upgrade to HD!

So, maybe you think that this doesn't apply to you right? Unfortunately getting used to poor data is something that just becomes a way of life for most of us. The same probably applies to you. Think of how many extra 'tweaks' you might need to make to a report before it is finished, or how many different search types you have to run to pull information from your system that should come up after just one search!

Notice any inconsistencies in the information you get? I bet you do. If you're done with workarounds and spending five hours on a report that should only need 30 minutes of your time, you need to talk to our solutions team. Imagine how much time you would free up to become even more brilliant at your job, and get some additional promotion inspiring metrics of your own for your next performance review?

The cool thing is that there has been a series of industry leading solutions developed to address enterprise data lifecycle management (EDLM), specifically geared to removing the common frustrations associated with poor data quality, finally enabling you to fully leverage corporate data to your advantage. So, if you're ready to upgrade your data to HD, reach out to members of your team to talk about how much wasted time each employee spends massaging and manipulating your corporate data, become your corporation’s data advocate. I know a pretty good data management company that can assist you with a plan to upgrade your poor data to HD. You won't regret taking on this type of initiative, actually... I would suggest you add it as a goal. That will definitely to that list of fabulous things you've done to deserve your next promotion!

Charlotte Kerver, Human Resources, Utopia, Inc.

Yes Virginia, There are Data Rules

2010-11-16T07:05:00.004-06:00

Data rules rule! You cannot manage what you cannot measure. You cannot control what you cannot manage. If you have no framework for measurement (i.e., rules) you are out of control. Business fails. Pure anarchy.

Data rules, business rules … like rules of physics … they already exist, they’re everywhere – you just have to learn them, discover them. We don’t invent them. In the real world, we cannot escape the physical rules that operate – as we process through our daily lives. Some rules of physics are harder to understand than others. F=MA much easier than E=MC**2. The apple falls from a tree. Photons become energy at the speed of light and act both like particles and waves – who knew?

Newton didn’t know. What happens to the apple, really? Newton had it only partly right – but in his day, they could only measure some of gravity’s effects. Newton’s original formula is replaced by Einstein’s General Theory of Relativity (and, even this today is thought to have a few holes in it).

Today our business applications are much more sophisticated than 30 years ago. We can measure more now. We see more now. We sense more. Companies are much more highly interconnected with a lot more CPU cycles than before … we can match supply and demand much more closely and run companies at or near the efficient frontier (as fast as you can go without the wheels falling off the cart).

If a company is operating on the “efficient frontier” (more on this idea later in another blog post), then it has optimized constituent relations, management approach, business processes, applications, meta data and raw data, minimized risks and maximized returns.

Beyond data rules, even business rules are governed by a higher order paradigm, the rules of the economy, regulations, social pressures, etc., things external – and they are subject to change (war, elections, recessions, etc.) When those things change, the business rules change (adapt or die), the meta data changes, and the raw data requires updating and/or enrichment – in order for a company to remain on the “efficient frontier” – to keep risk levels from rising, to keep returns maximized at the optimal potential given a certain degree of risk tolerance of the stakeholders.

Companies near enough to the efficient frontier may survive recessions, while the rest fade away. Enron, Worldcom, Lehman Brothers all broke their own rules, went beyond the efficient frontier, and then failed. Apple (funny), Amazon and Netflix make their own rules, create distortions, dis-intermediate and disrupt others in their industries, and redefine the way rules work in their respective markets. They create incredible value and recurring revenue streams.

Some data have rules derived from the ‘real world’ of physics while simultaneously having other rules defined by the business processes pertaining to that particular businesses eco-system (price, availability, criticality, where and how used, …)

Here’s where the “no-data-rules” guy’s theory falls apart … there are data rules that apply to things and they have valid, legal and range values because the thing (noun-modifier pair like bearing, round) has only so many properties. Rules exist to govern or guide that people will not enter, for example, a negative value or 1,000 meters for diameter. Simple. But there’s more.

That thing just happens to exist in an eco-system (“universe”) and it inherits particular unique attributes endowed upon it by that system (where used, how used, when used, last replaced, due for replacement, safety stock, stock room, plant location, QoH, assembly, BOM, etc.) These attributes don’t belong just to the thing inherently, but to the thing when it is matched with its owner or user, conditionally. These values change over time, as the company navigates the economy and executes on new product introduction, production, new sales campaigns, changes in supply and demand, etc.

Same item in a different business may have same physical rules (and attributes, in fact exactly the same, form, fit and function) yet the other rules (dynamic and configuration attributes) depend conditionally upon its eco-system, which could be vastly different than the other.

Rules differ across types of data; vendor, customer, material, financial … there really are data rules, you just have to be aware of them and leverage them to YOUR competitive advantage. If you are better than your contemporaries at data and business rules, and business process optimization, you will probably be the 800 lb. gorilla in the room.

You can’t have a negative value for stock levels and you don’t want three extra zeros inadvertently added to a purchase order, or a check written to an inactive vendor, even if that’s what the business process “rule” allows for – because some smart dude might design a business process that is wrong, or there was no data rule that said “old vendors need to be archived”! Gotta have a safety net, a red flag, checks and balances.

This paradox between data and business rules present a challenge, and an opportunity. While the physical data is relatively easy to define and set rules for, the other types are less easily understood and are bound by the business rules and higher order rules, which must be analyzed continuously.

When companies change their eco-systems, consolidate via M&A, improve their processes, upgrade systems – it’s the not-so-easy data that has the highest profit potential and value.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

New ERP Hated by All…Because No One Cleaned the Data

2010-11-11T06:30:00.002-06:00

Users don't like the new system - why? Because they see the same information they had in the old system – but in a new format.

How many companies have spent millions of dollars on a new ERP system, then added a few more for a BI system, then added Balanced Score Cards and very pretty dashboards, then the CEO still gets reports from an Excel spreadsheet. Ever wonder why? There are many reasons; however one we find common is that when the new shiny ERP system was implemented no one cleaned the data from the old system(s) BEFORE it was loaded into the new system.

As a result all the reports that had wrong information before – still have wrong information – but they do look really pretty. :)

Why does this happen? Often time it is because the data migration/conversion stream of the ERP project did not receive the attention that it deserved. It was left to the business – you know the old story – here is the file format we need – provide the data in this format and we’ll load it for you – but the responsibility for the quality of the data is with you.

It doesn’t have to be this way. There are tools and processes available that help organizations structure the data migration stream of an ERP implementation project to minimize the risk of poor data getting into the new ERP system. These methodologies have been developed based on the experience of professionals that have been doing data migrations for many years and have used that to ensure the methodology is practical and proven to result in business ready data loaded into the new ERP system.

Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.

Data Garbage In – Data Garbage Out

2010-11-09T06:39:00.002-06:00

“Garbage In – Garbage Out!” I know we have all heard and used this term countless number of times! But are we really able to put the true meaning and implication of the phrase across to various clients/customers that we work with on various ERP implementation projects?

More commonly termed as GIGO, yes, “garbage in – garbage out” is what the result will equate to, if the quality of data is not good. Quite often, on projects we start off on a good note by executing detailed data profiling, generating reports on data quality for the customers. But during the course of a project, the data cleansing activities are not prioritized enough. While, in the hustle bustle of meeting the project timelines and milestones, we manage to execute and implement the data mapping of various business objects to its entirety. However, are we able to deliver 100% accuracy on the credibility and usability of the converted data? A question that we all silently know the answer to!

As data management experts, we should only continue to recommend the best practices for data quality, including data standardization and perhaps even recommending to the customer on planning and budgeting for a pool of resources that are dedicated towards the continuous data cleansing efforts through the duration of an ERP implementation project. This can only add value to the project and business during and after.

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

“Filler-up” with Super Enterprise Data

2010-11-04T06:43:00.002-05:00

Crude oil and enterprise data share one common fact in that they are both intrinsically worthless. Crude oil on its own is a just a barrel of nasty gunk that really has no value or purpose unless it gets refined. It is the refining process that unlocks significant value as it yields many types of fuel and also several base components used in the chemical industry. It’s what you can do with crude oil that creates such value and power in today’s economy. Enterprise data shares this similar property to crude oil as the real value of data is created when it is processed with business applications to support operations, decision making and reporting within an organization. The organizations that can best process their enterprise data effectively with innovative applications will create value for their stakeholders and to the economy as a whole.

The similarity between crude oil and enterprise data I believe comes to an end after this initial illustration. Crude oil needs to be constantly explored beneath the subsurface of the earth at great economic and environmental costs. It is indeed a limited commodity which is not a sustainable fuel source. Enterprise data however, is limitless and can be collected easily in a variety of ways at costs that are minuscule in comparison to the finding costs of crude oil. Maybe we should consider our enterprise data as a sustainable energy commodity for our companies! Now if I can only get my car to run on this stuff…

Dave Lepa, Director Canadian Sales, Utopia, Inc

Data Business Process: Please abbreviate… “North Saint John’s Street”

2010-11-02T06:34:00.002-05:00

It’s a funny thing. A data blogger made a bold statement that there are no data rules, rather he made the argument that data exists in an enterprise to support the “business functions”** of that enterprise. Data rules, then were not required – only business rules were relevant.

It may sound like we are contradicting ourselves when we say we do, in fact, subscribe to his thinking regarding business process (or function) execution. But in our opinion, data without rules is like a house without a solid foundation. And in this dimension, apparently we cannot agree.

As a data centric consultancy, part of our methodology for a data migration, enterprise data management service, or a data health assessment™ is to document, analyze and understand the business rules. We actually scorecard whether or not the business rules are enforced and governed. It’s one thing to have a rule, quite another to enforce it in a consistent manner…anyone with a dog or children will appreciate this. So absolutely we require and need business rules. But at the foundation, data rules support the structure and the very definition of how data standards are enforced.

The approach should be to understand and enforce the following canons of enterprise data management:

Data Architecture
Data Standards
Data Governance
Data Quality
Management of Change

So all in, I believe the statement is wrong or semantically challenged. Data rules outline and can be used to enforce standards and the related metrics allow a data governance organization to manage what they measure. Ultimately to scorecard themselves against acceptable thresholds or tolerances tied to data integrity. One man’s data rules are another’s data standards. Semantics aside, data rules are a necessity.

John Ferraioli, SVP Data Lifecycle Management, Utopia, Inc.

Data Ghosts and Gremlins in the Machine

2010-10-28T06:44:00.003-05:00

Just in time for Halloween when, you thought it was safe to let your guard down . . . terror awaits. Not a conventional scary movie sort of horror, but something infinitely much worse.

You know the drill: at home, late a night, strangely awake and discomfited with your thoughts. The smallest noises are magnified and assume a malevolent air. Is that a branch banging against the gutter? It sounds sinister, like a peg-legged, man ascending the staircase, one slow step at a time – and your bedroom is the first door on the landing.

Transference, that’s the technique you were taught in analysis. Think of something else that could account for the noise, something benign and innocent, not at all evil. Transference, concentrate on something pleasant, mull it over, contemplate it, savor it in your mind – that brings back good feelings, buoys confidence, and banishes bad thoughts.

Work has been particularly satisfying lately. You’re moving to new systems and everything is on time so far. There’ll be improved performance and greater satisfaction from your employees. Hero is too strong a word, but you’ll definitely be admired, this is going to be a success, colleagues will find it so much easier to do their work, a few kudos will have to fall your way. And project spend is below target, the brass have got to be pleased. Everything is looking good.

But that knocking keeps intruding on your thoughts: is it getting closer? No matter how hard you try to banish it, the noise keeps building and seems to be getting louder, more present, more here.

Have you thought of everything? What could go wrong, why ever think anything could go wrong. It’s opportunity that’s knocking, not some gremlin. Concentrate, good . . .

Have you thought of everything . . . yes of course. Knock, knock, knock.

Something trying to get in. Incessant knocking.

Data’s coming from multiple sources. Is it data that’s knocking? Why would data knock? Can data get in? Does it know how? Will the peg-legged man let it in?

Have I thought of everything? How are data from all our siloed applications going to mesh in the new system?

Knock, knock, knock.

Will they get in as is? Will they work together?

Knock, knock, knock.

Or will they be demons and ghosts in the new machine, frightening the system into a catatonic state that says nothing, does nothing, reports nothing, analyzes nothing, delivers nothing, frustrates my users, my God, my kudos, my budget, my word.

Trick or treat.
Give the system something good to eat.
Trick or treat.
If data’s bad, we’re down to defeat
Trick or treat
Gotta get the data clean, that’s the feat
Trick or treat
Our data’s clean, we can’t be beat!

Knock, knock, knock

Is that opportunity or disaster?

Tony Stypinski, Client Partner-West, Utopia, Inc.

Know You Have a Data Problem Before It Bites You

2010-10-26T06:55:00.002-05:00

How good is your data? How well does it meet the requirements of your new ERP system? How much of the cleansing/migration effort can be automated and how much is the business going to have to do manually? Unless you know the answer to these questions it is really hard to plan a data migration project well.

This is why the first phase of a data migration methodology is to profile the data. The tools we prefer to use to do this are normally SAP® Business Objects™ Data Insight and Data Services.

These tools, along with a basic knowledge of the requirements allow the development of a snapshot of the data in the legacy system which can provide “insight” into how well it will fit into the new system, what issues might be coming up and also provide an idea on whether the problems can be fixed programmatically or if they require that human touch.

All too often, at the last minute, the business is told that there is data missing and the new system won’t work without it. They then run around hectically trying to get good accurate data or just through their hands in the air and provide ‘default’ data just to get something into the system. Neither option is best. By doing profiling as the first phase of the project many of these sorts of situations can be identified early and avoided. The business can get sufficient time to be able to collect the required data and to ensure it meets the intended business needs in the new system.

Peter Aynsley-Hartwell, VP of Solutions Consulting, Utopia, Inc.

Data Errors Disrupt Corporate Goals & Business

2010-10-19T06:36:00.001-05:00

And there it was this week in the news:

“Data issues have caused Bank of America Corporation (NYSE:BAC) to freeze foreclosures in 50 states. Worries have developed which suggest that faulty data may be being used to decide the fate of houses, and may be causing some people to be evicted without merit.”

Yet another example of corporate goals and business being disrupted by data errors. Surely actuaries are already calculating the millions of dollars each hour this is causing Bank of America – why? Data.

Or another unfortunate, yet not too rare headline from a month ago:

“Bret Schundler, who was fired Friday as education commissioner, said he learned Monday that he personally made the budget data error that helped the state miss out on a $400 million federal grant for school reform.”

Surely these could be simple data errors, business process errors, pulling data from the wrong tables and not realizing the transformations rules already applied to a column, a row, a field, etc…

These simple illustrations are how data stewards, data evangelists, and data architects need to keep pushing from within their corporate dynamics to keep data management on the front burner. No – it’s not the insurance salesmen in your house with pictures of burned homes and fatalities approach – but rather a thoughtful reminder that data governance, data management do in fact have real world implications and need to be adhered to in all of our corporate IT projects and held as a basic standard for initiatives.

So what examples or headlines do you have? Any specific examples you can share? Let’s make this a fun list of ways data has cost businesses in some fashion or another. Who has a good one? I’m sure we can all benefit from another good example or two!

Doug Jones, VP, Utopia, Inc.

Data for Mail Efficiency and Reduced Pollution

2010-10-14T06:37:00.004-05:00

The USPS has over 220,000 vehicles traveling over 1.2B miles annually (non-contract vehicles). These vehicles make a lot of starts and stops as they traverse our zip codes to deliver to millions of commercial and residential locations nationwide. Each start and stop causes wear and tear on brakes, tires, transmissions, engines, and the people driving the vehicles.

The USPS burns a lot of fuel, emits carbon into the atmosphere, and goes thru a lot of spare parts to keep all those vehicles running and safe for all. The USPS retail storefronts and the sorting and scanning machines require a lot of maintenance and spare parts too. Vast amounts of data, great technology and people are required to keep this hefty eco-system operating at full capacity and healthy.

