June 2007 – Informatica Perspectives - The Data Integration Blog

Monthly Archives: June 2007

Information Quality & Management Transformation

by Larry English on June 28, 2007 – 7:59 am

I recently received an email from one of my early clients. After having worked in four different companies in four different industries, she came to a sad conclusion, writing:

“The thing that they all have in common is a desire to cut corners and deal with quality later. It takes a lot of energy to be the information quality cheerleader, and I find it discouraging and overwhelming at times. Keep writing your articles and books to encourage all the people like me who are dealing with these issues every day.” P. G.

The discovery that P. G. has experienced is, unfortunately, the norm—not the exception. There are two critical elements in this experience.

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Getting Down To Business with People and Policies

by Rick Sherman on June 25, 2007 – 7:29 am

In the last several posts we discussed how people, policies and products are essential to successful Master Data Management (MDM) and Customer Data Integration (CDI) programs.

For those of us in high tech, it’s easy to fall into the trap of concentrating too hard on the products, technology and architectural aspects of solutions. I’ll admit I’m a bit of a nerd and many people in IT feel comfortable with technology too. It also goes beyond IT; in today’s society people often assume technology is going to solve their problems.

But the problem with our comfort level is that often the critical success factors of a solution lie with people and policies. And often there’s a little bit of politics thrown in to keep things interesting. Very interesting…

So, if you’re going to launch and sustain MDM or CDI programs, you’ve got to put the people and the policies on your front burner. Two things to focus on:

On the business side a data governance program is essential
And on the IT side, an integration competency center (ICC) helps to ensure successful implementation of the data governance program.

Let’s start with data governance.

I see it with my clients and you surely see it in your business: you’re striving to better use data to improve the top line by increasing revenues, and improve the bottom line by reducing costs. Add in compliance and privacy drivers and there is a compelling business case for enterprises to manage data as a corporate asset.

Many companies are motivated to initiate and fund programs to manage data, but is the commitment really there? And even if the companies have the commitment, do they know what it takes to succeed?

I wrote about this recently in the searchDataManagement.com article “Why data governance projects fail.” In the article I referenced a Gartner study that predicted that “by 2008, less than 10% of organizations will succeed in their first attempts at data governance.”

Why will so few succeed? Commitment. Not just to get the project off the ground, but more importantly, to sustain it on an ongoing basis.

The lack of commitment is highlighted by the fact that the following two bullets (which you’ve likely seen in white papers and marketing PowerPoint slides) are nearly always oversimplified:

Executive sponsorship
Business and IT involvement

In next week’s blog post, “The Two Titanic Data Governance Mistakes,” I’ll explain what I mean by this, and go into detail on these two bullets. In the meantime, feel free to comment to ask questions and share your experiences.

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The Measured Value of “Re-Use”

by Don Tirsell on June 22, 2007 – 11:36 am

One of the best ways an organization can accelerate integration projects and recoup investment in infrastructure, people and implementation costs is through reuse of assets produced in previous integration projects. In my experience, “Re-Use” is often touted by vendors in the data integration marketplace but very little information is truly published in this area. Why is that the case?

For one reason it is an “after the fact” measurement attainable only where technology is used over time to deliver on multiple projects. It also means different things to different people. “Re-Use” in one technology context may be simplistic copying of objects within a design interface, saving time and effort but only a micro-scale. “Re-Use” in another technology context may mean “complete project section” reuse where surrounding environmental ties of a project implementation are removed and large blocks of implementation are repurposed across efforts. The latter is a more powerful concept, truly leveraging large amounts of previous work and making change faster, and more accurate. SOA is certainly driving more examination of this topic as well, some positive, some negative. Read More »

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Alice in “Qualityland”

by Neil Gow on June 22, 2007 – 6:20 am

Alice: Would you tell me, please, which way I ought to go from here?
The Cheshire Cat: That depends a good deal on where you want to get to
Alice: I don’t much care where.
The Cheshire Cat: Then it doesn’t much matter which way you go
– Lewis Carroll, Alice’s Adventures in Wonderland

When confronted with the problem of how to address their data quality issues many organisations are faced with a similar dilemma to that which confronted Alice during her travels in Wonderland; “I know that I need to do something, but I don’t know where to start”. Knowing where to start and, equally importantly, the size of the problem as well as where an organisation needs to go are critical factors in ensuring that their data quality journey takes them where they need to be at the price they are prepared to pay.

