"One size doesn’t fit all" is a well-known refrain in the data governance community. Typically, this well-worn but evergreen adage is applied when discussing organizational structures. Two companies in the same industry, of like size and means, with similar objectives can take drastically different approaches for instantiating data governance within
In my previous post, I suggested that if we were to better articulate how master data management (MDM) is typically used, we could develop the components of solution templates that can speed the integration process. In this post, we’ll start to look at some common ways that the capabilities that
If you work for a bank or utilities organisation, then gaining a foothold for data quality is largely focused on proving you can increase the profits of an organisation. In the public sector of course there are different priorities so how can you demonstrate the value of your data quality
On the old dataroundtable.com site, I railed a few times against the perils and general stupidity of dual data entry. At a high level, barring some type of cataclysm, there's absolutely no reason for anyone to be typing the same data into two disparate systems. And that goes double for anyone
Big data seems like a daunting challenge because, as data management professionals, we have been taught by experts and learned from experience that we always have to dive deep into data in order to discover meaningful business insights, solve business problems, and support daily business operations. However, it’s possible to
A few years ago, I was presenting a morning course on master data management in which I shared some thoughts about some of the barriers to success in transitioning the use of a developed master data management index and repository into production systems. During the coffee break, an attendee mentioned
THIS IS HARD TO DO! In our agile world we seem to never get the data model completed until two weeks after we are in production, and every project plan wants to waterfall the completion of this deliverable. I think it may be due to the rapid way we gather and refine requirements. For
One of the most common questions I get asked by our members on Data Quality Pro is, “Can you do more articles on data quality dimensions?” Part of the reason for this request is when people first start getting involved with data quality, they invariably buy data quality books and
In my three previous posts, I pondered whether unlimited data could limit data silos (i.e., whether offering users the enterprise data management equivalent of unlimited data streaming could curb their enthusiasm for creating data silos), or if streaming past the limits of unlimited data could create more data silos if users became frustrated with the practical
You’ve read the research reports and seen the statistics. You’ve attended the conferences and heard the case studies. You’ve read the online articles and kept up with expert opinions. Your organization has even done a few big data sandbox projects – some successful, some not. Yet the jury is still