According to the USPS Sustainability Report revenue is down, profit is down, head count is down, volume of mail is up and the number of delivery points is up, that is, the route map is growing.

Total miles traveled (including contract miles) are 6.4B. The USPS outsources more than 80% of its miles by the way!

Talk about trying to do more with less! What if there was a way to cut that mileage down by half or a third? That would save fuel expense, spare parts and time, not to mention reduction of emissions.

One simple idea is to rearrange the location of mailboxes on every residential street in America. Check out this post on Education 4 Well-Being.

In most sub-urban residential areas mailboxes are on both sides of the street. This means the postal vehicle must drive up and then back down each street, and it must make a start and stop at each and every residence. It takes time for the driver to operate the vehicle and then drop the mail.

If the mailboxes were all on one side, and if each cluster of say four homes (two adjacent, and the two across the street) had a quad mailbox cluster in one spot, then the postal vehicle would cut its route length in half and cut the number of starts and stops by 75%. The fuel efficiency and wear and tear impact would be significant. But how much improved? How would it affect profits? Morale? Other?

If we had visibility to the costs of all the spare parts, the contribution of mileage due to the present configuration of the mailboxes vs. the proposed configuration, and other factors, it would be possible to create a visualization or dashboard, not only with instantaneous information on what’s happening right now, but full of “what if” information to analyze and evaluate different optimization schemes.

That’s the power of data working with ideas, business applications and a little creativity, to help the USPS survive their present funding crunch, keep our postal rates down, and save the earth too.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Data, Always the One Being Ignored

2010-10-12T06:34:00.004-05:00

In Dubai it seems that dwellings used to come above ground and stay above ground with only a one year maintenance contract. Following such it is the owner’s responsibility to find a contractor to carry on the maintenance? If the building erodes, etc…the main builder provides no warranty…is that right? Whose fault is it? Is not the builder’s reputation on the line here? Do we not say later – that the builder has bad quality material? We never say – oh the past owner never maintained the dwelling….time to change that…

Builders need to take a different stance and ensure quality dwellings and maintenance…. The same goes for IT C-level management who think that by getting the latest in ERP solutions and Business Intelligence software that their business user problems are resolved? Are they really? Are they not missing the most important variable in achieving their objective...DATA, DATA, DATA.

I see many IT managers and executives here in the Middle East spend resources - financial and human - purchasing Business Intelligence tools, upgrading their ERP systems, hiring internal developers to develop in-house applications hoping to resolve their lack of informed decision making, address business user issues, achieve cost savings, etc. but still fall short of why they started down this road. Data needs to take a front seat in order to achieve the determined objectives. According to a 2009 Goldman Sachs survey of Fortune 1000 CIOs – “data is a key asset for improved decision making and operational efficiency as six out of Top 15 priorities are driven by data while ERP is down to #34 from #11 in 2008.”

My recommendation is as follows: C-level IT executives should start taking the approach of looking at their data from a lifecycle management perspective; identify and fix issues in the business processes that create, manage and govern data throughout the lifecycle; and always look at solutions first from the perspective of aligning your organization structure, roles and responsibilities while managing enterprise data. Then and only then will IT C-level managers start seeing the objectives of their business users being met and achieve a better ROI on your IT investments.

Hani Obeid, Managing Director
South East Europe and Middle East, Utopia, Inc.

Enterprise Data and the Grain Supply Chain

2010-10-07T06:40:00.004-05:00

It’s a beautiful sunny fall day in Alberta, Canada and I am driving two hours south to visit my son Max in Lethbridge where he is attending University. Max had a birthday this week so my wife and I are making the trip down to have a birthday dinner, and also to fill Max’s refrigerator up with food so he can survive another month of studies on his own.

The drive though southern Alberta can be described as a wide open prairie of farm land and at this time of year the color of the landscape is mainly a golden yellow as farmers are busy working long hours bringing in the grain crops. High quality grain is grown and sold by Canadian farmers and distributed all over the world for food production. Massive farm equipment going through a slow orderly process in these never ending fields can be seen for miles. You can almost feel the sweat and toil that these individuals are enduring on this Saturday morning while most people are enjoying a nice leisurely day off. As I drive along the highway the dusty grain terminals in each small town we pass stand out as local monuments. The grain terminals are where the farmers bring their grain after harvest and it is cleaned, stored and shipped away in railway hoppers to the nearest port.

Grain production is a difficult expensive process when you understand the full season to produce grain starts in the spring with ploughing; seeding, fertilizing, and then the never ending prayers that take place for a growing season that will provide for a healthy yield. Of course the risk of drought or early frost can reduce the crop to a low value and all the hard work gets marginalized along with the farmer’s income.

I bought my son Max a few loaves of bread during the “fill up the fridge” shopping event later that day and I reflected for a moment remembering the farmers in the field that morning. I tried to imagine the entire supply chain process to get a loaf of bread to the consumer as I paid a couple of bucks each for these freshly made loaves of bread. I even laughed a little at how little we pay for such a product. If you looked at the entire grain to bread supply chain it consists of the farmers who grow wheat to the grain millers, transport firms, bakeries, retailers, advertising and branding agencies, and packaging businesses that are involved in transforming grain into a loaf of bread. Hard to imagine this long supply chain with so much human interaction and business risk can produce a loaf of bread for about two bucks.

Perhaps we can all take a look at our own supply chains at the companies we work at and contemplate the ways in which these can be streamlined and made more efficient. Maybe think about the farmers and the hard year over year efforts that go into their value chain processes. We can be thankful we work in an industry in which we can use our own locally grown enterprise data as our raw material to our value chains. Think about adding seasonal nurturing and ongoing governance and we will all enjoy plentiful harvests of value for years to come!

Dave Lepa, Director Canadian Sales, Utopia, Inc.

Enforcing Bad Data Quality Routines

2010-10-05T07:12:00.004-05:00

True story – the past five weeks of travel, hotels, rental cars, and flights have provided some new frustrating insights. As I have continued to try, try, and try again to sign up for a major rental car’s reward points and the long sought for – no lines, immediate access to car keys – I have been thwarted numerous times.

As I went online to sign up for the “VIP” version for this rental car company it asks for two major pieces of information, home address and billing address. Each time, with the appropriate data entered, credit card, insurance coverage options, car size selection – I hit the submit button only to be denied based on an address entered. So with some level of repeated due diligence I have carefully entered both addresses, passwords, etc…and still failing each time.

It wasn’t til this week at the Houston airport that I was greeted by a friendly counter clerk who finally told me – yup, known issue. The solve? Ah – just enter in my home address as the billing address – even thought this is 100% inaccurate. My corporate card is associated with our headquarters billing address...a short 1,200 miles from my HOME address. I just mis-entered the information intentionally and voila! It works…despite my driver’s license number and state of issue being ENTIRELY different.

So now I have access, VIP treatment and the rental car company has no idea of where I actually live. Great thinking huh? Can’t wait to see the downstream implications of presenting my ID and having to verify transactionally that it is indeed accurate, but could not be used based on some business rules from their Web site order entry issues.

This is another great example of IT and the business not being on the same page. Oh well, time to return the current car and find a gas station within 10 miles of the return site – ah, the joys of travel! :)

Doug Jones, VP, Utopia, Inc.

“No Budget for Data? Bring home the bacon!”

2010-09-30T06:57:00.005-05:00

Bacon? How does that relate to good, clean data for business? Economists fancy their terms for theories such as “free-market” when nothing’s free and “Pigovian Tax” sans pigs. But, you’re thinking new sin tax on bacon, like social tax on things like tobacco. No, it’s market externalities.

An externality is a transaction “spillover” where some economic benefit or penalty is incurred by third parties not involved in the original transaction. You’ll find examples of this in pollution, environmental accidents (think BP oil spill – “spillover”) and carbon tax (“cap and trade” or “carbon credits”).

Economist Arthur Pigou suggested a tax (or subsidy) to correct this abnormality, depending on whether it is positive or negative. The idea is to penalize indiscriminate polluters for example and reward accidental creators of benefits for others. Those free benefits themselves might be taxable too, so funds generated could be allocated toward additional market externalities – nice economic engine!

Figure 1: Externality Tree (Source)

What if we could create positive externalities like good, clean sources of energy that purify our environment as a side-effect of power production and distribution? Like energy, data can be produced and distributed, efficiently and effectively, that is good for the business eco-system. Perpetuate value.

Being a “network effect” asset, good (bad) data indirectly benefits (penalizes) third party constituents. Who owns the data? Who benefits from it? Who defines and specifies it? Who’s going to pay for it? No one wants to step up and pay for the data. “It’s not in our budget.” “That’s a cost we just can’t absorb right now.” “We’ll get by with a couple of monkeys in a closet working on the data.” Monkeys?!

"Data is a strategic discipline," says Gartner Research and Bloor Research points out, "80% of organizations will underestimate the effort by 50% and incur significant delays." Why not consider pros? Subsidize data implementing a Pigovian Tax, levied by consumption, usage, or pro-rata per revenue or benefits received at each business unit. Value generated by the tax and the data would pay the data management Center of Excellence (CoE) for their services.

Residual economic value benefits all stakeholders. A business intelligence dashboard and reporting system could easily be setup to measure, monitor and track the data streams, and the associated benefits. Consider it integral to your sustainability practice and reporting structures.

Now there is purpose, motivation and progress. Now you’ll get your budget. Now there’s meaningful, measurable ROI for the data and the business. Go Arthur Pigou!

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

100 Data Management Posts - Recap

2010-09-28T06:37:00.003-05:00

100 blogs in 100 days – WOW! What a fun, informative, educational journey we’ve had. Lessons learned? Creative thoughts, real life experiences, contributors with the talent for helping find daily events illustrative - really help us all frame the value of the space in new unique ways.

100 Thanks to JoDee Hale-Schmid our Global Mgr of Marketing for pushing us, pulling us, coordinating us and making sure we stayed on top of the countless exchanges that took place. Kudos JoDee – this was a great beginning for the Enterprise Data Lifecycle Management cult!

To the folks that were bold enough, fun enough, and willing to share your thoughts and create the discussions – Thank You!

So what now? We’ve completed the journey from New York City to Los Angeles (metaphorically) – but this is not a one way rental? Our goal is to keep posting a few days a week to contribute and keep the conversation going. Ah – but here is the challenge - the request:

To all of you that have been reading and thinking – this is the time to e-mail us with your thoughts and posts to be added to the blog. Game on! Write down your thoughts; give us your words of wisdom to share!

This was never intended to be a one way preach from Utopian’s to the data management community. We all gain by sharing, giving thoughts to the exchange. So – get ready, get set, GO! We can’t wait to see the posts, your thoughts, blogs, funny cartoons - whatever it is that you think makes us all start our day with a new thought and focal point for this great space.

Thanks again to all the well wishers, the emails of thanks, the contributors and let’s keep the fun rolling. Me? I’ll be icing my finger tips from hitting the keyboard so often :)

Doug Jones, VP, Utopia, Inc.

The Expanding Universe of Data

2010-09-23T06:44:00.003-05:00

It’s my first week of employment at Utopia Inc and I am out for dinner in Mundelein, Illinois close to Utopia’s global headquarters. A number of the new employees are sharing conversations over a few locally brewed refreshments on a patio under the evening sky. The conversations range from Utopia’s amazing service offerings, the places we are all live around the world, sports teams, fishing, golf, cricket (it’s big in India), and of course the latest techno geek toys available on the market. When it came to the new geek toy discussion, Doug Jones, my new boss pulled out his iPhone and fired up an app called “Star Walk”. My mouth dropped open as he pointed his iPhone at the sky (and I think I may have even drooled a bit of beer down my chin) as I all I could say was “WOW!” “Star Walk” by VITO Technology is a new application for the iPhone/iPad that allows you to tilt the screen of the device towards the night’s sky and it will show all the locations of the stars, planets, galaxies, constellations, nebula etc… in real time! The app provides amazing visual representation of cataloged data on all the unique locations in the universe. You may have seen this application shown on some of Apple’s latest television commercials. “Star Walk” is available on the Apple App store for about 3 bucks. I downloaded it the next day to my iPad and continue to drool to this day.

I have always been amazed at the ever expanding universe in which we exist and I frequently pick up and read magazines or books to try and better understand the massive galaxies, stars, and planets. It is hard to really contemplate the enormity of it all. When you try to normalize the vast distances, mass and gravitational effects, chemical compounds, temperatures, and the velocity of matter that exists “out there” versus what is known on earth your brain gets glazed over. I believe however, it is human nature to try and organize, understand and put structure to everything around us. In fact the universe was documented by early Greek civilization as vast arrays of random points of light from the stars were assembled and organized into thousands of logical structured pictures called constellations such as Orion and Ursa Major-the Big Dipper. (Amazing what you can do without television or video games to distract you…)

We have so much more data on the universe today than we ever have, but many significant elements still remain unknown and I believe will always remain unknown. The “Star Walk” app incredibly brings all this known data to our fingertips in real time in a clean, classified and consolidated visual view of the universe’s data elements. The clean structured data along with new technology permits for this kind of application to exist and provide such value and knowledge at your fingertips.

The parallels between the expanding universe and the expanding use of enterprise data are a bit of a stretch when you think about the universe is expanding faster than the speed of light and we on Earth will never be able to access this data on the edges of the universe. Fortunately, our enterprise data is only expanding times 2 on average (doubling) in every 1100 days. As we explore the quantum view of our enterprise data, it is comforting to know that we do have lifecycle data management apps in place to organize vast enterprise data repositories and to completely structure, classify and clean it so we can innovate with new technology and information systems that bring value to our world!

Live long and prosper…

Dave Lepa, Director Canadian Sales, Utopia, Inc

Götterdatarung

2010-09-22T06:42:00.003-05:00

In Wagner’s The Ring of the Nibelung a ring provides the possessor the power to rule the world. Through twists and turns of the lengthy opera, the ring forged from stolen gold, is the object of desire by many, including the gods, who scheme mightily to win it. Their plotting, passions and actions convolutedly, but eventually end in the ring being returned to the rightful owners, the Rhinemaidens. While the return restores purity and perhaps the promise of a new order in the world, it also results in the total destruction of the gods and their home, Valhalla; hence the name of this last opera in the Ring cycle, Götterdämmerung, or the twilight of the gods.

Non-opera buffs might ask, well . . . so what? Is it a stretch too far to analogize this mythological tale to data maintained in the realms of other gods, the DBAs? Could their Valhallas – data centers – be broken asunder if only data could be purified and returned to its rightful owners? Is there a Götterdatarung coming, a twilight of the old data order? A destruction of old ways of maintaining data, Valhallas of siloed applications with data that is not pure in the sense that it is not complete, rational, coherent, harmonized and unambiguous?

Enterprise systems are increasingly integrated, converging on the vision of seamlessness across applications in business. And technology today is finally catching up with this promise of standardized and harmonized data across the enterprise. The quest for those of us in the data business is to attain clean, honest data and in a metaphorical way, return that to its rightful owners, the data users. Never have we been closer to this ideal than now.

So data practitioners, let the trumpets sound, put on your horned helmets and venture forth to do righteous battle to herald a new order of fully interoperative applications that flourish because their data underpinnings are forged from the purest of data.

Tony Stypinski, Client Partner-West, Utopia, Inc.

Data Management Skills for Jewelry Making? OMG!

