Data Alone Does Not Change People’s Minds

On NPR’s Hidden Brain podcast, cognitive neuroscientist Tali Sharot discusses the role of data in changing people’s behavior.

From Data to Action

The goal of analytics is to have a positive impact on the performance of your organization. To have an impact, you usually need to convince people to change their behavior.

This is required whether you want to convince a CEO to adopt a new strategy, a manager to allocate resources differently, or a knowledge worker to change their processes.

That’s why data visualization and data storytelling have become key skill sets for modern analytics professionals.


Data is Not Enough

How do you convince people to change their behaviors? Many analysts fall into the trap of letting the data speak for itself.

On a recent episode of NPR’s Hidden Brain podcast, cognitive neuroscientists Tali Sharot explains that data alone won’t do the job.  (The podcast is embedded above.)

Most people are familiar with the concept of confirmation bias, where we tend to accept data that supports our existing opinions. Sharot suggests there are ways to override this kind of bias.

Some key takeaways:

• People evaluate new information based on what they already believe
• Strongly held false beliefs are difficult to change with data
• Fear tends to lead to inaction, rather than action
• Positive feedback or hope is a powerful motivator if you want to change peoples actions

This is a fascinating listen for anyone interested in telling stories with data. Not only does it offer suggestions on how to change people’s behavior, it also illustrates the power of tracking results and making them available to people.

I’ve pre-ordered Sharot’s upcoming book, The Influential Mind. You should too!

Recommended Podcast Apps

I have received a lot of positive feedback from people who enjoy listening to the podcasts I mention on this blog. Several people have asked me how to listen to podcasts.

You can, of course, simply click on the play button in the posts. But you can also subscribe to podcasts using a smartphone app. This lets you listen on the go, and also notifies you when new episodes are available.

Here are two apps I recommend if you use an iOS device:

• Castro Podcast Player is perfect if you are new to podcasts, or if you subscribe to a handful of podcasts.
• Overcast: Podcast Player is good for people who subscribe to a large number of podcasts. It is more complex, but allows you to set up multiple playlists and priorities.

Further Reading

• Read (or Listen to) Discussions of Analytic Models (9/28/2016)
• What Hollywood Can Teach Analytics Professionals: How to Tell Stories (12/23/15)

Source: NPR’s hidden brain podcast for March 13, 2017

Read (or Listen to) Discussions of Analytic Models

Organizations often feel their analytics are proprietary, and therefore decline to discuss how their models work. One shining exception is Nate Silver’s FiveThirtyEight.com. The site makes a point of exposing how their models are built. They also discuss their models as part of their elections podcast.

Data Storytelling

Recommended Reading

As students in my courses know, FiveThirtyEight.com is a data driven journalism blog founded by Nate Silver. FiveThirtyEight covers sports, politics, science, and popular culture.

If you are interested in visualization, analytics, or telling stories with data, you will enjoy the site.

Stories on FiveThirtyEight are always shaped by data. And if they develop a model of any kind, that model is openly explained. You may have to cull through footnotes, but its always there.

One of the most detailed discussions on the site right now describes their 2016 election forecast model. (With apologies to readers outside the US, this is a very US-centric topic.)

Podcasts

FiveThirtyEight also offers several podcasts, where you can listen to analyst discussions which are driven by data.

Until recently, these conversations rarely delved into the technical realm. On the elections podcast, if Nate Silver or Harry Enton mentioned “long tails,” “blended averages,” or “p-values,” the other hosts jokingly steered the conversation back to analysis.

That practice was put to an end a few weeks ago with the establishment of “Model Talk” episodes. Every second Friday the model itself is discussed in greater detail. For example, in the 8/26 episode, Silver describes the predictive value of state polls over national polls, and why it is important to build a model where state by state probabilities interact.

Here are links to the “model talk" discussions to date:

• The Elections Podcast Checks In On The 2016 Forecasts (Aug 26, 2016)
• What Makes a Tipping-Point State (Sep 9, 2016)
• The Key States In The Contest For Control (Sep 23, 2016)

Recommended Reading

I also highly recommend Silver’s book, The Signal and the Noise: Why So Many Predictions Fail—but Some Don’t. If you are interested in analytics, it is a fascinating read.

What Hollywood Can Teach Analytics Professionals: How to Tell Stories

You might not realize it, but you probably have something in common with the creators of the TV show South Park. 

Analytics yield insights that can have powerful business impact. These insights come from statistics and data mining—processes that are inaccessible to most people. If you want your business to learn and remember, you have to tell a story.

All too often, the communication of an analytic finding reads like a police report: procedural, laden with jargon, and stripped of meaningful business context.

That’s not interesting. People won’t learn from it, and they certainly won’t change their behavior.

How then to get your point across? You need to learn how to tell stories. Data stories.



Trey Parker and Matt Stone know a thing or two about telling a story. They are the creators of South Park, a wildly successful television show which has been on the air for 19 years. Like you, their success depends on telling interesting stories.

In the video clip above, Parker and Stone are speaking to a group of students at NYU on storytelling strategies. Trey tells the students:

We can take these beats, which are basically the beats of your outline, and if the words “and then” belong between those beats, you’re f***ed. Basically. You’ve got something pretty boring.

What should happen between every beat that you’ve written down is either the word “therefore” or “but.”

Data storytellers make this mistake all the time. "We did this…then we tried that…the algorithm showed this…the correlation coefficient is that…our conclusion is...”

This kind of forensic storytelling is boring. It won’t be remembered, and the value of the insight will be lost. Save the procedural detail for an appendix somewhere. People learn from good stories, not lab reports.

As Matt says later in the clip, you need causality to have an interesting story:

But. Because. Therefore.  That gives you the causation between each beat.  And that…that’s a story.

Be sure to watch the entire clip and, if you are so inclined, take some time off for an episode or two of South Park. It just might make you a better data scientist!



The embedded video is from the NY Times ArtsBeat blog post, Hello! Matt Stone and Tray Parker Crash a Class at NYU (September 8, 2011).  Hat tip to Tony Zhou and his Video Essay on F for Fake at the marvelous blog Every Frame a Paining.