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.
Teller, the normally silent half of the magician duo Penn & Teller, revealed some of magic’s secrets in a Smithsonian Magazine article about how magicians manipulate the human mind. Given the big data-fueled potential of data science to manipulate our decision-making, we should listen to what Teller has to tell
Were you a mother who listened to classical music during your pregnancy, or a parent who played classical music in your newborn baby’s nursery because you heard it stimulates creativity and improves intelligence? If so, do you know where this “classical music makes you smarter” idea came from? In 1993, a
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?
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
Loraine Lawson recently used the Eight-Fold Path of Buddhism, in which practitioners are encouraged to pursue right views, intentions, speech, actions, livelihood, efforts, mindfulness and concentration, as inspiration for her blog post The Five-Fold Path for Ensuring Data = Information. The post offered five recommendations for ensuring that data is transformed into
Since tomorrow is How-long-has-it-been-since-you-used-this-data-ween, it’s time to review your organization’s preparedness for preventing the zombie data-pocalypse. (Please Note: This should not be confused with your organization’s preparedness for preventing the zombie apocalypse, for which check out the resources provided by the Centers for Disease Control and Prevention by ever-so-carefully clicking on
In the era of big data, Kenneth Cukier and Viktor Mayer-Schonberger noted in their book Big Data: A Revolution That Will Transform How We Live, Work, and Think, “we are in the midst of a great infrastructure project that in some ways rivals those of the past, from the Roman aqueducts
In physics, antimatter has the same mass, but opposite charge, of matter. Collisions between matter and antimatter lead to the annihilation of both, the end result of which is a release of energy available to do work. In this blog series, I will use antimatter as a metaphor for a
In physics, antimatter has the same mass, but opposite charge, of matter. Collisions between matter and antimatter lead to the annihilation of both, the end result of which is a release of energy available to do work. In this blog series, I will use antimatter as a metaphor for a
In physics, antimatter has the same mass, but opposite charge, of matter. Collisions between matter and antimatter lead to the annihilation of both, the end result of which is a release of energy available to do work. In this blog series, I will use antimatter as a metaphor for a
In physics, antimatter has the same mass, but opposite charge, of matter. Collisions between matter and antimatter lead to the annihilation of both, the end result of which is a release of energy available to do work. In this blog series, I will use antimatter as a metaphor for a
Are your data quality metrics making the important measurable instead of making the measurable important?
Jim Harris (@ocdqblog) explains the prediction he made about Small Data and VRM.
What's the Fifth Law of Data Quality? Jim Harris explains.