2010-09-21T06:40:00.004-05:00

So designing and creating one of my jewelry pieces last week, had me thinking– isn’t the jewelry making process similar to the Data Conversion lifecycle that I utilize in my profession too? Being a Data Management expert by profession we go through a normal data conversion cycle at any data migration project – we do the data profiling, data mapping, data transformation, test and finally load the data into the new system. And here I was executing the same process for my jewelry piece –

Data Profiling - scanning through my beads and stones inventory to separate the good / clean pieces from the defective pieces;
Data Mapping - designing the jewelry and defining which stone goes where;
Build - subsequently laying it on the bead board to see how the design will look when complete;
Test – hmm, I do not like it at first go...so I rearrange pieces
Defect Resolution (ha!) - now I get a better sense of how I can enhance the piece – adding spacers, unique findings etc.;
Production Cutover - Finally able to string it all together!! And OMG!! I have a completed jewelry piece as well as the same feeling that I get after every successful Go-Live!!

And if any of the above steps was missed – the final product would not turn out as I really wanted it to. Rethinking and reading my write-up, I am content that I am able to utilize my professional skills set to my hobby too!! And until now I had related the jewelry making hobby just being another form of “art” – well, no more!!

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

Are you a “data packrat?”

2010-09-20T06:49:00.003-05:00

With apologies to the Blue Collar Comedy team and “You know you’re a redneck if…”

You know you’re a “Data Packrat” if:

You’ve never archived any data from any system in your entire environment.
You’ve never retired a legacy system because you don’t want to risk not being able to retrieve the data.
The project budget for data migration is greater than the project budget for implementation.
You do not have a corporate data retention policy.
Your corporate data retention policy is to keep everything “just in case”.
Your corporate counsel has presented an official “Memorandum to Record” saying you’ve ignored his/her advice on data retention and archiving and therefore cannot be held responsible.
Your independent auditor has increased their annual fee because they need to send three extra people just to sort through all the data you keep so they can find what they really need.
Finance comes to you “just in case” you have the files they need to research an audit issue from 10 years ago.
The internal audit department makes you keep an inventory of your files.
You have boxes with data reels, 8 inch floppies, hard disk packs, 5 ¼ inch floppies, 3 ½ inch floppies, tape cassettes and readers for all in your office.
Facilities makes you move yourself because they don’t want to risk a Workman’s comp case if the movers try to lift and transport all those boxes, etc.
You believe “aggregate data” is for wimps – and you keep transaction history since the inception of your company.
New hires in IT are brought to meet you as part of the IT on-boarding process.
You believe having the data is your key to job security.
You can’t buy a laptop that has enough RAM.

Please feel free to send in your contributions.

Keith Boardman, Project Director

The Side Effects of Data

2010-09-17T06:34:00.002-05:00

x777771# , that’s it, simple. Easy passcode to remember. Effective, simple, great idea right? Well it only took me 8 times to read the number of 7’s – with all the advances in technology the one thing that hasn’t gone HD - are my eyes.

Okay, carefully, 1, 2, 3, 4, 5 sevens, got it. Now I need to punch the numbers in, one seven, two sevens, three sevens, four sevens, five sevens, then 1 and #, voila! Success!! What? Now they report it back to me verbally – did she say seven four times or five times? Sigh – I’ll be late for this call for sure. Doubt I dialed it right, in fact it looks like six sevens now that I stare it longer (nose attached to the monitor at this point).

True experience yesterday as I joined a conference call. Then I sat there realizing had I joined the wrong call, or had half the folks had the same experience we may have just parsed the audience into two different discussions. Think of the wasted man hours, lost revenue, additional strain all created from an easy passcode to remember.

We do these types of things daily in business without a hard dollar quantification to the business. Give it some thought and look at what drives your business process for a week. Take a daily log of where data can negatively impact even the most innocuous downstream events…I bet you’ll be surprised!

Any other good ones to share? Let’s discuss :)

Doug Jones, VP, Utopia, Inc.

“Old is Gold” – Is This True for Data?

2010-09-16T06:47:00.002-05:00

We are all too familiar with the famous proverb “old is gold.” But is this true all of our day-to-day data scenarios? Perhaps yes, when talking about “experience,” “knowledge,” or for that matter where “people” are concerned. But surely not when thinking and talking “data.” This proverb reminded me of an example from one of my IS-U implementations.

Data mapping workshops are one of the key tasks during any data migration project. During one of these workshops at an IS-U implementation project, many data elements were identified to be mapped for the legacy customer master data. One such scenario/example was the migration of “spouse” or the “contact” information as part of additional customer data. This was stored in one single legacy field in the legacy system. Upon data profiling and data analysis, it was found that the spouse or the contact information was almost never updated after a customer is first set-up for utility services with the company.

E.g. John Doe became a customer with a utility company XYZ in 1990. His contact information at the time was Jane Johnson – his roommate. However, Jane Johnson moved out in 1999. But the company never updated this information in their records – nor did they receive any updated information from the customer. Now when they were ready for a data migration to a new CRM system, they identified customer contact information as important information to be brought over. However, they had no way of telling if the data in that field was accurate or not. In this example, we knew the contact data is obsolete and will not add value when converted over to the new system.

Thus, this was flagged as a priority data cleansing item – to correct the data the client manually contacted each customer via telephone calls, mailers, etc. in order to ensure accuracy prior to data conversion. Thankfully, it was a very small utility company- less than 60k customer base and the data cleansing effort was affordable and manageable.

But what do we do when the volume of the customer base is too huge to be handled manually at all? Of course, mailers can be mailed to the customers, but the updates still need to be done manually. In such cases, it becomes necessary to make a key decision of whether to bring this data over or not.

The factors to be considered in making these key decisions may include, but not limited to…

Is the data element a “nice-to-have” or a must-have for the new target system?
Is there any negative impact on the business if the data is not converted?
Will the legacy system be available post go-live in a read-only mode?
How much effort is involved in the data cleansing effort? Can this be automated or will be a manual effort?

With this read, I hope, as a data community, every time we read or hear “old is gold,” we will think twice!

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

Garbage Salad: Using what you have for an Enterprise Data Strategy

2010-09-15T06:32:00.006-05:00

For those of you who’ve had the pleasure of visiting our fair Second City (Chicago that is), you’ve probably seen the “Garbage Salad” listed on the menu of restaurants both fine and fast. It was while sitting in Portillo’s and eating one such salad that I saw the genius of this mixture of meats, cheeses, and veggies. In its original incarnation, the “Garbage Salad” was a mixture of everything the chef didn’t use that day or the day before. Why not use these existing ingredients to create something that adds value to while minimizing the need for additional ingredients? Sure, you might have to buy some lettuce to provide the salad’s foundation, but the ingredients that make this particular salad work should be readily available in any kitchen.

The same concept can be applied to an organization’s Enterprise Data Lifecycle Management (EDLM) strategy. When speaking about EDLM, one of the key tenets I espouse is to work with what you already have in the organization. Your organization has the people, the expertise, some of the processes, and many times the tools to be successful in managing enterprise data. There is no need to re-invent the wheel. There is no need to completely re-organize your people or processes. An enterprise data management strategy should maximize the value what exists in your organization and, like a “Garbage Salad,” use what is readily available. It’s where things are not available or need additional support that where consultants come in. A company specializing in data management can serve as the Executive Chef (with years of experience and many dishes on their menu) who knows how to put all of the ingredients together in a unique, creative fashion designed specifically to suit your culture.

Rich Anderson, EIM Client Partner – Midwest, Utopia, Inc.

Dogs and Data – Why it’s so hard to keep them clean

2010-09-14T06:39:00.004-05:00

Have you ever wondered why data seems to get “dirty” so quickly – even when you have people assigned to create and maintain it according to reasonable business rules? I was musing on this question the other day and mentioned it to the family. Now we’ve always had a menagerie at our house and it’s not “the dog,” it is multiple dogs at any one time. One thing led to another in the conversation and we came up with the following theories:

Data seems to attract dirt just by existing – dogs tend to go roll in disgusting stuff, tromp through mud, brush against cars, etc. Apparently data does the same thing – we just aren’t around to see it happen.
Dogs left un-bathed start to smell very pungent. Data left un-maintained tends to decay

Dogs run off if not kept on a leash - and come back dirty and happy. Data gets extracted and used without proper controls and unexpectedly shows up in meetings dirty (and presumably happy – or at least the person using it is happy)

When a small wet dog shakes itself, the spray zone is small enough to wipe up with a dish towel. When a large dog shakes, it may be time to call a professional cleaning crew. Same applies to small and large volumes of dirty data.
Extending that analogy - Small dogs can be bathed in a sink by older children and small adults. Large dogs require a bathtub or a hose on the driveway and a Sumo wrestler. Low volume data cleanup can be handled with normal office software. Large volumes require more powerful tools and automation – or a small army.
Well trained clean dogs are a source of great comfort, companionship, and pleasure. Clean accessible data can provide the same feelings to operations managers and the executives.
The only self cleaning that dogs do is in unmentionable areas. Data doesn’t clean itself at all – if it did, I’d be writing this blog about the similarities between data and cats.

Keith Boardman, Project Director, Utopia, Inc.

FAQ on Data Cleansing/Migration

2010-09-13T07:14:00.004-05:00

Data Cleansing – an interesting term…what does it actually mean? Well, in simple terms it means the “clean-up” or the scrubbing of the legacy data to ensure accurate, consistent and usable data is migrated over to a new system (e.g. SAP CRM&B, etc.).

To identify the data elements that need to be “cleansed” in the legacy system, “data profiling” is executed on various sets of legacy data files wherein, various legacy data files are analyzed with the help of tools like SAP® BusinessObjects™. In this process, various checks are performed on data –

Pattern Analysis – helps identify the various data patterns that exist in current legacy system for a particular field. E.g. Drivers License field – showed more than 95 patterns of data that resides in legacy today. To overcome this, business rules will be defined on how to interpret and migrate the data accurately and consistently.
Completeness Check – some elements are required in applications like SAP®; therefore, a completeness analysis helps identifying where the key data that maybe missing and needs to be updated in legacy.
Duplicate Check – identify duplicate data – duplicate meters, for example. If there are any identified, de-duplication rules can be pre-defined prior to conversion such that target system contains clean data.
Accuracy Check – For e.g., email-id field – a simple accuracy check on this element is to evaluate if there are no “@” signs in the data then the data needs to be cleansed in the legacy system such that the information is accurate and will not error during conversion to the new system. These inaccuracies could be arising from typos, spelling errors or lack of naming standards, for example.

As an ongoing task, depending on the volume and nature of cleansing activity, various data elements identified for data cleansing are either cleaned-up manually by the business folks; or programmatically fixed during the extract process; or will be managed via business rules applied during the conversion process.

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

Ping! Data GaGa!

2010-09-10T06:50:00.006-05:00

Is this a coincidence or what? Just a few weeks ago we featured a three-part series on “iData.” Well, Apple has done it again, introducing “Ping”, “a social network for music.” Here’s a screen shot …

Speaking of Lady GaGa what better way to get those sticky eyeballs. Remember a decade ago, all the buzz around the dot-com craze – get sticky, get advertising dollars, get venture capital, go public, get rich? Steve Jobs certainly knows how to create ideas and sell product.

Ping mixes social networking (“network effect”) with Lady GaGa (“stickyness”, “utility”, “affinity”) with Genius (“business intelligence”, “CRM”, “data mining”) with consumer products and digital assets on royalty (iPods, iPads, iPhones, iTunes – music, movies, and more) to create an eco-system of incredible value.

It’s so simple in delivery, yet there is great complexity behind the scenes. Bottom line? Value for users (consumers), value for Apple, value for stockholders, value for vendors (musicians).

Being the great country we are, I’ll bet a large percentage of all of those people I just mentioned, who are benefiting from this highly interconnected universe, are donating to charity and good causes.

Charities could use good data too – to help convert those funds into food, supplies, housing and education, and logistically get it to those who need it most – in the quickest and most efficient ways.

Can’t wait to see the next techno-revolution from Steve Jobs’ imagination. Steve, if you’re listening, please create an iCharity – that would be your greatest legacy. Thank you.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Are you ready for GDS? Do you know what it is?

2010-09-09T06:32:00.003-05:00

GDS stands for Global Data Synchronization, networks and protocols that make it easy for trading partners to exchange information that helps grease the wheels of commerce through the supply chain. If you’re a manufacturer, distributor, or retailer and you haven’t heard about GDS, then rest assured you will.

GDS was developed to enable trading partners to exchange product information – pricing, descriptions, packaging, and so on-- via data pools that manufacturers, distributors, and retailers can subscribe to, posting and receiving relevant information to help them order more accurately. There are data pools for various industry segments, such as retail, medical, electronics, and many others.

Information from these pools helps distributors and suppliers:

Speed products to market, delivering increased customer satisfaction
Take the ambiguity out of reordering, resulting in fewer returns and fewer out of stocks
Make invoicing more sure and accurate, reducing charge backs and disputes

In order to work effectively, data that is synchronized (i.e. exchanged between trading partners) must adhere to common standards and be classified according to norms. Otherwise, there is no value if the data being synchronized are bad and buyers and sellers can’t exchange reliable ordering and product information through the data pools.

In order to get ready for GDS you need to put your own product data in order first. This begins with making sure that you understand your data, typically by analyzing product information to make sure it is clean, complete, standardized, and de-duplicated. Then you can begin the process of adjusting the data to conform to the data pool requirements for content and standards.

Tony Stypinski, Client Partner-West, Utopia, Inc.

Persuasion Trumps Dissuasion, Data Builds Value in Networked Systems

2010-09-08T06:38:00.004-05:00

Ever heard of the “network effect?” It’s a principle coined by John Hagel, III (co-author of “net gain” and “Net Worth” with Arthur Armstrong and Marc Singer, respectively). It’s based on Metcalf’s Law (value of a network is related to the square of the number of connected users as follows V = n(n-1)/2 = n**2 –N / 2). It means simply that there is an “increase in the value of a product or service to future users as the number of other users adopting the product or service grows”.

Data is that kind of product and when provided as a service, it’s that kind of service.

Hagel introduced the “amortization effects” principle ,which is that which reduces “the average cost to develop, produce, or deliver a product as the number of customers expands.” Data has amortization effects.

Finally, Hagel said that “Learning effects drive down operating costs as businesses over time discover new ways to operate at lower cost.” Data has learning effects.

So let’s look graphically at how data has created value over the last century …

In the “old days” people traded shells for goods or bartered 1-on-1 their time for something of need. These are 1-to-1 transactions. Large-scale business was not possible, unless each participant had deep pockets. Data was “1 shell” for “1 handful of potato seeds”. You have a unit of measure, a description and a price. The transaction involved the exchange of hard goods for hard currency – high friction.

Next, forging way ahead to the time of Alexander Graham Bell, we have business-to-business and business-to-consumer transactions over the phone line, still 1-to-1 styled transactions; however the connectivity could be 1-to-many, in discrete 1-on-1 events over time.

Because of other simultaneous inventions, such as trade credit, hard currency no longer needed to be part of the transaction which of course helps speed up business and reduce the friction (transaction costs).

When only two phones existed there was one connection, with five phones there were ten, with ten there were forty-five, and with 100e+6 (phones) there are 4.99e+15 (connections) – but nobody can actually make a call like that! Seems more like Web 2.0, right?

Then comes the personal computer and Al Gore’s internet – or was it (D)ARPA(NET)? Anyway, this enabled “B2B” and “B2C” transactions that were nearly many-to-many, in nearly real-time – using credit cards (Pcards) the cost of transactions begins to drop significantly. And there’s still a unit of measure, a description and a price for each transaction. You see data growing in prevalence in all business functions – purchasing, sourcing, operations, maintenance, sales, and marketing, accounting, etc.

Here there is a divergence in the emphasis on where investment should be; brick-n-mortar or click-n-order; applications, hardware or data; web sites and ecommerce and social networks? Truth is, every form of business model requires good data, and the right blend across the spectrum of media. People have become discouraged from participation in the next generation due to fear, uncertainty and doubt – but look around and you see the transformations, you see the successes.

