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 his book Proofiness: How You’re Being Fooled by the Numbers, Charles Seife coined the term disestimation, defining it as “the act of taking a number too literally, understating or ignoring the uncertainties that surround it. Disestimation imbues a number with more precision that it deserves, dressing a measurement up as absolute
In my previous post Sisyphus didn’t need a fitness tracker, I recommended that you only collect, measure and analyze big data if it helps you make a better decision or change your actions. Unfortunately, it’s difficult to know ahead of time which data will meet that criteria. We often, therefore, collect, measure and analyze
In his pithy style, Seth Godin’s recent blog post Analytics without action said more in 32 words than most posts say in 320 words or most white papers say in 3200 words. (For those counting along, my opening sentence alone used 32 words). Godin’s blog post, in its entirety, stated: “Don’t measure
My previous post explained how confirmation bias can prevent you from behaving like the natural data scientist you like to imagine you are by driving your decision making toward data that confirms your existing beliefs. This post tells the story of another cognitive bias that works against data science. Consider the following scenario: Company-wide
Nowadays we hear a lot about how important it is that we are data-driven in our decision-making. We also hear a lot of criticism aimed at those that are driven more by intuition than data. Like most things in life, however, there’s a big difference between theory and practice. It’s
Many people, myself included, occasionally complain about how noisy big data has made our world. While it is true that big data does broadcast more signal, not just more noise, we are not always able to tell the difference. Sometimes what sounds like meaningless background static is actually a big insight. Other times
In my last three posts on data ethics, I explored a few of the ethical dilemmas in our data-driven world. From examining the ethical practices of free internet service providers to the problem of high-frequency trading, I’ve come to realize the depth and complexity of these issues. Anyone who's aware of these
Imagine if your ability to feed your family depended upon how fast you could run. Imagine the aisles of your grocery store as lanes on a running track. If you can outrun your fellow shoppers, grab food off the shelves and race through the checkout at the finish line, then
In my previous post, I examined ethics in a data-driven world with an example of how Facebook experiments on its users. Acknowledging the conundrum facing users of free services like Facebook, Phil Simon commented that “users and customers aren’t the same thing. Maybe users are there to be, you know... used.” What about when a
I have previously blogged about how the dark side of our mood skews the sentiment analysis of customer feedback negatively since we usually only provide feedback when we have a negative experience with a product or service. Reading only negative reviews from its customers could make a company sad, but could reading only
