In my previous post I used junk drawers as an example of the downside of including more data in our analytics just in case it helps us discover more insights only to end up with more flotsam than findings. In this post I want to float some thoughts about a two-word concept
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Analyzing the data lake
Junk drawers and data analytics
In the era of big data, we collect, prepare, manage, and analyze a lot of data that is supposed to provide us with a better picture of our customers, partners, products, and services. These vast data murals are impressive to behold, but in painting such a broad canvas, these pictures
Hadoop is not Beetlejuice
In the 1988 film Beetlejuice, the title character, hilariously portrayed by Michael Keaton, is a bio exorcist (a ghost capable of scaring the living) hired by a recently deceased couple in an attempt to scare off the new owners of their house. Beetlejuice is summoned by saying his name three times. (Beetlejuice. Beetlejuice. Beetlejuice.) Nowadays