Then along came highly interconnected businesses, VPN networking, private online marketplaces, public markets online, Amazon, eBay, iTunes, and so forth – highly connected networks with highly complex, collaborative streams of all kinds of data; data that is creating incredible value, extreme value.

Because of huge advances in computing, networking, applications, banking, finance, business processes, government intervention (or liaise faire), people and policy, we have the ability to mine data, create decision support systems in ‘real-time’, and even apply predictive algorithms to inventory management or even marketing. Wow!

Data is persuasive, rich in content and in context – and it can be shaped in a way that considers our past behaviors to help us make decisions on what to do next. Sometimes it is too persuasive and the suggestiveness and psychology of the message needs some fine tuning. Sometimes the data simply isn’t proper so while the applications may be right, you get garbage in – garbage out results.

Data has come a long way as have the systems that surround it – and together have created trillions of dollars (shells) in GDP, economic expansion, and better quality of life for mankind – well, maybe we’re not there quite yet, but if we do it right (sustainably) then hopefully QUALITY will rise above QUANTITY in how we humans measure our successes and failures, versus just chasing the dollar, which after all is just a promissory note written on paper. Paper currency is going the way of the dodo.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

The Data Memory Game

2010-09-07T07:52:00.005-05:00

Well – I want that 45 minutes of my life back! So my Dad calls with a quick request – help him recall his Yahoo! email password. Simple right?

Okay – so background, new laptop, old one died, saved passwords are no longer available. No problem I tell him (cringing through the phone because I know this is going to crush my Tuesday evening – new episode of Covert Affairs will have to be watched on the DVR). So we set off on a virtual identification path by selecting the forgotten password link – which he assures me he tried prior to calling me. This is where the dilemma began.

As part of the forgotten password process he can answer two simple questions, which he had put in place when he got the email account eight years ago – see it coming? First question – where did your Mom grow up – WACO – done! Second question – what street did you grow up on – huh? What the heck Dad? You’re 65+ and now you are playing mental challenge games? I can’t recall what I had for breakfast and here he is playing memory recall for fun? Okay - “simple” he says, “it’s Hesketh Street.”

Hmmm, okay – types away – INCORRECT.
Take 2 – variant on Street to ST – INCORRECT.
Take 3 – try it without a space – INCORRECT.
Take 4-10 included attempts of all the other variants we could recall, including misspellings of Hesketh, etc… - Finally we were locked out for 24 hrs.

This fat fingered, mistyped answer is a simple fix – and yes we got into the account. Clients face this challenge every day in a different format. They end up with 10 entries into the materials database for a basic part like ¼” bronze wingnut. They find that the lack of standardization and taxonomy can allow folks to enter it as wingnut, bronze ¼”, bronze wingnut ¼” – etc…. Company names, addresses, part numbers, descriptions have WAY too many variances that can allow the end users to create duplicates, waste time rationalizing multiple records and trying to fix systemic issues transactionally.

The problem is compounded when there are regional or geographical preferences, multiple legacy apps for the same purpose, etc… As companies aim for standardization, single instances of applications, one version of the truth – they find the enterprise data quality requirements can be quite large and complex. Understanding the data, the business rules, the downstream implications and how the business is impacted is step one. Step 2 is buying my Dad a little notebook to write down all his passwords so I can get back to my show.

Doug Jones, VP, Utopia, Inc.

It’s all Ball Bearings, Son! The Devil is in the Data

2010-09-03T06:47:00.003-05:00

Some say the "Devil" is in the details - in our line of work, we phrase it that the "Devil" is in the data...and the definitions.

Take for example a recent client who’s MRO material master was somewhat suspect. The business objective was to enable inventory optimization and reduce associated carrying and warehousing cost (an industry average is 25% of costs associated to inventory). A secondary objective, was to enable strategic sourcing and spend analysis. The goal was to save millions.

Our approach was to identify the business processes as well as the data processes that lead to this sub-optimal situation. Our first step was to understand, profile, and analyze the data, we call this a Data Health Assessment™ (DHA). We found duplicates, mismatched naming conventions such as “Bearing, Ball” and “Ball, Bearing”, and overall a great deal of inconsistent, standard-less, and inactive/flagged for deletion material masters. This is not to say that they weren’t running their business or supporting plant maintenance, it simply meant that it was not at optimal levels. And in this economy, we’re all being pushed to do more with less. Our appreciation, and later, the client’s was that business processes and objectives were being compromised due to their bad data.

With evidence in hand, we could illustrate the data corruption – a quick example was eight ball bearings that had eight different material masters and eight different descriptions - but were all being used for the same functional requirement! Apparently they were buying them for eight different prices from different suppliers; one supplier was selling the same functional equivalent for two different prices. Business case – clear as day. We recommended and executed on a data transformation and enrichment engagement and once the data was standardized, the client was well on their way to inventory and spend optimization.

It was the little things – the definitions in the data… things like ball bearings that helped save millions and over achieved their business goals. Logically, now that the data’s clean – our guidance is to help them keep it clean with sustainable governance and data creation. But that’s another story for another day.

Do you have a similar experience? Let me know.

John Ferraioli, SVP Data Lifecycle Management, Utopia, Inc.

Profoundness of Data Part 3 – Data Has the Power to be Transformational

2010-09-02T06:44:00.003-05:00

In Part 1 “Bad Data Begets Bad Business” we talked about examples of companies that succeeded or vanished because of the condition of and usage of their data or data related to them in some way – data can make winners and losers.

In Part 2 “Mind Your Business, Tend Your Data” we took a deeper look at how data can add tremendous value, how it has dimensions and complexity, and that it is part of a larger value-creating eco-system.

Here in Part 3 we’re going to talk about business transformation.

With Pixar, the data became so complex and advanced, there was a fear in Hollywood that real human actors would be replaced by digital actors, and there was a fear that 2D cartoon animators would become obsolete – and to some degree that happened. The salaries for human actors decreased as a result of less demand for their services. For the animators that evolved, adopting new tools (using computers and 3D animation techniques), they kept their careers, made a bigger impact, made more money, and forged a new art form.

To me, it’s a matter of supply vs. demand, for the synthetic vs. the real, and basically both (human and virtual actors) are tools utilized to tell a story. But what is “real”? The story is real even if the “actors” are not. If there’s a good story to tell, and one tool or another is the better medium through which to tell it, then it comes down to which tools provide the best ROI in the bigger picture (no pun intended). Likewise in business the ‘story’ is the way executives use tools to deliver value to the market and owners.

There will always be a need for human actors in movies, in plays, in theatre, and the art of 2D animation will be preserved, in part because classics are made in that medium and because 3D is always story-boarded using 2D as a starting point. The beauty of our world is in its diversity.

While we’re talking about movies, we could just as well be discussing the construction of buildings, the production of widgets, or the sale of retail goods; you have people, computers (technology), processes and data, and an unlimited way of combining them all in order to achieve the end result – value for the stake-holders, whoever they are and however they deem to measure and define that value. Generating “value” is why businesses and (non-profit) organizations are created in the first place.

As in our previous iTunes® discussion, iTunes® is a high-value adaptation of CRM, using elements of product, consumer, scheduling, demographics, etc. to create value. iTunes has an enormous task to deliver all those sources and elements to the right customer at the right place (their email) at the right time (ASAP).

Every CRM system has a corollary – an SRM/SCM system --- one helps create, measure, monitor and track demand and the other helps create, measure, monitor, match and deliver to that demand. I think of ERP as being the underlying backbone of processes that keep a business going, like a central nervous system. CRM, SRM, SCM, ERP … they all have people, process and technology … and they all have data, lots of it. Business Intelligence simply gives us the view we need to set direction and policy, and make decisions rapidly. Needs good data!

Good data = good business!

Pixar and iTunes® create immense value without polluting the environment, without hoarding raw materials thus without driving up the prices of such, and without taking up vast amounts of energy thus their carbon footprint is quite small per dollar of sales. Pixar is lean, green and actually not very mean.

Profoundness of Data – Conclusion

A closing thought is this - the businesses that elevate the definition and utility of data to higher levels (extreme levels), to create more value (extreme value), to find greater efficiencies, to explore and create new markets will be the survivors in the 21st century. Those who enable this will become millionaires and billionaires.

Pixar is another example of a company that re-invented an entire industry using people, process, technology, and data, and has become truly ubiquitous and relevant. Let’s enable more Pixars – the world is a better place because of them and those like them.

Just a side-bar; Steve Jobs was responsible for both Pixar and iTunes®, as he co-founded Apple computer (which invented iTunes®) and he gave Pixar its original $10M needed to launch which became a $7.4B acquisition by Disney. Thank you Steve Jobs for making the world a better place!

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Profoundness of Data Part 2 – Mind Your Business, Tend Your Data

2010-09-01T06:46:00.002-05:00

In Part 1 “Bad Data Begets Bad Business” we introduced the concept that if you don’t keep an eye on your business, someone else will. Let’s make a huge connection between your business and data. If you don’t keep an eye on your data you might end up losing your business. Your data is very valuable. It needs your attention and you need to tend to it.

Data is multi-dimensional and also multi-universe. For most people it’s the physical and structural data that first comes to mind; attributes and the values of those attributes, the definition and names of attributes, it can be the combination and order of attributes that describe a thing, it can be the arrangement of things in a hierarchy called a schema or taxonomy, it can be tax rates, taxes paid, the prices of things, it can be all the data that describes items, materials, vendors, customers, employees, patents, contracts, work orders, purchase orders, and so on – everything you need to run a business.

We can elevate the complexity of data still – it can be the assemblies of things like machines or equipment, items in a BOM or configuration, spare parts that go together with a work order and a particularly skill of worker to perform a maintenance task, the schedules of maintenance tasks that keep a factory running, the replenishment schedule of raw materials on a production line – data can be everything you need to make widgets and to keep your factories and assets working in top condition.

Further still, in complexity, in more recent times, we’re adding huge amounts of unstructured documents, presentations, spreadsheets, emails, pictures, audio, and even video. Voluminous!

There is a literal explosion of data – not unexpected with the proliferation of technology and input / output devices with every consumer of products and information, and with every employee at every company. But a lot of bad data is being created, poisoning the landscape. More on this later – data can corrupt business eco-systems.

Here’s the really profound part - as Pixar has so eloquently shown, data can be so complex, as to become character. One’s and zero’s can be elevated to the status of a personality used to tell a story, and there have been stories told that have generated billions in value. That value is not just measured in dollars, but it is measured in social terms as well. What child has not learned about sharing or about friendship or family from favorite 3D characters?

I am reminded of the character in the Star Trek series whose name actually is “Data” or “Mr. Data”. He’s not human. He’s a machine with a positronic network (a brain, computer) and he’s loaded with all kinds of structured and unstructured data – but with the additional element of self-awareness and the will to act. Aptly named Data – he is the embodiment of what good data can do – and when he’s acting strange, it’s because he’s got bad data or his brain has had some wires crossed. This isn’t unlike when a company acts strange – bad data, or the processes that use the data are wired incorrectly, resulting in sub-par performance. Bad data can be viral.

Back to data, it can become the very essence of a personality captured in 3D animation. Mr. Data was a human acting like an android – a bit of a short-cut. Think of Buzz Lightyear and Woody, the Incredibles, Cars, Monsters Inc., Wall-E, and so on --- in every case, data has been defined, organized, orchestrated in a way giving us an image, sound, a moving animation and virtual (if not humanized) characters. Pixar has animated out of vapor, enabling the telling of incredibly heart-warming, entertaining stories.

In “The Matrix”, we have another “universe” within which our minds are prisoners of a virtual world created to keep us unaware of how our bodies are truly being used – as slaves to produce energy (“copper tops”) for the self-aware, menacing machines. There were previous attempts at creating this virtual world and because it was imperfect, “entire crops were lost”, as the machines would say. This is analogy to some real-world events.

This is, to me, a huge leap in how to perceive the value of data and its utility. When data elevates to character, it has color, tone, voice, mannerisms, personality traits --- it comes to life --- and it can be a huge asset to the movie studio, as well as the toy manufacturers, the retailers – entire industries. The follow on are the sales of toys, clothing, accessories, DVD’s etc. derived from the character and the rest of the “cast” from animated movies. Data is real.

And guess what – all of those industries use data in CRM to help better understand the customer and in SRM to better source materials and ERP to better produce and deliver the goods, and to manage the people and assets efficiently in that eco-system.

We’ll dive deeper into Pixar’s story in Part 3 – Data Has the Power to be Transformational.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Profoundness of Data – Part 1 – Bad Data Begets Bad Business

2010-08-31T06:39:00.006-05:00

Introduction - Transform an Entire Industry

This is a three-part series on the profoundness of data; how it can bankrupt you … or make you a hero, a champion of change, even a new kind of industrialist, informationist, perhaps billionaire!

In recent times, there has been a profound loss of trust between people and business and government. Transparency and correct reporting increases trust – as does operating in a sustainable way - that takes good data. Toyota Motors had significant problems with parts and the reporting of safety issues. That has caused a recall of 8.5M vehicles and damaged Toyota’s image and reputation for high quality – their stock has dropped 50% in three years. Something is wrong in the way Toyota deals in information. Data makes yesterday’s losers and tomorrow’s winners. Let’s explore how.

Profoundness of Data, Part 1 – Bad Data Begets Bad Business
Profoundness of Data, Part 2 – Mind Your Business, Tend Your Data
Profoundness of Data, Part 3 – Data Has the Power to be Transformational

Part One – Bad Data Begets Bad Business

Consider these hypothetical headlines:

“Reader’s Digest*, purveyors of rich content, losing market share by 30% a year”
“Blockbuster** busted by Netflix using better data better”
“BP broke because of bad data about blow-ups, maintenance, and safety records”
“Moody’s basically bankrupt because it brought bad data to the market”

There are countless companies that have gone bankrupt, been acquired, or otherwise fallen due to lack of attention to their market, their customers and ‘their’ data in ways that others did.

When there is a paradigm shift in technology, process, people, or something else that disintermediates a company or industry; one should cannibalize their own business or someone else will.

Data creates tremendous value. Take an example from the movie industry, where one art form which came to be considered a national treasure was displaced by another (the “cannibalizer”), and how that changed an industry and perhaps, history itself, as far as entertainment is concerned.

Now, the Walt Disney Company originally turned down the chance to become an early owner and adopter of Pixar. Pixar had developed processes, intellectual property, and hired the people (talent) that became the idea factory. Then later, after the idea that traditional 2D cartoon animation was becoming obsolete, Disney changed their mind.

While watching “The Pixar Story” on CNBC the other night, it dawned on me, as John Lasseter was talking, that while we think of data as just 1’s and 0’s in computer memory, he’s elevated it to an entirely new level, and as a result created incredible value.

The amazing thing is, Pixar’s business requires very little in the way of raw materials (unless you count “creative thought” from human talent as a raw material) and the output isn’t even a material thing (unless you count the plastic DVD’s the movies are stamped onto). The output is a virtual object, yet the Walt Disney Company paid $7,400,000,000 for Pixar (in stock) in 2006. Yes, $7.4 Billion!

Good data – transformed a $10M startup into a $7.4B entertainment industry powerhouse. Pixar’s movies are available on iTunes further adding value.

In Part Two - "Mind Your Business Tend Your Data," we’ll explore further how Pixar created such immense value using data. In Part Three – "Data Has the Power to be Transformational" – we’ll examine business transformations and how data enables it.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

* Reader’s Digest aka RDA Holding Co., parent company of The Reader’s Digest Association, Inc. coming out of pre-arranged Chapter 11 bankruptcy on February 19, 2010 – the company has reduced distribution intentionally and is selling off certain businesses and eliminating certain non-performing product lines to strengthen its balance sheet and return to profitability. The company has its first history in 1920 and its magazines are enjoyed by millions world-wide – but is its business model as relevant today as it was 90 years ago?

