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October 29, 2007

Live from TDWI Data Governance Summit


Notes from the Keynote at TDWI for 10/29

Great talk on data quality by Akady Maydanchic. The top line takeaways were:

- Data quality is not getting better it is getting worse. While there has been tremendous progress specifically in the area of customer data, all other areas of data have actually gotten worse. That is related to the next point, that speed of data movement is bad for
- Speed is antithetical to data quality. What has happened over the years is that while systems have become faster, that has left less time to fix DQ problems. When data used to get moved once a month, there was plenty of time to go back and fix data quality problems before they propagated to downstream systems. Then movement time shrunk to a week, and there was less time to fix problems but they could still get fixed. In fact, lots of DQ problems are self correcting if there is enough time for users to go back and fix their errors. But has data movement has gone real time, data is moved to downstream systems before it can be corrected and if it gets corrected in a down stream system and not an upstream system, then you end up with data inconsistency problems.
- Tools are lacking. The DQ tools are still immature and have not progressed much in the past 5 years. The tools available today are too focused on single column values. The big issue is in tracking complex business rules and data relationships between tables and between systems. The tools donít do a good job of dealing with complex rules. Tools also need to provide a better infrastructure for doing quality scorecards. There needs to be an environment that enables performing a data quality assessment then allow ongoing scorecarding to see progress


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Posted by Todd Goldman at 9:30 AM | Comments (0)

October 19, 2007

More responses to my October 12 blog

Bob writes:
Isn't MDM just another form of data consolidation? What makes it so radically different that it warrants it's own moniker?

My response:

Bob, great question. I would say the difference between a data consolidation and MDM is that in a data consolidation, you consolidate the original data sources into a single data source and eliminate the original sources. This means that you only have to deal with that consolidation once.

MDM keeps all the original sources and merges the data values according to a set of business rules that you define for survivorship. However, you don't eliminate the original data sources so as new data is added to those sources, those new values get merged, purged and matched into the master data system. The master data, sometimes also refered to as reference data is then used by downstream applications as the "golden master".

The idea of MDM is to create common master data that is used by other applications so you have consistent reference data that is used by all applications. The idea of a data consolidation is usually tied to an application consolidation where you are trying to eliminate excess or redundant applications and the result is that you also have to consolidate the data.

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Posted by Todd Goldman at 11:45 PM | Comments (2)

Response to comments on my last blog


As this blogging software doesn't make it easy to respond to all your comments directly at the comment, I will do it in a new blog... so here goes:

Craig McGill writes:
"but perhaps the reason for including MDM at the start of all the articles was for search engines finding the article (and the analyst) as well as part of the SEO strategy."

My response:

Craig, only the last two blogs have been about MDM and I wrote about it because it is a topic that interests me and I had just come back from the Gartner MDM conference... wow, how cynical some of us have become. :(

Craig McGill writes:
As for the other point of understanding the landscape, that's a fair point, but any MDM company worth the title (disclaimer: I do the PR for VisionWare PLC) is going to have staff trained up to find out where you are, what you want from the MDM and what you don't want (as important as the first two in my opinion).


My response:
The issue I am writing about isn't understanding "what you want and don't want" from MDM. The challenge, especially when you move out of the CDI space and into more complex data environments like financial reference masters, counterparty data masters or just product masters for manufacturing firms, the level of complexity moves into dealing with thousands of data attributes. Furthermore, the level of comlexity of the data relationships that must be rationalized go beyond simple one to one matches and you end up dealing with complext transformations and multinested case statements that are needed to handle the merge, perge and match process.

So while I can't speak for what is going on over on your side of the pond in the UK, I can say that over here in the U.S. the companies I am seeing are really struggling with where to start... hence my recommendation.

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Posted by Todd Goldman at 11:15 PM | Comments (1)

October 12, 2007

Can we get some good MDM advice! Please?

I just finished reading an article by a leading industry analyst on Master Data Management and I am left begging for some good advice. The generic "blah, blah, blah," that is going on has got to stop or else this industry will certainly collapse under the hype that this IT analysis company so often complains about, and in this case is only too glad to participate.

In a nutshell, this person talks about the cornerstones for MDM and they are:
- Vision
- Strategy
- Governance - Organization
- Processes
- Technology Infrastructure
- Metrics

the difference between what this person wrote and what I just wrote above was that they prefaced every term with "MDM", so these cornerstoes were actually MDM Vision, MDM Strategy and so on. Wow, thanks for the brilliant insight... not! (as my 12 year old, or Borat for that matter, would say).

