Getting in Front on Data

De: Thomas C. Redman PhD

Narrado por: Randal Schaffer

This book lays out the roles everyone, up and down the organization chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics.

By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs and making it more difficult to make good decisions and leverage advanced analyses. While the problems are pervasive and insidious, they are also solvable! As Tom Redman "the Data Doc" explains in Getting in Front on Data, the secret lies in getting the right people in the right roles to "get in front" of the management and social issues that lead to bad data in the first place.

Everyone should see himself or herself in this book. We are all both data customers and data creators.

©2016 Thomas C. Redman (P)2016 Technics Publications

Recommending to my Executive Team

As a data professional, I'm immediately implementing some of the strategies described in this book, and recommending it to my Executive team.

For those who want to change the data organization

Very practical understanding given by Thomas, of a very spaghetti-like problem. I am the king of the "hidden data factory," and I am very proud of it. It is one of the only things a person can be proud of. These upper echelon managers that Dr. Redman refers to, the champions of cross-departmental efforts at data process reform, don't exist. He must be hallucinating. Still, like unicorns, they are perfect and ideal.

I bought a physical copy of the book, to put on my desk at work, because my head manager asked during the interview, "What are the last two non-fiction books you've read?" I've lately realized that all of my managers wanted to see more "thinky" behavior from me, more awareness of the business rather than just an extreme tight focus on data.

Deceptive and pointless

I'm a data professional. This book misses the entire point of.... everything. It brings in deceptive analogies (dirty lakes of "data" water) and extreme examples (lost Mars rovers) and just weird nonsense (equating intentionally deceptive data to bad data) to make the case that data quality is the be-all and end-all consideration. Most importantly it bizarrely and repeatedly makes the point that data is either "good" or "bad." That's not the correct classification at all: it's whether or not it prevents you from making good/bad decisions, and how bad a bad decision actually is compared to a good one. Those of us living in the real world have to take small matters such as budget and cost-benefit into account. And, realistically, there are many levels of review in most organizations around surprising data results before any serious decisions are actioned.

Anyone who is actually worked on data strategy would know that this book doesn't hold water. I truly hope that everyone else who is reading this book as a primer doesn't take it as gospel.