** Blockbuster was delisted from the NYSE on July 7, 2010 as it struggles to restructure. Netflix which didn’t build thousands of retail brick-n-mortar stores has seen a 10X increase in share price since 2005.Netflix has a CTO (Chief Talent Officer), a CPO (Chief Product Officer) and a CCO (Chief Content Officer) – this speaks volumes to Netflix’ approach to data and their business model.

Go-Live Risk and Cost Overruns vs. Data Readiness

2010-08-30T06:53:00.002-05:00

Many IT projects do not hit their mark. How many times have you heard, “You can have it on time and on budget and have all the features working 100%, no problem!”? The reality is go-live delays and less-than-expected ROI. What if you could shorten the time and extend the ROI of your IT project?

Figure 1: Time is Money Curve

Expected breakeven point (BEP) is based on the time it takes for positive cash flows to equal the negative cash flows, cumulatively. The yellow curve does so earlier, because it passes $0 sooner and more steeply, thus the total cash expended is recovered quickly. The area under the curve is total dollars.

Expected ROI is based on the magnitude of the future cash flows, which level off, and the time to get there; again, the yellow curve gets there faster and it is higher. We like to see the curve shift up and to the left. There’s always risk it will shift down and to the right … there are many risks in complex IT projects.

By investing in your data early in your IT planning, you will very likely avoid the risks of unknowns creeping into your new system on go-live day. So, what kinds of problems creep in?

Toxic legacy data dumped into new system causes same old problems as before
Inability to optimize vendor contracts across the enterprise
Inability to streamline inventory controls and spare parts across plants
Cannot run effective national sales and marketing campaigns
Mass marketing mailings still going to incorrect addresses, costing millions in postage

...and so on, depending on what business process application(s) you are implementing

And what are the implications caused by those problems?

Cost overruns due to extra resources deployed to resolve problems
Iterations and delays to go-live, increased risks of going live, unpredictable results
Stalling the benefits intended by the new system :: time is money
Delayed cash flow impact caused by the efficiencies of the new, sub-par system
Inefficient use of assets (your business isn’t as productive as it could have been)
Inefficient use of cash (working capital is tied up while the system is off-line)
Ratios like Return on Sales, Return on Assets, and EPS going down
Sub-optimal long-term performance of system :: reduce business performance
More cash than planned to get your system live and function :: reducing ROI
and more…

The incremental investment to get your data right is marginal when contrasted with the millions of dollars spent on new business process application software and computer systems, and even less significant in light of the implications bad data can cause. Yet, that small investment can have a substantial impact on your overall IT project planning and success, and help reduce risk.

Whether you’re planning a new IT upgrade, migrating to a new ERP system, adding business intelligence, MDM or consolidating systems, don’t forget to assess your data landscape and plan for it in parallel with your IT investments. It could be a game-changer for you.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Improving Data Quality During Migration

2010-08-27T06:44:00.003-05:00

As a software consultant specializing in data migrations, the idea of addressing and improving data quality during migration frequently is brought up. I mean, we are migrating core business objects with business rules at the field level… isn’t this a golden opportunity to improve on our data quality?

The answer quite simply is yes. But just how far can you go? A true data quality project is…well…a project. With a data migration project, DQ is not front and center. Moving the data to the target system is the priority, while trying to improve the quality at the same time will certainly add work.

You know what? The truth is this is a golden opportunity for the business to address and improve data quality. The truth is that without much added ETL effort, you can definitely improve your data quality during the migration process. The business owners of the data will have an increased level of effort by enhancing business rules, but the delta work pays huge dividends. I’ve adopted three basic rules and reinforce them with the business as we embark on ETL specifications and mappings.

First, each data mapping session is always preceded by data profiling of the object. Show the business what data lives and breathes (or died) in the legacy system. Second, be specific and identify areas for improvement. A good data migration consultant can identify and point these out easily with the results of the data profiling always available. Lastly, be clear about what cleansing can be done via the transformation effort vs. a cleansing process outside the ETL loop.

For example, take a material master object. Product types, product groups, and other ‘code value’ fields can easily be cleaned up with a cross-reference table deployed during the transformation. Whether this cross reference table has tens of entries to simply map value A to value B, or hundreds of entries to consolidate or further delineate values is no additional work during transformations, and a powerful data quality weapon.

Cleansing outside the ETL loop, for example, would be standardizing text descriptions, or validating city, state, zip code combinations. Good software can cover many of the ‘outside the ETL loop’ cleansing. I‘ve been using SAP’s BusinessObjects software as an ETL/DQ tool and it handles all of the examples above inside the ETL loop. Now that’s DQ power!

Jim Anderson, Sr. Project Manager, Utopia, Inc.

Data: A Tool to Reinvigorate Advertising

2010-08-26T06:50:00.009-05:00

Once upon a time, there was this crazy idea – what if cataloging and auctioning spots in movies and shows for ad placement – contextual ads, modified by region for demographic / geographic optimization, could reinvigorate the advertising industry? For example, a soda can – a generic “green screen” soda can, that could be used in a scene (act 3, scene 5, of episode 7, of some show #1234, of XYZ studios, location #9) which would be known from history to generate 1M eyeballs for 30 seconds (assume it’s a funny scene that takes place in a restaurant).

Here’s what the “schema” might look like, for the static information:

Name: XYZ Studios
	Location: 9, Burbank, CA
		Show: #1234, “Restaurateurs”
			Episode: 7
				Act: 3
					Scene: 5, Table in Restaurant
						Duration: 30 seconds
							What: Generic Soda Can
								Brand: to be auctioned
									Primary Key: XYZ.9.1234.3.5

That virtual soda can would be bid out to auction for advertising space, on an advertising auctioning platform, to a variety of some beer, soda, or juice makers, or whomever, for the highest payment for that spot in that particular scene. We have seen product placement in shows and movies, where the actors have to hold the object with the logo positioned just so, or the object is static, placed on a table somewhere in the shot. If it’s a real object shot in the original then it has permanence – generic virtual objects can easily be substituted, via digitally editing.

The bidding would occur by such time so that the exchange of moneys and the digital images could be done before the production of the scene. The scene in fact could be shot far in advance with the virtual soda can replaced by the virtual branded can from whomever bids the highest for certain airings of that show, or by region --- hot cocoa in the winter up north --- cold beer in the south in the summer.

This is another “schema” of the dynamic (changing) information indicating usage vs. demographic vs. location vs. season – which is linked relationally to the static information above by the Primary Key:

Primary Key:
	Primary Key: XYZ.9.1234.3.5 Location of Broadcast: Northeast Timeframe of Broadcast: Winter Demographic: Females Age 35-55 Usage: Soda Can or Equivalent Action: Actor takes sip with label at camera Eyeballs: 1,000,000 (total)

A database of historic broadcasts suited to the above schema shows 250,000 females fitting the demographic for hot chocolate beverage – so various brands of same would bid for that spot. The platform would use data about scenes, the demographics, the number of eyeballs, the time of day that scene would air, and attempt to match it with prospective advertising bidders who might “fit” the contextual and usage dynamics for product placement.

The concept caught my eye. It isn’t unlike how advertisers bid for airtime to show their commercials during high-demand shows such as the Super Bowl – but in this case, it’s micro-marketing rather than macro, efficiently creating an exchange of value for many smaller ad placements at lower prices, extracting a fee for each transaction. Bulk trades could be easily as enabled as the mini-trades, with strategic placements, etc.

Where’s the value? Imagine all the consumer products that could be placed, say 1,000,000. Now imagine all the movies and shows and episodes aired in a given year – globally, say 1,000,000. You have 1TT possible matches, 1 Trillion. Now let’s say a 10 second spot gets $1000, so you have $1BB in potential advertising placement. Let’s say this platform gets a 10% fee for matching advertising to placements – that’s $100,000,000. Not bad. Nice annuity.

The point is, using a little imagination, creativity, an initial $10M investment for piping the infrastructure of industry in a new way, one could create a new, lower friction means of conducting business, grow market share, re-invent an industry. Oh, and that $100MM per year would probably net you about $1BB in market value.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Data Garage Sale

2010-08-25T06:35:00.004-05:00

You can say it countless ways, but the key is how you say it. Getting and keeping master data clean is important. How you identify its’ importance is the hard part, right? Tether standardizing your product, vendor, material, customer, human resource data to the net business benefits – simple! Okay – not simple – but critical if you are going to demonstrate to the rest of the business the importance of driving these types of projects.

In a project currently underway we have a client that identified 3-5% of their MRO spare parts in inventory are for equipments and assets no longer even part of the business. The ability to sell those parts, reduce shelving, warehousing and management of these materials had a hard dollar savings back to the business. The effort and costs associated with finding, standardizing and cleansing master data in this case was FAR exceeded by the cost savings and realized dollars to the bottom line.

Can you link your savings to your master data clean up? How about linking the on-going data governance? Imagine if you were archiving closed historical transactions and really moving along the data management continuum to the most effect data storage techniques?

Doug Jones, VP, Utopia, Inc.

IS-U Data Migration Workbench a.k.a. EMIGALL (2) – Data Import Programs

2010-08-24T06:36:00.003-05:00

In my earlier blog, I gave a brief overview of the IS-U Data Migration Workbench. In continuation, I have summarized how this tool helps in reducing the need for ABAP development for the upload programs.

SAP does not deliver standard upload programs in the ISUMIG Tool. The import program for a migration object is dynamically generated utilizing the customization of the “migration object.” A “migration object” consists of “auto structures” that further contain the field level details. Each field in an auto structure can be customized with different processing rules for the data. Without going into too much of the technical details, most commonly used processing types are:

Initial – initializes a field – i.e. the field is not to be used in the object.
Fixed Value - defines default values for the field for all records.
Transfer – legacy data is to be “transferred” as-is without any interim processing.
Rule - legacy data can be processed with an ABAP rule – simple or complex ABAP rules can be added for the additional field processing.
Via KSM – legacy data value needs to be validated, checked and processed utilizing the ISUMIG KSM table – e.g. for referencing the higher-level objects. As a result, the SAP newkey of the higher-level object is transferred for processing.
Conversion – a mapping cross-reference table can be defined for various legacy values mapping to SAP values using this processing type.

The “migration object” also consists of various predefined migration events that the ABAP code gets generated against. The events, combined with the field rules, together facilitate in the import program generation. The import program cannot be altered using the ABAP editor. These programs can be modified or enhanced using these migration events and/or the specific field rules. Therefore, each migration object needs to be generated or “activated” after each modification to the field rule or a migration event.

Thus, utilizing this tool efficiently, to its maximum capability; aids in reducing the extensive ABAP development for the conversion upload programs.

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

iData – Part 3 “What if Data in the Eco-System Went Bad?”

2010-08-23T07:53:00.007-05:00

Let’s cook your noodle a little now. What if the iTunes® data was incomplete, out of date, inconsistent, duplicitous, incorrect, misleading even? For one I wouldn’t be able to discover music I like, thus my purchase frequency and dollar volume would be lower, or non-existent. My playlists and tags would be all wrong. ‘Genius’ wouldn’t make proper recommendations, I’d be very unhappy, and Apple’s stock price would probably plummet when the music stopped flowing out (and the cash stopped flowing in) and the inventory of iPods® and iPhones® piled up … well the eco-system just would not function properly. It would be very ill, toxic, maybe even die.

Everyone would be impacted, the producers, the music makers, the entire music industry perhaps would plunge into a recession and take the rest of the country with it. OK - a bit dramatic – but only to illustrate the point.

At the end of the day, the question isn’t who owns the data so much as who’s benefiting from the eco-system. Fact is, everyone participating in the eco-system benefits from the data so we all have a vested interest in making it correct. Each participant has a role and each role has certain responsibilities for data entry and rights to data usage. These are rules in the system (more data). These are like business rules in a company. Here we have data governance being implemented and managed, and we’re not really aware that it’s going on around us. Perfect!

The metadata and data models, the workflows, the networking, the ease-of-use, the trust in the system – all need to function as one homogenous system for optimum value and performance. The eco-system’s architecture is based on a simple premise that the foundation of it, which is the “data”, is going to support it for the entire life-cycle of the system and its upgrades and retro-fits.

This is fundamental. It is like a law of physics or nature. It just doesn’t change. Businesses are eco-systems. Properly functioning eco-systems do not have viruses running around infecting sub-systems, making the eco-system ill. Bad data is like a virus, its toxic, and it can make a business ill. Businesses have to evolve to survive in the world in which they “live”, and part of that evolution is the data, the technology upgrades, and the people learning to adapt with it; accepting change as a good thing, because change is beneficial to all when managed correctly.

Hence the need for integrated, end-to-end, full-spectrum enterprise data lifecycle management (EDLM), creation, governance, stewardship; and that, as we can easily see, takes people, process, policy, promotion, and technology. I don’t really care who owns the data – we’re all beneficiaries. Now, where’s “my” iPod®?

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

iData - Part 2 “How Data Creates Business Value”

2010-08-20T06:45:00.005-05:00

The content in iTunes® is made up of binary bits and bytes – we like to call this data. It’s arranged in a certain way we like to call it music (complex data). These “objects” have certain characteristics or attributes, we like to capture such as Title (Name), Author (Band), Length (Time, in seconds), Genre / Classification (Rock, Blues, Jazz) and so on (more data). iTunes® allows me to add my own “tags” to these objects like “favorite” and apply a star rating system.

Each time I play a song iTunes® adds one increment to that song’s counter (usage) – all of this is still data. As the data is built layer upon layer, more and more value is added to it, depending on its use, distribution, how many people it affects and what it affects, where, when and why.

This data, the processes and system wrapped around it gives us the ability to create playlists which are yet another kind of data – linked lists of my favorite songs from certain authors from certain genres. iTunes® ‘Genius’ looks at all this and says, using heuristics and pattern matching technology, maybe this guy would like to buy some more from this band or in that genre, and presents me several appealing options (to buy more data). Bang! Targeted or micro-marketing – it’s like an iRobot for your music.

Millions of people are adding “user-defined” content to this eco-system, and it takes on a life of its own. Who owns the songs? Who owns the tags? Who owns the playlists? Who benefits from those tags and playlists? There is a lot going on here.

There are different kinds of data, metadata and systems with workflows for usage of that data in iTunes® which create value – entertainment value for you and me, and enormous business value for Apple® (owner of iTunes®). Think of iTunes® as a city and we’re driving around using the roads which have tolls. The city can maintain the roads and build more of them. The toll gives us the right to use the roads, but we don’t own the roads.

Wait a minute! Those tags are mine! I own those – how dare Apple® make billions of dollars from information I provided them? I should get a discount on the music, right? Well, no, because this is a system of give and take, of an exchange of information for value, of entertainment for money. Each participant in the system contributes some and takes away some. The intermediary or infomediary (Apple®) probably benefits the most – but they have the greatest burden and risk as well. Thus, Apple® invested to build the infrastructure and invented it, hence thrives on it.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

iData - Part1 “What is Data and Who Owns It”

2010-08-19T06:53:00.003-05:00

iTunes® allows me to search, learn about, and retrieve relevant songs that meet my tastes (specifications). I can then purchase (act upon) individual songs (rights), download a copy (digital representation) to my computer over a network and enjoy. I’m enabled to make lists of favorite songs (metadata with tags) and synchronize my playlists (BOMs, assemblies) to devices that allow me to listen to (use) that content in the arrangement I prefer (priority).

Who owns the song? Who owns the tags? Who owns the playlists? Who benefits from those tags and playlists? Let’s explore that. And let’s keep a keen eye on particular terms we’ll use to examine data ownership and value. Note also when I buy a song, Apple® sells it – and transaction is a process that involves yet other kinds of data that must be correct for the events to succeed.

