Chapter 8: A Bad Movie is a Bad Movie

• from the book: Data Quality is for Everyone

There are lots of exciting new technologies on the landscape today. You’ve heard about the Internet of Things, Blockchain, cloud computing, and Machine Learning. Depending on the nature of your job you, or your company, may be exploring them.

The reason I bring this up is that most people assume that new technologies mean better data. But this is a huge mistake. It is important that you understand why, so you don’t fall into a trap.

Consider a “movie” and the means by which you watch it. Not so long ago, the only way to watch a movie was to go to the theatre. Then along came TV and you could watch in your home. Then video cassettes, and you could watch anytime you wanted. Now you can stream movies, on practically any device, and watch them any time and place you want.

As I’m sure you know very well, streaming will not change a bad movie into a good one!

In this analogy, the movie is the “data” and the means by which you watch it is “technology.” And just as making movies is different than developing new means to watch, so too you must manage data and technology separately.

This point is so critical that I want to repeat it. You must manage data and the technology used to store, move, and process it separately, even though you need both. The big mistake lies in subordinating the data to the technology. Yet most people make it.

Interestingly, I find that everyone understands this analogy, and its implications, once they hear it. But they don’t think of it on their own!

HERE’S WHY THIS MATTERS TO YOU. I’m not trying to discourage you from new technologies. Frankly, I love many of them and some of them can be real game changers. But when “Data: The Movie” comes out, separating data from technology will be a key plot line. George Clooney will play me and the movie will win an Oscar! You should find a starring role for yourself!

Here is one way to get started: Write down three ways that this chapter is relevant to your organization and convene an hour long meeting with your colleagues to discuss the implications.

In case you missed any of the previous chapters you can find them all here.