Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.
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
In my two 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
My previous post pondered whether unlimited data could limit data silos, which was inspired by an extended disruption in the internet service provided by my local cable company. This provided me the opportunity to see just how unlimited the unlimited data plan on my smartphone really was. Ironically, shortly after my
Some strong storms recently caused an extended disruption in the internet service provided by my local cable company, which gave me an opportunity to test the reliability of my smartphone's mobile broadband connectivity as well as see just how unlimited my unlimited data plan really is. After successfully passing both aspects of
I have previously blogged about sneezing to unleash the data quality ideavirus, but in this post I have a different kind of sneezing in mind and, unfortunately, in nose. The common cold is so-called because it’s the most common human infectious disease. Despite the apparent irony of my traditional spring and summer
This is the time of year when we like to make predictions about the upcoming year. Although I am optimistic about the potential of predictive analytics in the era of big data, I am also realistic about the nature of predictability regardless of how much data is used. For example, in
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?
I have previously compared data visualizations to the magic mirrors of business intended to reflect what you need to see, such as true business insights, but which, because of how our eyes process data, may just be reflecting back your own image of what you want your data to show
In my previous post, I used a game show metaphor for one aspect of metadata management, namely making sure table definitions are not ambiguously labeled. In this post, I will use name tags as a metaphor to discuss an important intersection of metadata management and master data management (MDM), an
Although its simplest definition is “data about data,” metadata can be better thought of as a label that provides a definition, description or context for data. Common examples include relational table definitions and flat file layouts. More detailed examples include conceptual and logical data models. Among its other possible uses,
