The Data Roundtable

On her popular blog, my friend Jill Dyché recently wrote about Big Data Governance. From the post:

Often clients explain that they need to treat transaction data differently than they need to treat, say, customer master data. Fewer business rules, more history, that kind of thing.  That’s when we start the work of classifying different…

In my last post I outlined my reasons for sitting on the sidelines regarding data quality professional certification.  But I’m throwing my full support behind two programs, those of the IAIDQ and eLearningCurve.  (Note:  I also clarified my close ties to both in that post.  Here’s the link).

Loraine Lawson recently blogged about the importance of Finding Your Data Pain Points in order to build the business case for convincing your organization about the need for a data governance program by communicating the business value of governing data.

Even as we limit ourselves to considering two types of events that can lead to an inaccuracy or inconsistency in your data set, the ability to trigger a verification of accuracy when either type of event takes place seems way beyond the reach of most organizations to do on a regular basis. Nonetheless, the fact that real-world things change in spite of your attempt to model and persist information about them does make the concept of “data entropy” an issue that requires some consideration, and I promised that we would look at “feasibility” in dealing with the challenge.

NOT ME!  NEVER ME!  I refuse to get old!  For those of you who know me, you know that maturity is definitely not one of my strong points –  especially when there is liquor involved!  But what about when you are faced with someone else’s design and implementation?  Let’s say in a data warehousing or master data environment.  This could be a new customer or a new contract assignment, and you really don’t want to make them mad at you on day one.  You may encounter things like:

For some time, I’ve been conflicted about data quality professional certification and/or certificate programs.  The two I know best come from IAIDQ and eLearningcurve.  In the interest of full disclosure, I am an IAIDQ cofounder, and I continue to assist it in many ways, mostly small.  I’ve provided my inputs into certification content…

I’ve written before on this site about how Amazon.com keeps impeccable data on its customers. Today, I’d like to discuss how one of the company’s products – Amazon Web Services (AWS) – allows companies to do truly remarkable things with extremely large data sets. (Of course, tools like Hadoop also facilitate the analysis of Big Data).

In which Jill turns to eastern philosophy before the next office smackdown.

I always like predictions. Every January I write a blog post about predictions for the coming year. Analysts and magazine columnists also routinely ask me what’s next. In past years I’ve predicted the increasing importance of data governance, the rise of cloud computing, the changing role of the executive sponsor, and the widening abyss between the business and IT. It’s sort of like being an armchair quarterback. And it’s all so, well…predictable.