But first, remember that a right may not necessarily entitle you to exclusive authority and responsibility over an object, but rather give you limited access and control titled in your name, for a limited amount of time, or with other conditions and restraints (aka, license).

Somewhere around here I’ve got some old cassette tapes. I owned the cassettes, the tape in them, but not the music – I only owned the right to listen to it (as long as I had physical control). I could not copy, redistribute, or resell the music. My cassettes could be shared with friends and played on any cassette player (but not 8-track players) – and only one usage concurrently. For two concurrent users, we needed to have two cassettes of the same songs, and two players.

In order to keep up with technology, to upgrade, to avoid obsolescence, I had to upgrade my entire music collection (library of rights) to CD (a different media vs. cassette tape or 8-track) and also purchase CD players (different technology). Hmmm… my rights are being messed with.

Didn’t I already own the right to listen to those songs? Yes! But I only owned the right to listen to those songs in the previous format and only on devices that were compatible. Aha! Don’t you love entitlement – I hate it when what I feel entitled to is taken away, don’t you?

With iTunes® I’m re-purchasing again. You’d think these music authors, producers, “labels” and distributors made enough money on me already. Yet, I keep paying them over and over again for the rights to listen to the same old songs. But I love my music – it keeps my motor runnin’.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Data Management Initiative Check Point

2010-08-18T06:41:00.004-05:00

As a check point – we’ve now published 75 of the “100 blogs in 100 [business] days!” Our first blog published was the introduction of our initiative followed by Data is Delicious. The outreach, comments, articles, e-mails and sharing has been more than we could have ever hoped for. Thanks to you - the data management community for your time, attention, and willingness to share your feedback and comments.

There are countless groups that help share our story – not just in blog format, but rather as the tip of the spear in corporate America – concerned data management experts helping make their organizations more effective every day. We’ve collectively benefited from the work at ASUG, TWDI, DM Review, Gartner, Forrester, LinkedIn Data Management Utopia group, various other LinkedIn Group communities, SAP Insider Learning Network, SAP Community Network, etc. These groups, like a simple blog and conversation online - are great ways to affect fresh new ideas, collaborate on successful levers, tools, tips we can deploy in our own organizations.

As an enterprise data lifecycle management (EDLM) company we find that we need to approach each client in a variety of ways. While our methodology, framework and processes remain constant, each client is in a different place on the continuum of data management. Where is your company on the continuum? Have you completed a Data maturity model to determine your company baseline? Processes? People? Technology?

Share with us and the rest of community – where are you today? What are your biggest challenges, concerns and areas of opportunity to make your business better? Help continue the conversation….

Doug Jones, VP, Utopia, Inc.

IS-U Data Migration Workbench a.k.a. EMIGALL

2010-08-17T06:39:00.000-05:00

IS-U Data Migration is a broad topic of discussion. This blog post just covers the basic introduction of the Migration Tool without going into any technical details.

IS-U Data Migration Workbench, more commonly referred to as EMIGALL Tool these days, is the mandatory Data Migration Tool for SAP’s IS-UT or the CRM&B data model. This can be accessed via t-code EMIGALL. Essentially, this tool is utilized for the data transfer from the legacy system(s) to the IS-UT or the CRM&B application.

This tool represents migration objects as logical units corresponding to the IS-U business objects instead of database tables. In other words, data is not represented and inserted into the dB table by table; instead all table updates required to create a business object entity (e.g., business partner) are executed together. This is achieved by utilizing the standard IS-U function modules that correspond to the dialog transactions/functions. This also ensures optimal data consistency check for all the required transactional validations. These function modules are direct input based and not batch input. Thus, avoiding the transactional screen processing and thereby, results in improved performance.

The migration objects are represented in a predefined migration hierarchy and the data transfer should be executed in this hierarchical order. For example, contract account cannot be migrated unless a zero-error data transfer or, an acceptable conversion success threshold is achieved, for its higher-level object business partner has been accomplished. The picture below gives an overview of most of the migration objects generally utilized across various utility implementations. This list could be slightly different from project to project.

Key and status management is a key component of the IS-U Data Migration Workbench that facilitates the maintenance of cross-reference between legacy system keys (external) and the newly generated (internally or externally) SAP object keys. Essentially, each data object will be transferred with a unique OLDKEY (easily identifiable legacy system key). And during the data transfer, for each of the successful conversion, this OLDKEY to NEWKEY (internally / externally generated SAP number) relationship is updated and stored in the KSM table that is maintained dynamically within the workbench. It also ensures the smooth data transfer in the predefined hierarchical order. Wherein the higher-level object is referred using its OLDKEY when converting the lower-level objects that are dependent on it.

Subsequently, the migration function modules will go “fetch” the corresponding NEWKEY from the KSM table to correctly associate the lower-level object to its higher-level object.

Of course, the scope of ISU Migration Workbench is not just limited to the above.

The tool, in fact, allows for:

Customization of migration objects that minimize the ABAP coding typically required for various data conversion programs
Dynamic generation of migration object import programs
Distributed data import
Import job monitoring
Detailed error analysis and correction
Customization for performance optimization during data imports

Hopefully, I will blog about the other aspects of IS-U Data Migration in the coming days too!!

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

Data Migration Migraine

2010-08-16T07:20:00.002-05:00

It’s been known to make grown men and women cringe in fear and weep.

It’s the prospect of having to migrate legacy data to a new system.

The people who sold the new system promised efficiencies, better reporting, more productivity, ROI, reduced TCO, and so on through the alphabet. Didn’t they know that the legacy systems had evolved over time with scant regard to data governance? Data structures had likewise changed, often reflecting the biases and peccadilloes of the data architects or DBAs who happened to be in charge at the time.

As a result, data “quality” was inconsistent, if present at all: not just in the nonconformity of individual data elements, but also in the uneven contents of fields themselves.

The challenge of rationalizing, normalizing, and cleansing the entire mishmash of data as a prerequisite to loading into the new system, that’s where the nightmare, the data migraine resides. The prospect of scrolling through screens of Excel or Access files to find non-conforming data, maintaining glossaries of standard data elements (remembering which already had been assigned and which still had to be created!). Now that’s a headache and a half. And the time to do this – go-live promised a mere 12 months away by some management honcho who’d never examined a data file in his life. Ohh the agony, ohh the nightmare, ohh the migraine!

This is the time to investigate data quality and ETL tools; time to see if this problem can be automated, routinized and industrialized – Advilized out of existence. Fortunately, there are tools available that can make data cleansing, normalization and rationalization relatively easy and automated. In fact, it’s possible to pre-process data so that it can be loaded into the new system with 100% success the first time around. Adopt a data quality expert to find out how to turn a migraine into a high-gain, career advancing success!

Tony Stypinski, Client Partner-West, Utopia, Inc.

Data Elements – Duplicate Parts

2010-08-13T06:45:00.000-05:00

One of the very first things that happens when one is challenged to reduce the stock of spare parts in inventory – whether it be MRO spares or parts for production – is the search for duplicates by part number.

The results of that search can be interesting, but are those duplicates you found actually duplicates?

Before jumping right in and starting to perform drastic surgery, make sure that you investigate the manufacturer’s numbering schema. You may quickly find that many manufacturers have schemas that require additional data. For example, one manufacturer I found had what appeared to be unique part numbers … but on closer examination, that specific part number covered multiple sizes of the object. The same part number was used for all impellers (of a specified material) of that configuration in sizes from four inches through 16 inches. Had one simply purged the system of “duplicates”, a scream from the preventative maintenance group would shortly be heard.

Alternatively, the fact that multiple unique-appearing part numbers exist does not mean that the items are unique. Upon investigation, it was found that one manufacturer utilized THREE different numbering schemas for the same item. That manufacturer had a part number, an item reference number, and a catalog number. Amusingly, even taxonomy folks working for that manufacturer weren’t completely aware of that fact until we started to investigate and included them in discussions.

Finally, if the manufacturer practices part number enhancement – wherein the base number is extended by adding option codes – one cannot use the base part number to identify duplicates (but can use them to find “close enough” matches). The examples that spring to mind are transmitters and bearings. If the complete part number is not a match, one cannot assume duplication. An application that calls for a specific bearing, say an SKF 5306ZZ, cannot necessarily use an SKF 53062RS. True, both ZZ and 2RS connote seals, but steel and rubber are quite different (which is why you seldom see a locomotive on the freeway).

Peter Dahl, Senior Consultant, Utopia, Inc.

Data Owner Responsibilities and Characteristics

2010-08-12T06:36:00.003-05:00

Every enterprise will definitely have the objective to improve efficiency and effectiveness in managing data. Efficiency will result in minimizing the cost of administering data and effectiveness will result in maximizing the data quality. From my perspective, a data owner should have at least the following roles and responsibilities to achieve efficiency and effectiveness.

Responsibilities:

Approval of…

Data Definition
Data- Related Business Rules
Coding Rules
Data Classification and Access Rules
Retention Rules

Assign Responsibilities to...

Data Administration - To add new instances and To maintain/update Specific Characteristics
Data Authorizer - who must approve additions and changes to the data
Data Controller - who must periodically verify the quality of the data

Review Change Requests...

Characteristics:

Unique to ensure only one definition(thus not one per business unit or per site)
Senior position, to ensure rules can be enforced
Knowledgeable about the data (though preliminary reviews of the business rules often take place with their staff)

Let me know if I need to add anything to responsibilities and characteristics to make it even more efficient and effective.

Vinod Reddy, Data Migration and SAP MM Techno Functional Consultant, Utopia, Inc.

Maximizing Your Data Quality Tool Investment

2010-08-11T06:37:00.004-05:00

In the data management space, a data quality tool is a must have for a sustainable enterprise information management program.

Depending on your software choice, it is a versatile tool to have deployed as it can address a number of your basic data management capability needs: extracting data, profiling, standardizing, enrichment, matching and merging and loading data across a variety of hardware and database platforms, file formats, purchased software and proprietary applications.

As important as it is to have utility tools that provide the above technical capabilities it is how those capabilities can be deployed that really provides the business value to the company.

The obvious use for a data quality toolset is in the area of data migration; either the one-time legacy system to the new target application ETL process, or for on-going periodic data migration as in the case of ERP to data warehouse data transfer.

Other options for this toolset is to leverage the data transform and validation routines you created for your one-time data migration process and redirect those jobs to run against your new strategic source system. Either passively, after data entry, to monitor your data quality and adherence to business and validation rules or real-time at the point of data capture to automate enforcement of data standards and ensure data integrity.

A surgical use of the data quality tool is managing your “denied party” requirements. Typically, each country maintains a list of individuals and companies that are to be blocked from trade and commerce with legal entities within a given country. Data quality tools can be leveraged to access these “denied party” lists and compare to your customer and vendor files to ensure your compliance.

One final suggestion with regard to the leverage and reuse of your data quality tool suite is in the area of mergers and acquisitions. Think your “one-time” data migration process now on “wash, rinse and repeat” mode. Any organization that is active in the M&A space should really be leveraging data quality tools. Data migration is usually one of the longer tracks to plan for in an M&A project.

A clean team can use the tools to evaluate your acquisition target and assess the data quality and provide estimates for the time required to convert and transform the data to your target applications using your existing business rules as its baseline.

The clean team can also assess the number of unique customers and products you will be acquiring and the overlap you will have with their existing suppliers – all data points to be assessed during the due diligence process.

Furthermore, having a standard data migration process in place allows you to stream line the data conversion process and enables you to take data off the critical path during the transition phase. There is no substitute for front-end data assessment required to map your acquisition data structures (context and structure) to your target applications. But 80% of your transformation logic and 100% of your data load routines are reusable which can provide significant reductions in cost and time to deliver.

Jim Whyte, Director of Solutions Consulting, Utopia, Inc.

Why SAP Data Archiving?

2010-08-10T06:37:00.004-05:00

The other day my client manager Alex came over to my desk and asked me to have a cup of coffee with him. We got our coffee and began chatting and Alex said to me… “Kiran tell me something… why archiving, why can’t I cut all that cost by adding some space on my storage?”

Well guys what I told him that day qualifies to be documented on a blog and here is how the conversation went.

When SAP developed products they did not introduce the archive development kit as an add-on. They knew that once clients start implementing their solution the data is bound to grow, and when data grows you need a method of managing it for the following reasons:

Reduce Hardware Costs

When data is archived it can be stored on cheaper storage devices, this not only reduces the overall TCO of the system, but also increases the overall performance of the SAP system.

Reduced Administration Costs

The smaller the database, the smaller the administration cost is for the overall system. The time needed for everyday checks, background monitoring, etc. is reduced.

Legal Requirements

The law of the land states that any firm doing business needs to have history data up to xx amount of years. This can be for audit requirements or for tax purposes.

Business Requirements

Are your employees complaining of slow response time during transactions? Are they facing transaction issues during peak hours? Is statistical data showing drastic performance issues? Well you know the answer – it’s time to archive unnecessary data from the database…

Technical Requirements

Apart from all the above, archiving helps your basis teams with the following actions:

Reduced maintenance and back-up recovery
Reduced time for SAP upgrades
Increased system stability and availability

“Why can’t we just add more storage space?”

Adding storage space to your existing database will just temporarily solve your issues, but what a lot of us fail to see is the big picture and how this will increase your problems in the long run.

Archive programs are designed to process data in blocks, when you select a particular table to be archived blocks are identified in those tables and are selected for archiving. If today we increase database space by adding more storage then tomorrow we run into the risk of failure in archiving data.

The only way to archive at that point would be to bring down the landscape and concentrate all the memory of the SAP system in fixing archives. Imagine the amount of down time to perform this activity, and downtime is directly proportional to huge losses on the balance sheet.

It is always advisable to start an archiving implementation with a SAP implementation to make sure that the entire landscape remains healthy.

Now as you can guess, this was a lengthy coffee conversation. Was Alex able to convince business for archiving? Well let me keep you guessing on that, but what is more important is we understand the architecture behind archiving and try to get the best out of this technology.

Kiran Viswamurthy, Data Archiving Consultant

Enterprise Data Lifecycle Management for Sustainability

2010-08-09T06:44:00.012-05:00

Imagine driving your big SUV around New York City looking for South and Broad, but due to a typo, you were supposed to find 220th and Broadway. You drove an extra 30 miles, wasted two hour’s time (without traffic), burned six gallons of gas, and emptied three tons of carbon monoxide into the air. What if this was a common occurrence, say three times a day, 365 days a year? 30,000 wasted miles. What if 100 people had this problem – 3,000,000 wasted miles? Because time is money, data is also money!

Suppose you run a courier company and instructions come via automated CRM software with the addresses for pickup and delivery – and that data is wrong (as above) 30% of the time, 3 orders out of 10, daily. That’s a real-world, time-wasting, energy-consuming, expensive problem. Let’s say you’re UPS or FedEx or a national bank, and you’ve got millions of queries into your database daily, and your database contains millions of duplicates and millions of incorrect entries. Huge problem!

Through efficient virtualization of servers and optimization of configurations of networks, software and databases, companies that run large data centers are reducing their energy consumption. This means tens of millions of dollars in electric bills saved annually – for just one (1) large company. It means less demand on the aging, deficient energy grid. It also means hedging against future costs of power, because we don’t know what electricity will cost in five or 10 years. This means long-term sustainability.

But still it takes more CPU cycles and time for search and retrieval when the relevance of the data found is suspect. If your database is 30% more complex and 30% larger than it needs to be, because of incomplete, incorrect, inconsistent and duplicitous data, then it is 60% more energy consuming than it should be. That’s if you can limit your attempts to just one or two to find what you need.

The server virtualization benefit is decreased costs for servers, software licenses and networks, and the power consumption (CapEx and OpEx reductions) but – can this be improved further still? The answer is emphatically “YES”! Reduce and optimize your data – there is an exponential explosion of volumes of data, both structured and unstructured. But how?