When planning their “journey” organisations need to address the issue of data quality holistically by considering each of the three DQ pillars in turn; firstly “People”, then “Ideas” and finally “Technology”. Many DQ initiatives have failed as the primary focus has been on delivering a technical solution. However without the right framework in place and operated by the right people this approach will never deliver the results that organisations need. Time and time again within the IT industry it has been proved that the pure application of technology will never solve business issues, as technology in itself will never win the “war”, it is always the right people with the right ideas who use the technology in the right way.
Read More »

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Why Enterprise Data Warehouses Have not Been Master Data Management Solutions

by Rick Sherman on June 18, 2007 – 2:56 pm

In last week’s post Master Data Management (MDM) – Going Where the EDW has Gone Before, I discussed the idea that Enterprise Data Warehouses (EDW) have long been used as default MDM solutions.

In this post I will discuss two barriers that have inhibited it from working, and what I think does work.

Barrier One: Multiple “Single Versions of the Truth”

First, the EDW supports “downstream” analytical application and business intelligence but not “upstream” data sources such as Enterprise Resource Planning (ERP) systems. This is not an inhibitor to supporting BI and reporting. But it does become a problem when it’s expected to support operational MDM, i.e. creating a master list to support ERP systems. The underlying assumption for most IT groups is that the ERP systems manage their own data, including master data, since the ERP system was where the data originated in the enterprise.

But a funny thing happened on the way to delivering the single version of the truth via ERP systems – people used more than one of them, and each one had its own version of master data. Maybe an enterprise had products from different ERP vendors or maybe they implemented the same ERP system multiple times in their enterprise specific to different business groups.

Regardless of why, the end result was that the ERP systems needed a MDM repository to accomplish an enterprise role. That has led ERP vendors to start building their own MDM solutions specific to their products, and for other software firms to build stand-alone solutions.

Barrier Two: No Silver Bullets

The second constraint on successfully implementing an MDM solution using an EDW has been the assumption that technology was going to solve the MDM problems without significantly including people and process (and of course politics) in the equation. Data governance, with the business taking ownership of the data and IT becoming its custodian, is needed to successfully design, deploy and sustain a MDM solution.

Business people need to help IT identify master data, define it and assist in determining what the resultant master list should be. Managing master data needs business commitment if it is to be treated as the corporate asset that it is. It is important to note that this inhibitor is not systemic to any technological approach, but rather needs to be included in whatever solution is developed.

What’s the Solution?

So, you may figure that since the approach using an EDW as the default has not worked flawlessly, it must be time to buy a MDM product, right?

Although buying versus building may be the most cost-effective approach, let’s step back before you sign that purchase order on a shiny new MDM product. You should determine what is needed before you buy. Failing to understand why past efforts have come up short is a sure recipe to failure. You might deploy a new MDM product, but still not achieve the business results you want. Even worse, you might be creating yet another data silo – moving you either further from a single version of the truth.

The two ingredients to success are an Enterprise Data Integration (EDI) platform and Enterprise Data Management (EDM).

An EDI platform addresses the first inhibitor discussed above by enabling two-way integration between your operational and analytical solutions using the appropriate technique, such as SOA, EAI, EII or ETL, that is needed to integrate the various enterprise applications and EDW. By implementing tactical stand-alone data-integration tools in the past we constrained each solution built. Typically, an EDW used ETL and the enterprise applications used EAI technology, which limited the ability of the analytical and operation systems to exchange and integrate their data

EDM, particularly data governance, is essential for any MDM solution to get started and stay in operation. No technology is going to eliminate the human element of these solutions.

Should you buy a MDM product? Maybe, but before you do make sure that an EDI platform and an EDM program are in place to ensure success.

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