Let's see what other prefixes I could put into that list and still have it work:

- Corporate (Corporate vision, corporate strategy, corporate governance ... yup it works!)
- IT
- Business
- Financial
- HR
- Data
- Web 2.0 (not perfect, but not too bad)

yes, they all work, more or less as well. Let's continue:

- X-ray (OK that just works for Vision)


I think you get the point. This advice is so generic as to not be usefull at all. So rather than just complain, I will give you my more practical advice for how to start.

First, start by understanding your existing data landscape. You can't get where you are going if you don't know where you are. Since MDM is all about improving data consistency, your presumption is that you have data inconsistency problems or you wouldn't be considering MDM in the first place. So figure out what your current environment looks like.

More specifically, start by analyzing the data sources that you are planning on using to populate the MDM hub. Understand how attributes (columns) align both within and across your data sources. What are the commonalities, what are the differences and what are the conflicts. Do the same for each row of data, the cells and the data structures.

You can do this by writing SQL and using TOAD or by using any of a number of tools for data analysis available in the market (full disclosure... my company Exeros sells this kind of a tool). Make sure however that you can do analysis both within data sources and between data sources. Remember you are trying to find sources of redundancy and inconsistency across your data sources so cross system analysis is critical. Note that you don't have to analyze all of your data sources, just take a look at the most critical ones, you know what they are, and use that to extrapolate. From there you can establish a baseline from which to work and then put a more realistic plan in place.

Once you have done this, you have the basis for determining the work involved in rationalizing your data sources, you will have raw data to calculate the ROI of an MDM project by quantifying the % of inconsistencies in your data and by estimating the value of reducing or eliminating those inconsistencies. And you will also get a better grip on what else you need to do before you really get started.

For lack of a better term, I would call this an MDM Readiness Assessment.

So go get an MDM health check... start with an MDM Readiness Assessment.

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Posted by Todd Goldman at 4:45 PM | Comments (3)

October 8, 2007

Reflections on the Gartner MDM Conference

When I logged in this morning, I originally was going to write about the Gartner Master Data Management conference that happened two weeks ago, but got sidetracked by the Business Objects acquisition.

However, I decided that this is still worth a quick entry.

My top takeways from the conference were:

1) End users are just learning how to spell MDM. The most common questions that came up were, "How do I justify the ROI of an MDM investment?" and "How do I get started?" Both questions reflect that the end user market is still in learning mode.

2) Presentations were still very high level. There were not a lot of precriptive presentations with suggestions on what to do and how to do it. There clearly is a need for more precise description of what exactly people need to do to get going. This is reflected as well by the questions that people where asking per my first point. If this market is going to make it, we all have to get off the high level blah blah blah and get specific.

3) Just because a vendor calls what they do MDM doesn't make it so. As a vendor, this one kills me. If you are going to get up on the dais and talk about how you help improve MDM deployments, don't just take the stuff you used to sell as data quality or ETL or EAI or whatever and just give your old pitch and add MDM at the end of every third sentence. There are too many vendors who think that good marketing is just taking their existing stuff, adding the latest buzzword as a suffix or prefix and then trying to sell it. NOTE TO VENDORS - Your prospects are smarter than that!

By the way, there were also vendors who do provide real valued added MDM products as well so I don't want to lump everyone into the same bucket. But as for the charletons selling ice to Eskimos, remember that you can only do that once.

4) There is definitely a need out there for solutions that enable data consistency. Distributed computing has driven distribution of data which has driven inconsistency in data which has driven bad decision making, poor customer service, slow time to market for data intensive projects not to mention the difficulties of data governance in a distributed environment. End users are really suffering and there is definitely a big opportunity for those solution providers who can help improve data consistency for their clients.

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Posted by Todd Goldman at 12:00 PM | Comments (0)

Business Objects (BOBJ) gets acquired!


Well, I know it only seems that I blog after an acquisition, and I have to admit that I have got to get better about logging on more often to write, but wow, yet another acquisition in data management country.

SAP decides to enter the acquisition frenzy and purchase Business Objects for $6.8B. For those of you who don't watch this market that much, that is a huge deal for SAP. SAP has traditionally been a not invented here kind of place and has tended to build rather than buy. Most of their acquisitions are of small technology firms... so for them to belly up to the bar and buy BOBJ, that is like buying a dozen pints and downing them all.

Regardless, this continues to be exciting times in data management land with ongoing consolidation. The downside is that while the number of large suppliers is thining, there is little investment in the market from the VC community. It could be that all of the easy ideas have been implemented, but that is usually a good thing for startups since most big innovation in software has come from the start up community in recent years.

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Posted by Todd Goldman at 10:00 AM | Comments (0)