Entries Tagged 'data quality' ↓

I found this video on YouTube and thought it was great. It is embedded below, but if that doesn’t work for you here is the URL: http://youtube.com/watch?v=TbzQvswrOTw

Feel free to leave thoughts and comments on this below!

In my post ‘8 Reasons why Data Governance fails‘, Carol S asked, “Does anyone have experience in developing a data governance strategy?”. I haven’t talked with her about this yet, but I’d like to recommend the following high-level path to launching Data Governance in an organization. This is very high level (read: quick & dirty), so you’ll need to take time and break these steps down into discrete tasks.

1. Start a Data Initiative to fix a few specific problems
Before the words ‘data governance’ are ever uttered, start an initiative to fix a few problems that span the enterprise. Make sure these are problems that bother people so that you’ll get buy-in. As part of this initiative, build a business team that will work on the problems (they’ll later become your Data Stewardship Council).
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This post was written by Vincent McBurney, blogger at ITToolbox and consultant for BearingPoint.

A Carnival is a monthly publication of links to interesting articles on a topic - in this case data quality. It travels to different blog homes each month. We have some great DQ blogs for the first issue and at the bottom of the post we announce the host of issue #2.

Here are the DQ posts for issue #1. Please visit the blogs highlighted and leave comments to show your support.

An Amazon Post that’s not about the Kindle

Daragh O’Brien is busy in Data Quality. Why are there so many DQ gurus in Ireland? He is part of the The International Association for Information and Data Quality (IAIDQ) and writes the entertaining IQ Trainwrecks blog. He has submitted a great article from there called oops Amazon did it again… about how a search for a Spiderman wristwatch on Amazon returns something unexpected. Say hello to your friendly neighborhood Spiderman. Continue reading →

Dataflux is sponsoring a free Webcast today titled “ZERO TO FIVE: A Stepwise Progression for MDM and Data Governance”. Jill Dyché and Evan Levy will discuss:

• Why MDM needs to be deconstructed into discrete functional areas

• Why understanding business requirements means understanding functional requirements

• Why successful MDM depends on a solid data governance framework

The webcast is Today at 2PM EST. To register, click here.

This is an un-sponsored post.

Whether your are new to the discipline of Data Quality or an old pro, you’ll likely have heard, over-and-over, that poor data quality leads to an overall poor value of the data and ultimately poor decision making. This is certainly a good point and I have no argument with it. I would like to point out, however, that there have been times when poor data quality, mistakes, and little quality assurance has led to quite valuable products. Submitted solely for your entertainment, here are some examples of the high value of poor quality.
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Check out the article “The Death of Dirty Data” published in The Canadian Underwriter (of all places).

It is really well written and focuses on data quality, data governance, and data management.

“Data is the foundation of practically everything that goes on in today’s organizations, and so managing the quality of data is reaching “business-critical” status.”

A very interesting article was written over at the Data Strategy Journal recently - in it they attempted to simply define Data Governance. Easy task right? Not so much. Their method for getting at some definitions was to interview consultants, practitioners, and vendors and asking them to simply “define data governance”. I like the approach of asking people in different positions to define something - this almost certainly ensures diversity since their perspectives are very different. Its a bit frightening to see the many different types of answers, but I certainly understand them since the discipline is still emerging.
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The last session that I wanted to write about was titled, “A Business Value-Driven Approach to Data Quality” and was presented by Richard Trapp from Avaya. For those of you don’t know of Avaya (I suspect most of you do, as you probably have one of their phones sitting right next to you), they were spun off from Lucent and are now a leading business communications technology provider. Richard started the DQ program at Avaya and went about doing it in a very unique way — every effort he makes is focused on the trackable dollar value it brings back to the business.
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I attended an all day session by Danette McGilvray titled “Ten Steps to Quality Data and Trusted Information” at the IAIDQ Conference. The session provided a really nice framework that you can follow, step-by-step, to implement a strong Data Quality Program in your organization. Danette is the President and Principal of Granite Falls Consulting, Inc. a firm “specializing in data quality management to support key business processes around customer satisfaction, decision support, supply chain management, and operational excellence.” Throughout the presentation it became very obvious that Danette has “been there and done that” many times over. Her examples of successes and failures on projects she has worked on throughout her career really helped crystallize why her ten steps are effective.
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On the first day at the Information and Data Quality Conference, I attended the session, “Using Data Profiling for Proactive Data Quality Improvement“. That session was for the first half of the day, so for the second half I attended the Len Silverston session titled “Power and Politics in Data Quality Improvement Efforts“. This was a great session that had very little to do with data theory and data management, and a lot to do with interoffice dynamics. The session opened up with the question, “What is the biggest problem in data quality today?” Many good answers were tossed out by the attendees, but I think the answer that Len submitted trumped them all… read on for the answer.
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