Cleanse, consolidate, correct and complete the data, tag appropriately, then eliminate the duplicates. Also, employ intelligent data archiving / retention strategies. This reduces your database footprint. If the data is cut in half, the search time is cut by 4X (basic binomial search performance), thus the power consumption for each search (by human or machine) is cut four times. But there’s more.

Keeping the data “clean and green” via real-time data governance (and organizational data governance) and making sure newly added data conforms to data models and rules (business and environmental) helps assure that the power consumption levels stay reasonably linear or reducing, rather than increasing as business speeds along.

Bad data doesn’t just mean bad business, it also means bad for the environment. The new paradigm for data isn’t just clean, but green and lean.

David Kuketz, EIM Client Partner – Southeast, Utopia, Inc.

Clean data . . . clean business

2010-08-06T06:42:00.003-05:00

Ever have a new project manager, ask you the old saw: “How do you want your project delivered: on time, on budget, highest quality? Pick two!”

Fortunately, for business and data, the choice between clean data / clean[ly run] business does not have to be exclusive. In fact, they are more inseparable than most would think. Cleanly run businesses thrive on clean data, the lifeblood of informed strategy, competitiveness, and success. And these days it’s becoming easier and easier to insure clean data as the underpinning of a well-run business.

This is thanks to wider recognition of the importance of data governance to the accuracy of data and the growing adoption of data cleansing tools as part of an integrated approach to managing data from inception and during its lifecycle through to archival. Data governance programs recognize the importance of having good data to run a business well, and provide support for the concept and resources to make it happen.

Particularly when businesses are large and have multiple sources of data – think regionalized customer and vendor databases, general ledgers for different divisions and countries – clean data is vital for consolidated reporting across the enterprise. As BI and data warehousing technology has grown in prominence, so has the need for good quality underlying data. Without excellent data, how can sense be made of the business, how can strategic direction be planned, and how can execution against the plan be measured? The data has to be clean in order for the business to clean the competition.

Tony Stypinski, Client Partner-West, Utopia, Inc.

Do Corporate Cultures Change?

2010-08-05T07:26:00.002-05:00

I’ve been a professional in the data quality, data integration, and data migration software and services industry for many years. As such, I’ve worked with all kinds of corporations in all kinds of industries from all over the world.

In my experiences, no two corporate cultures are the same. As you can image, some organizations/departments are easier to work with than others and I feel honored that I have represented solid companies offering solid solutions over the years. Fortunately, I’ve only had a few bad experiences to show for it.

I was recently asked to work on a project for an organization that I have a long storied history with. This organization has always been demanding with high expectations. That in its self never bothered me. However, when displeased with a particular software provider, this organization never bluffed as they would simply toss out the incumbent the first chance they got.

I’m sure it isn’t a stated policy. And I’m not stating whether that is a good or a bad strategy. It has simply been their corporate culture.

Having the opportunity to reacquaint myself with this one particular organization, what should I expect all these years later? It made me wonder whether corporate cultures change. In my case, there’s only one way to find out!

Chuck Schardong, Senior Consultant, Utopia, Inc.

What is Master Data?

2010-08-04T06:42:00.002-05:00

Definition: Master data is reference data that is shared or exchanged across strategic applications, work processes, lines of business, or enterprises.

My definition has been personally field tested for the better part of 20 years and has served a number of Fortune 500 organizations very well. For me, the focus is on the sharing and leveraging of the reference data across the various organizational boundaries that master data can and must cross.

The definition is based on the goals and objectives my former employer was trying to achieve back in the late 80’s – ERP consolidation, moving from a decentralized, LOB/regional multi-national company to a global, top-down, centralized management, standardizing applications, infrastructure, business processes, roles and responsibilities, and reporting. To achieve that, we needed common reference data across our solution landscape. Does that sound familiar to you?

We were reducing the number of ERP systems, moving to four regional instances, based on SAP software (common applications). This required a broader view of master data than most are willing to adopt today. We wanted to ensure that our four SAP instances were configured identically. It was the start of our implementation of enterprise data governance, data standards and standardized master data content. We knew we couldn’t achieve one, without the other.

I often wonder if I would have the same deep belief in my MDM definition and support of centralized data governance if it would have been possible to run our entire enterprise on a single instance back then. I might not have been as concerned with managing configuration table changes across four production instances (e.g. order types, pricing conditions, and unit of measure, currency and exchange rates in addition to customers, vendors and materials) are system development landscape with common data content across development, user acceptance, training and production environments.

We successfully implemented a global data warehouse a few years following our ERP consolidation and we recognized that all fifty, yes 50, master data objects and hierarchies we maintained in our propriety MDM application were either enterprise KPIs or global reporting dimensions in our data warehouse. That wasn’t by design up front, but it became a benchmark for us when determining what additional reference data objects we should consider to centrally manage on behalf of our enterprise.

So what is master data? It is somewhat in the eye of the beholder – but I firmly believe it is those data objects shared and exchanged across your enterprise, that are core to your business operations. Additionally, they are the data objects that you will pick up and migrate from one strategic application to the other, as they are timeless. They are your enterprise “family jewels” and will be handed down from one generation to the next.

Jim Whyte, Director of Solutions Consulting, Utopia, Inc.

Parametric Data – Don’t let the “metric” scare you.

2010-08-03T06:38:00.003-05:00

It’s funny how looking at different parts of a compound word gives different connotations. In our taxonomy world, parametric has nothing to do with the metric measurement system … it’s all about parameters. Parameters are the “old” version of our current terms attributes or characteristics. However, parametric also implies that there is order to the characteristics.

Why would we care about order? Ok, that’s a tongue-in-cheek question; order is invariably a good thing. Seriously though, nowadays we seldom manage to run any data system - whether it is a mom-and-pop shop with a simple database of retail products, or a full-fledged ERP – without considering the value of an excellent taxonomy. And, until someone creates and donates a worthy full-fledged taxonomy to the public domain, it’s usually developed in-house.

A good taxonomy will include all the elements that allow the advantages of database tools – a noun and modifier (or class), attributes (or characteristics), a short description and a longer, fuller description. Ideally, the attributes are ordered. Why ideally? Well, let’s think back to my parametric term. The implied order in the list of attributes allows the use of the noun and modifier (class) and the attributes (characteristics) to automatically generate the full description. The order dictates, then, that the description will contain the relevant information about the object from most to least relevant. Why? Well, again, we can use database tools to make comparisons, and allow the use of fuzzy logic in searches. If the data in a text block flows from most to least relevant, the most relevant attributes can be matches, while the least relevant can be fuzzy matches … which allows one to find something “close enough” in an emergency. Parametric data also allows for the most effective reviews for potential duplications.

The critical thing, of course, is the order … and it is quite difficult to arrive at a perfect order. Five taxonomy experts will likely have five opinions on the order of any specific item. To confirm that, check out product catalogs for five different bearing companies and you’ll very likely find that none agree on all the attributes, or on the order of relevance. My rule of thumb for dictating the order of attributes is that each successive attribute should narrow the set of possible matches the most. For example, the noun bearing will contain all possible bearings. The class roller bearing is an immediate huge reduction in the bearing set. One then needs to look at OD, ID, width, material of construction and other such relevant attributes to determine which will reduce the set of potentials most.

While one can only dictate the ordered attribute set and the resulting parametric description for one’s own business, a well defined and controlled taxonomy will allow the use of standardized tools to reduce the variability of data and/or goods as well and helping ensure the data/items can easily be found and harder to inadvertently duplicate.

Peter Dahl, Senior Consultant, Utopia, Inc.

Moving Your Data to a New System?

2010-08-02T06:40:00.003-05:00

It’s Just Like Moving Your Stuff Into a New House!

I recently had the opportunity to discuss the subject of getting data ready to move to a new system with one of my clients. After the standard presentation, I made the following analogy to attempt to get the idea better appreciated. Imagine two established adults getting married and moving their stuff into a new house. Both have complete sets of items, but they need to make decisions on what to take and what not to take.

A few cases to consider:

You go through every item that you currently use. Do I really need this item in the new house? What is its function or purpose? If I don’t need it, then don’t take it. If I need it, then does it need to be cleaned or repaired?

You go through the items that are stored in the attic or basement. These are the old items that you currently don’t use. Do you want to keep them or are you going to write them off? What is there value? If you were to take them, where would you store them in the new house?

You must decide on duplicate items, like washer/dryers. You don’t need them both, but how to decide which one to take? Newer, better fit, more functional? How to manage the negotiations, who wins and who loses. Maybe neither one wins, and you decide to buy a new one and not take the old ones.

Maybe the new house has a need for items that neither person currently has? What is the new standard, type, color, size?

This work can be very time consuming, especially if you have a lot it items to consider. You need to make decisions; sometimes against items that you have a history with, but will not fit will well in the new environment. Clean things up and make the decision before you move everything over and then find out that it doesn’t work. Look forward, what do you really want?

If you do this well, it will make your future life a lot better.

Duane Failing, Senior Consultant at Utopia, Inc.

Can Data Quality Influence A Company’s Ability to Sustain Environmental Initiatives?

2010-07-30T07:15:00.003-05:00

In reading through our various blogs, my colleague Tony Stypinski’s blog The Greening of Data got me thinking about another aspect of data. Data quality can help corporations become more efficient by supporting their “green” sustainability initiatives.

The “green” initiative is on the rise with many companies scrambling to adhere to the movement of being environmentally friendly and the ability to reduce their carbon footprint. The push for this initiative can stem from a number of reasons, such as the want to be environmentally friendly, the need to reduce costs, legislation requirements, customer preferences, etc.

One real life example that immediately comes to mind is UPS . This company has several initiatives to continuously improve their environmental performance and reduce their carbon footprints. Not only are they feeling the benefits, but so are their customers. They are feeling the relief in their checkbooks. UPS has environmental initiatives which focuses on improving efficiency, specifically by reducing miles flown and driven and using the most fuel efficient mode of transportation. The company’s philosophy has made them acutely aware and focused on their environmental impact as it pertains to how they conduct business and operate.

Now you are probably thinking that is all fine and dandy, but where does data come into play? In the case of UPS’ initiative to improve efficiency, I can only assume that their approach to this initiative was based on…first to provide drivers with optimized routes based on package destination to reduce miles driven to deliver packages. A route planning application and good supporting data would be required for this initiative. Also, due to the dynamic nature of fuel prices, they will also need the capability to continuously monitor fuel consumption. This includes the type of fuel consumed to determine fuel cost and emissions to ensure compliance and facilitate decisions regarding what fleet uses the most fuel and what type of fuel should be used. To come to such a critical decision UPS most likely could not have done so without good quality data. In order for company to be able to sustain and improve their initiatives over a long-term, they will need the capability to continuously monitor fuel cost, emissions, and services offered to achieve proper balance. Data governance can play key role in enabling, accelerating adoption of these initiatives, and sustaining the initiatives to realize the benefits offered by them.

No matter what a company’s initiative is it will always be influenced by data in some fashion. Whether your initiative is “green,” pink, or purple with yellow polka dots it will always necessitate the need for good quality data in order to make the initiative a reality and be successful. Don’t you agree?

Pawan Gupta, Senior Consultant, Utopia, Inc.

Dealing with Multilingual Data – Is it a Nightmare??

2010-07-29T06:36:00.001-05:00

Jim Whyte, director of solutions at Utopia, Inc., wrote an interesting and educational article entitled Data Governance – It’s Your Business Processes, Stupid… In the blog he stated “data governance involves addressing the framework for data-related decision making”.

That statement triggered a thought…what if international data is involved in a data governance project? How difficult is it to profile, standardize, enrich, de-duplicate, or integrate with multiple languages (e.g. Chinese, Japanese, or French). How difficult is it leverage the 20% technology to maintain this data? How difficult is it to analyze multilingual data? Is it really going to be a nightmare???

In my past professional experience with multilingual data I have successfully leveraged SAP® BusinessObjects™ Data Services tool to successfully deal with such data. Dealing with multilingual data involves setting a code page and language packs at three levels: data, operating system, and tool. SAP® BusinessObjects™ Data Services tool has proved to be very compatible and efficient with multiple languages in my experience. Its data cleansing packages allows your company to successfully work with multilingual data.

I am not saying the task at hand is very easy, but it is definitely feasible especially with the right tool and skill sets.

I am curious to hear how other organizations are dealing with multilingual data. What tool set are you leveraging?

Prashanth K. Vaida, Data Migration Consultant at Utopia, Inc.

Five key factors for Data Migration Success

2010-07-28T06:29:00.003-05:00

Many of us have been a part of various data migration projects at some point or the other. We all know data is important. But can we name at least five key factors that will ensure a successful go live? Well, here is my two cents (although I believe it’s worth a lot more ). Though, I know there are more than just these five key factors the following are my top five.

Understanding Data – it is absolutely imperative for the data team to understand the legacy data and how it exists in the legacy systems. Along with how the data is structured in the legacy systems.
Resource Scheduling – now of course, you want the right resources with the right skill sets and knowledge to do your data migration project! Imagine, calling a plumber to fix your AC at home!! I’m sure we would never think to do that. Just the same, we should ensure the data team houses resources with appropriate data skill sets as required for the project.
Scoping the Requirements Accurately and On Time – not too early, not too late. You know how the saying goes: “there’s a time and place for everything!” You don’t want to scope out the data requirements before the business blueprint requirements are signed-off on. If you do, then just be prepared for those “change orders” to start rearing their ugly heads well into the beginning of the development phase. Unfortunately we’ve all run into these way to frequently!!
Data Quality Framework – ensuring that a data quality framework is in place is equally important among other things that will lead to a successful data conversion. This implies not just having means/tools to perform data profiling and analysis, but also a plan to identify data bottlenecks and recommended solutions and/or data cleansing plan of actions as well.
Data Validation vs. Data Testing Strategy – projects need to ensure an effective data migration testing strategy is in place and is part of the data migration project. The clear demarcation should be also be made between data validation and data migration testing. Wherein, data validation can comprise random sampling methods to ensure that the data is converted accurately as per the data mapping rules. However, a data migration testing strategy should comprise a series of iterative “mock conversion runs” for all objects in scope where the converted data is utilized by the business process / functional teams to thoroughly test the integration points/transactions as well. In addition, these iterative “mock conversion runs” also allow for validation or conversion programs, conversion error analysis and fixes that will eventually lead to a “zero-error” data migration!

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

SAP Data Migration - Are You Up for the Challenge?

2010-07-27T06:29:00.003-05:00

SAP applications and ERP projects are big. They are cumbersome. They are expensive. Companies are constantly trying to mitigate their risks for these organizational changing events. Data migration is a key component of that project and it brings about several significant challenges:

CHALLENGE 1: NO Business Disruption During Go Live and Beyond
Poor quality master data unable to support core business processes may disrupt your organizations daily business
Ongoing support of current legacy systems during transition can be overwhelming
CHALLENGE 2: SAP User Adoption Needs to be High
Lack of quality data can make new system unusable to new users
Return on Investment is delayed as users don’t take full advantage of the new system or capabilities because they don’t “recognize the data”
CHALLENGE 3: Avoid Surprises
Delays are frequently caused by surprises in the data
Surprises early in a project can be managed – surprises late in a project are a huge problem
CHALLENGE 4: Data Governance

The data governance approach should be establish before the migration event to prevent issues after go live

So, what is the solution? For me there are four pillars on which a successful SAP Data Migration is based:

Tools - You need a world-class toolset for both data quality and data integration (Take a look at SAP® BusinessObjects™ Data Services).
Methodology – This should be a proven methodology that involves the business early, aligns with the overall project milestones, and is flexible enough to account for differences in each organization.
Templates – SAP data migrations have been done hundreds of times. Make sure to use the accelerators and templates that are available. There is no need to re-invent the wheel.
Expertise – SAP data migration is a special skill set. It can be learned, but best practices indicate you need the resources who have been on that train before.

