Sometimes I walk into a room and say, “Alexa, what’s the temperature outside?” She answers by speaking the current temperature followed by an abbreviated weather report. She’s so human-like, I have to resist the temptation to say “Thank you” when she finishes. Importantly, Alexa is not a she; it is a component of Amazon's Echo natural language processing system. Amazon has anthropomorphized Echo with a female voice and a feminine name, which makes it easy to call Alexa a “she.” Should we be polite when we speak to it, or is it OK to be abrupt or even abusive? The device won’t care. It doesn't have feelings; but how will we teach our children to differentiate between machines that sound and act like people, and other disembodied voices that actually are people?
A vice president at a very large company just sent me a purchase order for a "blue disco ball." That's my metaphoric name for a specific kind of middle management error that most vendors, suppliers and even solutions providers love most. What is a "blue disco ball?" It's every senior executive's worst nightmare and every vendor's holiday bonus all rolled into a budget-busting good time.
We just used a few overeducated millennials and some open-source code to get a bunch of cognitive nonrepetitive workers fired. Which sucks! Incredibly, we didn’t use AI or machine learning to do it, just imagination and some free stuff. The bad news is that unless these people learn to do higher-value cognitive nonrepetitive work, they are not going to be employable. And the really bad news is that even if they do learn to do higher-value cognitive nonrepetitive work, when we start using machine learning and AI tools to do their jobs, they will actually be unemployable.
Generally speaking, there are two kinds of companies in the world: data rich and data poor. The richest of the data rich (Google, Facebook, Amazon, Apple, etc.) are easy to name. But you don't need to be at the top of this list to use data to create value. You need to have the tools in place to turn information (data) into action -- that's what the data rich do that the data poor and the data middle class do not.
Because the velocity of data is increasing and will always increase, the need for data literacy is increasing and will always increase. This does not mean that to be successful executive you have to become a data scientist -- quite the contrary. It means that in order to be a successful executive, you need to understand how data is turned into action, be familiar with the methods of data science and data scientific research, and be able to think strategically about how to use data to create value for your business. All other things being equal, there is a significant difference between being literate and being fluent.