MIT Information Quality Industry Symposium Day 1

I’m just settling in for Day 2 of the MIT IQIS 2009 and thought I’d throw out some thoughts for a couple of future posts I’m drafting. Here are the quick recaps from yesterday. 

Danette McGilvray - Ten Steps to Data and Trusted Information

A great primer on how to manage any Data  Quality project. Her 10 steps made a lot of sense. Not all of the methodology would be used for any given project, but still it worked for me. I also won her book, “Executing Data Quality Projects” in the drawing at the end of the class.

Bill Inmon - DW2.0 and Unstructured Data

After 10+ years in Data Warehousing I finally got to see Bill Inmon speak. Bill is the rockstar of the DW world. He’s regarded as the Father of Data Warehousing and treated as royalty at a conference like this. His new stuff was all about contextual ETL. Sounded interesting, but I believe there are others working on the same thing.

Keynote: Ronald Bechtold - Transforming the Army with High

Ronald is the Chief Data Officer at the Army. Cool title. Not what you’re picturing. He is a passionate CIO type who has a huge challenge. Definitely some words of wisdom in there. “Focus on solving problems,” rather than tools, technology or data. Good stuff!

Joe Bugajski - MDM Improves Information Quality to Deliver Value

Joe had some great examples where Data Quality actually led to increased revenue. Imagine that! Value from Data work. I think that’s what we’re all striving for. Joe is a big personality who speaks well, so this one was entertaining.

Mark Goloboy (that’s right, me) - CRM Data Quality for Sales and Marketing

After a bit of nerves, I found my groove and thought the presentation went really well. Some good questions about where my company started with Data Governance - it’s a very new ffocus or us. I also got to push back on some industry experts when asked why we weren’t focusing on MDM to start. Plus, Bill Inmon attended.

Martin Boyd - Product Data Quality Product from Silver Creek

More contextual analysis. Seemed to be done in a very smart way. The software was functional at big clients, and they had figured out how to solve some complex issues around improving poor product data. If they had the same thing for Customers, it would be a more interesting product. More development or a merger are needed here.