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Blog Posts by Bill Franks

While statistical significance and other technical measures of performance are commonly assessed and reported, such measures aren’t enough for an analysis and associated presentation to be deemed a success. It is also necessary to determine and communicate how the results…

Everyone is familiar with the age-old adage that if you must explain a joke after you tell it, then the joke will be a flop. The same principle is true when you put data in front of a live audience,…
It is natural to get excited about the prospect of building and deploying an interesting and high impact new data science process. Unfortunately, you have to also put effort into some less exciting aspects of such an endeavor. One item…
The most important factor in determining if a given data science project will succeed or fail in a business environment is not the quality of the results. In an ideal world, that would be the case, but unfortunately it isn’t…

We are all painfully aware that there is plenty of uncertainty in the data we analyze and in the results that we generate through data science processes. Most of the time, we focus on removing as much uncertainty as possible…

As data science processes continue to become operationalized and embedded within business processes, the importance of governing those processes continues to rise. While governance has been a major focus for many years when it comes to managing data, governance focused…
Thanks to all who joined IIA’s annual Prediction and Priorities led by Bill Franks with co-panelists, Kathleen Maley, Eric Siegal and Drew Smith. If you’d like to give it another, or a first look, please go here to register for…
Less than two years ago, data literacy was not something I heard many people in the business world talking about. Recently, it is something that comes up in more conversations than not. In this post, I’ll address a few misconceptions…
A few weeks back, I was in a discussion with some analytics executives when one gentleman made a point that sounded odd at first. He suggested that in many cases we actually want the predictions we make with our models…