Lately, there's been lots of buzz around the logical data warehouse (LDW). In fact, Gartner is hearing LDW mentions as part of data warehouse (DW) inquiries almost 20% of the time and considers it a "megatrend." The definition usually includes some use of data virtualization or data federation capabilities to complement
One of the benefits of running an online data quality and data governance community is that over the course of many interviews, you start to see common threads and patterns emerging in the way practitioners create success in their data-driven programs. Data governance is a relatively new discipline, so it’s
In the past few weeks I have presented training sessions on data governance, master data management, data quality and analytics at three different venues. At each one of these events, during one of the breaks a variety of people in my course noted that the technical concepts of implementing programs
In my previous post, I outlined the main components needed for a phased approach to MDM. Now, let's talk about some of the other issues around approaching MDM: data governance and the move to enterprise MDM. Where does governance come in? Throughout your MDM program, it's important that deep expertise
In my last post I introduced the term “behavior architecture,” and this time I would like to explore what that concept means. One approach is to start with the basics: given a business process with a set of decision points and a number of participants, the behavior architecture is the
For decades, data quality experts have been telling us poor quality is bad for our data, bad for our decisions, bad for our business and just plain all around bad, bad, bad – did I already mention it’s bad? So why does poor data quality continue to exist and persist?
Instituting an analytics program in which actionable insight is delivered to a business consumer will be successful if those consumers are aware of what they need to do to improve their processes and reap the benefits. As we have explored over the past few posts, success in the use of
The data quality and data governance community has a somewhat disconcerting habit to want to append the word “quality” to every phrase that has the word “data” in it. So it is no surprise that the growing use of the phrase “big data” has been duly followed by claims of
Each year, I'm excited to see the awards nominations for Data Steward of the Year come in. It's not just because we enjoy seeing the program grow each year (which is true, based on the number of nominations we receive). It's also because of the variety of the nominations –
In my last post, we started to look at some of the issues with the concept of “big data governance,” especially when a large part of governance is intended to prevent the introduction of errors into data sets. Many big data analytics applications focus on the intake of numerous varied
