Teaching Us How to Avoid the Data Distraction
A recent article by Stephen Goldsmith introduced me to a wonderful term “the data distraction.” This is the tendency for data users to start with the data instead of the problem, challenge, or opportunity that inspired them to look at the data in the first place.
As Goldsmith points out in the article, the data distraction can be easy to fall into. The available data seems to provide so many opportunities with just simple actions like counting, aggregating, and basic comparison. This is particularly true if the organization has only recently gained the ability to effectively work with the data it’s collected. In avoiding the data distraction, organizations are able to discover the more important “why” behind the “who”, the “what”, and the “where” that people are so often focused on. In the case of San Francisco, getting beyond the data distraction revealed:
fine and fee reductions often resulted in more revenue, not less.
This result is completely counter-intuitive and resulted from being able to move below the superficial story to more deeply probe not only the data, but the problem itself. We are able to do this by:
- defining key terms
- questioning assumptions
- testing hypotheses
This isn’t easy and is often outside the experience of most people, whether in or out of government. These are collaborative skills that must be learned and cultivated over time in order to realize a data driven culture.
So how do we teach people to avoid the data distraction and get to the important and impactful insights we’ve been promised from great analysis?
This question presented itself soon after I started teaching data analysis classes. My natural inclination as an analyst was to start with the data. I soon realized most of the participants in my classes were just going along with what I was doing but often had little idea what I was trying to demonstrate or why. The lucky ones were able to see their own work in the examples I gave and perform the mental effort to apply the techniques to problems they had in their work.
Taking a step back with the help of a wonderful consultant, Matt LeMay (yes, even consultants needs consultants) and collaborator Julia Marden, I began to see it wasn’t the tools and techniques I was teaching, but the mindset and the approach to the work that sought to answer questions with data rather than suppositions, assumptions, and best guesses. I wasn’t teaching them analysis or Excel but teaching them how to think analytically using Excel.
I then came upon this great quote, which helped inform my thinking:
If you do not know how to ask the right question, you discover nothing.
- W. Edwards Deming
From this, I brought my own design thinking process for developing classes and scoping data analytics projects into the classroom. In my Data Analytics for Managers class, students spend the first half of the day collaboratively brainstorming and planning the work to address noise in NYC. They learn by taking a poorly scoped problem (“reduce noise complaints in NYC”) and break it down into the important aspects of the problem, identify the desired outcome, assess the available inputs, and identify the desired outputs. We then sequence a series of questions to be answered in the course of the analysis, with attention to the outcome rather than just producing outputs (an issue we delve into at great length throughout the class).
While I emphasize that in our trainings, our focus is process over product, but I have to say, we’ve had some great products:
So how do we fight the data distraction? By training as we want to act, teaching data analysis not as an operation only concerned with the data or the tools to analyze it, but as a holistic approach to analysis as a way to provide fact-based solutions to well understood problems.
Feel free to share your own thoughts on avoiding the data distraction to the comments section below. I’d love to hear about it.