Using Data Science to Transform Information into Insight

3 Facts I Bet You Didn’t Know about Data Science and Scientists:

 

  1. Data scientists are not mystical practitioners of magical arts.
  2. Data scientists are “sexy” according to a recent Harvard Business Review article.
  3. Data science can “call presidential races, reveal more about your buying habits than you’d dare tell your mother, and predict just how many years those chili cheese burritos have been shaving off your life.”

I learned these facts minutes after picking up John Foreman’s new book Data Smart: Using Data Science to Transform Information into Insight.  Data Smart is the textbook for anyone wanting to turn raw data into action that makes a difference.

John is the Chief Data Scientist for MailChimp.com, the email service powering subscriptions marketing campaigns.  MailChimp also powers blogs like this one, allowing you to sign up and receive blog posts in your inbox.  John has also worked with a range of organizations from the FBI and Department of Defense to global corporations including Coca-Cola and Intercontinental Hotels.  You can follow him on Twitter @John4man.

Data Smart by John ForemanJohn, who did you write this book for?

I wrote Data Smart for anyone who wants to learn the cutting edge analytic techniques that businesses like Amazon and Facebook are using to turn their data into revenue.

And when I say “learn,” I don’t mean just “learn about.” In Data Smart, readers use actual techniques, such as artificial intelligence and data mining, to solve real business problems. That way the reader can get a sense of how to apply them to their own work. Think of the book as on-the-job training.

That’s why each chapter works through a data science technique in Excel – spreadsheets are a safe environment that readers feel comfortable working and following along in.

He who would search for pearls must dive below. -John Dryden

I wrote Data Smart for anyone from business intelligence analysts to programmers to quantitative marketers to sports analysts to C suite executives. For anyone who truly wants to learn analytics, this is the most accessible book for gaining a foothold in the discipline.

Misconceptions about Data Science

Give us your definition of data science.  What’s the biggest misconception people have about your field?

Data science is the use of transactional business data (think sales data, website traffic, social interactions, ad conversion data, employee performance data, etc.) to make decisions that result in revenue growth for the business.

There are a few big misconceptions about data science. First, the field isn’t just for those who do online advertising (e.g. Facebook, Twitter, or Google). No, a brick and mortar mom-and-pop shop can benefit from artificial intelligence models too. For instance, if you run a hotel, being able to forecast demand in light of your prices and competitors’ prices is invaluable. And that’s true whether you’re a single hotel or Intercontinental.

Second, you don’t need a Ph.D. to do data science. Some of these techniques, like customer segment detection, are analytically tough, but anyone with the motivation and some spreadsheet skills can learn how to do it.

The Surprising Predictive Power of Analytics

You have been predicted.

Companies, government, universities, law enforcement.  All are using computers to predict what you will do.

Will you click on the link in the email?

When will you die?

Will you pay your credit card bill on time?

Are you pregnant?

Dr. Eric Siegel recently released Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die. It’s a fascinating book that has surprisingly broad ramifications for all of us. Eric is a former Columbia University professor, the founder of Predictive Analytics World and Executive Editor of the Predictive Analytics Times.

Let’s start with the definition. What is predictive analytics?

It’s technology that gives organizations the power not only to predict the future, but to influence it. The shortest definition of predictive analytics is my book’s subtitle, the power to predict who will click, buy, lie, or die. Predictive analytics is the technology that learns from data to make predictions about what each individual will do–from thriving and donating to stealing and crashing your car. By doing so, organizations boost the success of marketing, auditing, law-enforcing, medically treating, educating, and even running a political campaign for president.book_med_2

Why should the average person care about predictive analytics?

Prediction is the key to driving improved decisions, guiding millions of per-person actions. For healthcare, this saves lives. For law enforcement, it fights crime. For business, it decreases risk, lowers cost, improves customer service, and decreases unwanted postal mail and spam. It was a contributing factor to the reelection of the U.S. president.

Let’s jump to politics then. How did President Obama’s campaign gain an edge by using persuasion modeling?

The Obama campaign’s analytics team applied persuasion modeling (aka uplift modeling) in the same way it can be applied to marketing: drive per-person (voter/customer) campaign decisions by way of per-person predictions. If an individual is predicted to be persuadable, then make campaign contact (e.g., a knock on the door). By utilizing resources (campaign volunteers) more effectively in this way, the campaign enacted the new science of mass persuasion. They proved this won them more votes, within swing states and elsewhere.

Everyone is talking about “big data” but data on its own isn’t interesting or useful. You explain how data can show incredibly interesting insights including the fact that if you retire early, your life expectancy drops. Tell me more about that and what else we’ve learned from it.

Beyond the great hype around so much data, the real question is what to do with it. Answer: use data to predict human behavior.

The whole point of data is to learn from it to predict. Talking about how much data there is misses this point. What is the value, the function, the purpose? The one thing that makes the biggest difference to improve how organizations operate is to predict.