In the past I've posted about how some data scientists are only interested in being data scientists, but are thrust into management. I'm here to tell you first-hand that even those who want to be managers still just want to code sometimes.

Chances are that you're collecting a lot of data for a host of reasons, but it is sitting idle on some server. You've got Dark Data.

How many times have you - or your data scientists - lamented the fact that the team lacked enough data to properly train a model? 

As a senior leader, how much technical knowledge should you have?  We offer one perspective here.

I've written 12 blog articles with the goal of helping my readers avoid the data Trough of Disillusionment. Here, I'll describe the Trough and the rest of Gartner's Hype Cycle for Emerging Technology.

One of my favorite things to do is to show people what is possible. Another is to be resourceful and grab whatever is at hand to solve a problem.

Question: Have you ever wondered how many different types of analytic insights you can get from one dataset? Read on to learn the answer.

Squirrels are darn resourceful. Just when you thought you cut off every path to the bird feeder, you catch them flying through the air, landing exactly where they planned to.

Do you really know what your graph is saying? Learn some of the things to look for, and see the same data graphed eight different ways.

Concepts like artificial intelligence, machine learning and data science should no longer be referred to as 'emerging technologies'. 

Who is the smartest person in the room? Answer: No one. Particularly with respect to data operations. Consider the parable of the Blind Men and the Elephant.

Ever meet a new developer or data scientist who just loves managing people? If so, you've truly met a unicorn.