Ultimately the message is simple, data migration is a risk. Address it early in the project so that you don’t have surprises late in the implementation.

Rich Anderson, Client Partner, Utopia, Inc.

Data Standards: Abbreviations – is Terse Worse?

2010-07-26T06:42:00.002-05:00

Ofttimes we are forced to squeeze as much information into a small space as we can manage. Many or most computer systems limit the characters available for describing something and that forces one into the SMS mindset of using acronyms or abbreviations to get concepts across quickly.

While it can be amusing in the SMS world, in the world of data that is seldom the case. One needs to very carefully control the use of acronyms and abbreviations. In an SMS, one is communicating with a known entity on the other end, and available to explain that OMG was not “Old Man Goldfarb”. In data, the person who created a description is rarely around to explain that – in this case, at least – ABS meant acrylonitrile-butadiene-styrene (plastic) and not Antilock Braking System. Depending on the context, ABS could be an abbreviation for a muscle group, or for “absolute”, or various acronyms such as the Australian Broadcasting System; Asset Backed Securities; or … well, you get the picture.

Although context can often help us discern which acronym or abbreviation is intended to be conveyed, one must question the relative value of saving a letter or two. My rule of thumb is that a common and generic acronym should always be used when it is the norm – PSI jumps to mind. Non-generic or industry specific acronyms should be avoided unless one is absolutely certain the description will remain in-house or at least in-industry.

Using the theorem that we have very little time and space to gain the reader’s attention and understanding, one must ensure that we strive for readability … and not try to impress with how much we can cram into the tiniest space!

BRG OUTBD SKF A123-BX is a nice terse descriptor. A practitioner familiar with the rotating equipment class can probably quickly discern “bearing”, “outboard” and the manufacturer and part number. How about the accountant trying to do some forensics, or that order desk clerk who’s just started today?
Always keep in mind that your audience may not be who you intended it to be; may not be as educated in your specific taxonomy as you are. Make sure you balance terseness with readability. If you do have to use abbreviations or acronyms, you certainly want to make sure that you have them documented and the list available.

Peter Dahl, Senior Consultant, Utopia, Inc.

SAP® BusinessObjects™ Data Services Tips and Tricks

2010-07-23T06:31:00.002-05:00

There are lots of tips and tricks for using data services, but here are several of my favorites which I have recently implemented or have suggested to clients to implement in their designs.

Create and Reuse Objects

This helps save on valuable development time, allowing multiple implementations to share common options, and allows future changes in option settings to be made once and automatically distributed to many implementations.

Replicate SAP base transforms to your base transforms and then customize for your business rule standards
Create custom functions such as search and replace logic
Create embedded data flows to allow potentially complex data flows to easily be reused

Good Programming is Simply Good Business

While data services essentially eliminates the need for custom code development, the concepts of good programming constructs and syntax remains

Simplify business rules

instead of a multi-operand statement, use in construct

day_of_week_initial in (‘M’,’T’,’W’,’F’)

instead of

day_of_week_initial = ‘M’ or
day_of_week_initial = ‘T’ or
day_of_week_initial = ‘W’ or
day_of_week_initial = ‘F’

In an “if then else” statement, put the most frequent occurring conditions as the first rule, second most frequent as the second rule, and so on. HINT: Profile the data first!!

Minimize the number of input reads and output writes perform as much logic as you can in memory and consolidate the number of transformation in a data flow if possible

Chuck Schardong, Senior Consultant, Utopia, Inc.

What is Data Management?

2010-07-22T06:46:00.003-05:00

There are probably a number of definitions floating around in the data space. One I came across was from searchdatamanagement.com that defines data management as…

“Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner.”

As a data subject matter expert I see data management on a much grander scale. It is essentially the process of defining data strategy, data architecture, data models, data definitions, and eventually applying these aspects into the company’s day-to-day business processes. The key aspects of data management are:

Data Standardizations (including naming conventions)
Data Definitions and Modeling
Data Cleansing and Analysis
Data Retention Policies

Essentially, these core factors assist in facilitating the effective management of data throughout its lifecycle within an organization.

In other words, data management is defining and laying the foundation along with a set of ground rules for the data. In essence data management is a critical piece in a company’s data strategy.
Data management includes [but not limited to] a combination of the following elements…

Data Governance - wherein identifying the data owners for various business functions / areas of an enterprise.
Data Standards – for example, naming conventions – “LIMITED” will always be standardized to “LTD”; “CORPORATION” will always be standardized to “CORP.”
Data Architecture / Model – process of identifying and defining the data requirements, flow of data across various functional areas and eventually how the data will be stored.
Data Quality Management – profiling the data, cleansing the data, ensuring data integrity
Master Data Management – define core processes and procedures to effectively manage “non-transactional” data, i.e. data is that mostly static in nature or does not change quite often.

Effective data management can only add value to any of your enterprise’s existing processes!!

Ashvinder Rana, Data Migration Lead, Utopia, Inc.

Data Doughnut Hole

2010-07-21T06:39:00.002-05:00

A doughnut hole is an odd expression to describe a gap or a discontinuity, most often of a programmatic kind. Most recently the expression’s been in the news around the so-called Medicare doughnut hole. This occurs when prescription drug coverage for patient reimbursements ends when payments reach a threshold level. Thereafter the patient is responsible for the full cost of prescription medication until another threshold is reached, when Medicare starts to kick back in again.

Efforts and activity around data cleansing and getting data fit for corporate purposes can also reach a doughnut hole. This can happen during projects when data starts getting cleansed and users begin to see results based upon the transformed data. Euphoria over seeing reports and analytics that are far better than previously available (if at all) sometimes results in the view: “OK, we’ve gone far enough, we’re clean enough, let’s stop and start using this data, there’s gold here!”

Other times it’s a more formal or conscious decision to cut back on cleansing, as when users decide they’re comfortable with a level of data maturity that may be less than 100%. Such a “suboptimal” condition might be all that’s required to run the business effectively. Take for example vendor fields: perhaps not all vendor fields are equally critical and those that are not do not need to be filled in 100%.

But as BI becomes more pervasive and business continues to value accurate reporting, data can cross the doughnut hole when demand for better and more complete reporting picks up. This fuels the completion of data quality to even higher standards to support the continually evolving requirements of the business. Coming out on the other side of the doughnut hole is sweet desserts for those in the business of refining data for consumption!

Tony Stypinski, Client Partner-West, Utopia, Inc.

Construction of SAP Vendor data

2010-07-20T06:45:00.002-05:00

I'm currently working on an SAP Vendor conversion project. The high-level goals are to extract the SAP Vendors, cleanse the vendor data, identify duplicates, append enriched data, and then apply custom rules, which will be used to update the SAP Vendor tables.

At this client location, we are seated in an area that overlooks the construction of a new office building and they are just now putting in the basement floors. Every day I get to watch their progress.

So you think I'm going to blog about how building a new office complex is like building data quality of vendor data within an SAP R/3 application? Yes, that would be a good analogy, but nope...not going there today!!

Yes, we are both building things, but I'm thankful I'm building vendor data quality and not an office complex. While I'm sure that the construction workers would argue with me, I much prefer to be inside today in a nice air conditioned office while temperatures outside exceed 90 degrees today. The construction workers are out there every day doing back breaking work while I'm seated in a nice comfortable office chair -- of course I have to get up every hour or so to stretch my legs and back. I do watch in amazement while the crane operators climb up and down 13 flights of stairs on a daily basis -- and I feel a little sheepish that I was upset earlier in the week when one flight of escalator steps at the parking garage was out of operation!

I simply enjoy what I do -- and yes, it is much less hazardous to build things virtually :)

Bottom-line, I hope you enjoy whatever it is that you do!!

Chuck Schardong, Senior Consultant, Utopia, Inc.

Information Silos: “I didn’t know! They never told me.”

2010-07-19T07:02:00.003-05:00

It never fails to amaze me as to how information silos naturally develop in an organization. I’m not talking the physical silos where data is squirreled away in a repository where access is restricted, but those departmental boundaries where information flows seem to ping off the org chart walls like grains of corn against concrete. This phenomenon is never more apparent as when you conduct an EDLM (Enterprise Data Lifecycle Management) maturity assessment. During the assessment a cross section of business and IT managers are interviewed and they are each asked the same comprehensive set of questions. They get to answer from strongly disagree to strongly agree. But before the interviews begin the assessment team performs an inventory of key systems, applications, practice, and process guides to set the baseline of the data management infrastructure. Collecting the truth data if you will.

Where the assessment interviews get interesting is when the respondent’s perceptions not only disagree with each other, but when they disagree with the data management baseline. For example, a typical maturity statement is, “our organization has access to a data profiling solution.” The assessment team knows the answer is yes because they inventoried it, and even spoke to the IT team that supports the install. And yet in a group of ten to twenty respondents the answers can be all over the map, from strongly disagree to strongly agree. As an interviewer on the assessment team I want to answer for them, or at least correct their answer, but I can’t. I want to educate them and explain where the profiling solution is, who to talk to get training, but as part of the assessment we are recording honest perceptions. Remember perceptions guide behavior, and if a person perceives something does not exist they won’t pursue it. Those same perceptions impact an organization’s usage and treatment of the data. They can be as hard and impermeable as concrete walls.

It can be confounding to an assessor when they ask the interviewee to respond to “our organization regularly conducts data profile checks on key data objects,” and the group of respondents in the marketing department on the 4th floor generally answer “agree” and the sales operations staff on the 3rd floor generally replies “strongly disagree” or “don’t know.” They both use the same data systems, but the one group has a more positive attitude towards the data than the other group. It can be even more fascinating when there is no floor in the building or departmental boundary separating them. How can this be? Again, remember, the negative perceptions of the one group will impact the amount they trust the data, and hence use it.

The answer to the dilemma is communication. As simple as it may be, and hence quickly discounted, a regular regimen to communicate, in this case data quality activities can immediately elevate organizational performance. If the staff is just told where to go on the corporate Intranet to view the Data Quality Dashboard their awareness would increase and that first step starts them on the road to more effective usage of a key corporate asset: data. The fact is some groups, functions, and departments are so heads down in trying to do their work that they forget to grease the operations gears with information. I’ve heard the statement, “but we told them that!” I then follow up with “but did you remind them?” The truth is in the scoring. The groups with the better regular communication have perceptions that match the baseline, and are therefore are more effective at using organizational resources, like data.

Frank Dravis, Solutions Consultant, Utopia, Inc.

Data Governance: The New WTF? Where’s the Foundation

2010-07-16T06:38:00.002-05:00

Gotcha! In our world, our translation of the classic – “WTF” is Where’s the Foundation. As in the data foundation.

Consider that 80% of a business process is master data; the remainder being either conditional or transactional data. And if business processes are critical to your companies’ success, execution of business objectives and your reporting integrity from which you make all decisions against…. Well, I’d say “WTF” when things don’t go as well as they should --- or could.

Your approach should be to establish your data foundation – which will help you leverage data as an asset for the lifecycle; from its point of creation through archival.

WTF – Where’s the Foundation ;)

John Ferraioli, SVP Data Lifecycle Management, Utopia, Inc.

Data Governance - It’s Your Business Processes, Stupid…

2010-07-15T06:37:00.002-05:00

Data governance is 80% people and process and only 20% on technology.

Don’t get me wrong, I am a huge proponent of leveraging technology to enforce and sustain data integrity and business rule compliance – but technology (e.g. data profiling, standardization, enrichment, duplication, MDM, and data integration) alone won’t get you where you need to go. You have to change long-term behavior if you are going to sustain a data governance program and the place to do that is within your business processes.

Data governance and MDM are organization-wide programs. What we are attempting to do is instill best practices with regard to data maintenance and data management optimized for the benefit of the entire organization.

Data governance involves addressing the framework for data-related decision making while master data management involves making the decisions in the context of the data governance framework. All of this entails cutting across the various organizational boundaries that exist within your company today, whether they be functional, line of business, or geographical. Sound business processes cross these boundaries (often referred to as data silos) and your data governance and MDM processes must do the same.

Integrating business processes can be difficult, especially if you get mired in discussions related to efficiency and effectiveness (after all your company is making money today without the level of streamlining and integration you are likely proposing…right?). The key is optimizing your business processes for the enterprise with regard to quality and productivity.

Show me bad data and I will show you existing business processes well-defined, but poorly executed or poorly designed but consistently executed. Want to institutionalize data governance? You have to address the processes that support the data.

Jim Whyte, Director of Solutions Consulting, Utopia, Inc.

What Constitutes Master Data?

2010-07-14T06:55:00.002-05:00

In my experience working with master data, I have come across various definitions for it. Every client has a unique definition of what constitutes master data. A couple of versions that are popular are…

a) only reference data along with true “global attributes” should be considered master data

b) any data attributes which contribute to defining a product/customer/vendor should be considered master data.

Both the definitions have found resonance with various companies. But a lot of companies often struggle to adhere to either of the definitions. Often the focus of the data definition team/data architect is distracted from defining what attributes define master data to what attributes should be part of the master data system so that the processing of the data operations (CRUD) becomes easy.

Have you ever had a pizza with too many toppings? Theoretically, a pizza crust can hold more than 10 toppings. But after four toppings, the possibility of the pizza crust not holding up to the weight increases drastically. (Don’t believe me…try adding 10 toppings online at Pizza Hut or Dominos, and you will see a warning popup.)

Similarly, adding all the attributes that simplify the overall master data management (MDM) processes is a win-win for the MDM development team and the business, but my experience shows that this falls apart very quickly just like the pizza crust. The governance is unwieldy, the workflows are complicated, and the points of failure abound.

In the case of master data definition, size matters. The smaller, the better.

So, I believe that in order to have a Master Data Management System that is stable, relevant, and useful, data architects have to make every effort and insist on sticking to the true definition of master data.

Venkat Chaganti, Senior Consultant, Utopia, Inc.

Data Governance – Separation of Power

2010-07-13T06:51:00.002-05:00

A lot is written about data governance; what it entails, who should be involved, and how it should be administered. For many who are trying to initiate data governance in their organization this can be a murky and confusing topic. I want to simplify it.

You need data governance in place in order to design, build, implement and more importantly, sustain, any type of data related initiative whether it be MDM, data quality, data migration, reporting or archiving. All these efforts require business requirements to be identified and defined, all require decisions on what to do and when. All will require issues that will need to be resolved, compromises made, etc. Without solid data governance in place, these efforts will result in fire fights, “emergency” decisions being made and choices selected for low cost of entry over long term cost of ownership.

So what is data governance? I like the definition CIO Magazine has used; “Management is the decisions you make, governance is the structure for making them.”

Data governance is all about the people and processes required to make decisions; who can make them, when, and how. It is about establishing and defining (read documenting) the roles and responsibilities for your data governance:

Steering Team - who set the business priorities, provide authority and funding, and monitor results
Data Trustees – who approve data processes, authorize data standards, assign accountability, approve changes, and monitor data quality
Business Data Stewards – who design data processes, establish data quality levels and resolve issues
Data Maintainers – who create, maintain, search, retrieve and archive data records
Shared Services – who provide monitoring and oversight of data processes, issue escalation, change management and communication, provide enterprise common services (e.g. data quality, monitoring and profiling) training, establish best practices and facilitate data council meetings.

It is about integrating your data governance organization into your Software Development Lifecycle (SDLC), to ensure standards are enforced in new IT initiatives and that existing master data is opportunistically leveraged.

Knowing who to contact, who to ask, and who can approve is key to ensuring that your critical data (master, reference and transaction data) is being properly weighed and considered for the benefit of the entire organization, not one project or function silo and that is the key to sustainability, repeatability and operability which should be the primary objectives of any data governance program.

What do you think?

Jim Whyte, Director of Solutions Consulting, Utopia, Inc.