Predictive analytics is red hot. Why? What organization couldn’t benefit from making better decisions?

Just ask the Obama campaign, which used sophisticated uplift modeling to target and influence swing voters. Or telecom firms that use predictive analytics to help prevent customer churn. Or police departments that use it to reduce crime. The list goes on and on and on. Virtually every organization could benefit from predictive analytics. Don’t confuse traditional business intelligence (BI) with predictive analytics. BI is about reports, dashboards, and advanced visualizations (which are still essential to every organization). Predictive is different. Predictive analytics uses machine learning algorithms on large and small data sets alike to predict outcomes.

But predictive is not about absolutes; it doesn’t guarantee an outcome. Rather, it’s about probabilities. For example, there is a 76% chance that this person will click on this display ad. Or there is a 63% chance that this customer will buy at a certain price. Or there is an 89% chance that this part will fail. Good stuff, but it’s hard to understand and harder to do. It’s worth it, though: Organizations that employ predictive analytics can dramatically reduce risk, disrupt competitors, and save tons of dough. Many are doing it now. More want to.

Few understand the what, why, and how of predictive analytics. Here’s a short, ordered reading list designed to get you up to speed super-fast:

" The Signal And The Noise: Why So Many Predictions Fail — but Some Don’t” by Nate Silver. Nate Silver built an innovative system for predicting baseball performance and predicted the 2008 and 2012 elections within a hair’s breadth — all by the time he was thirty. The New York Times now publishes, where Silver is one of the nation’s most influential political forecasters. Why read: This book will simultaneously inspire your “predictive” imagination and ground you in the realities of predictive analytics.“ Predictive Analytics: The Power To Predict Who Will Click, Buy, Lie, or Die” by Eric Siegel. Former Columbia University professor and Predictive Analytics World founder Eric Siegel has written a very accessible book on how predictive analytics works. It’s chock-full of dozens of real-world examples, such as how Chase Bank predicted mortgage risk (before the recession), IBM Watson won Jeopardy!, and Hewlett-Packard predicted employee flight risk. Why read: Predictive Analytics is a perfectly paced explanation of how predictive analytics works and a repository of dozens of real examples across many use cases.

Uncontrolled: The Surprising Payoff Of of Trial-and-Error For Business, Politics, and Society” by Jim Manzi.  Predictive analytics is not magic — it’s science. Jim Manzi reminds us of the power of the scientific method and reveals the shocking truth that many huge societal and business decisions are made based on  misinterpreted data and statistics. Why read: Controlled experimentation amplifies the results of predictive analytics and helps avert the risk of inaccurate predictive models.

Data Mining: Practical Machine Learning Tools and Techniques” by Ian H. Witten, Eibe Frank, and Mark Hall. If you write code or have a computer science background, you’ll probably want to know the gory details of how predictive analytics works. Why read: Data Mining provides a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. 

The Forrester Wave: Big Data Predictive Analytics Solutions, Q1 2013 (Forrester client access only). Forrester assessed the state of the  big data predictive analytics market. To see how the vendors stack up against each other, we evaluated the strengths and weaknesses of top big data predictive analytics solutions vendors, including Angoss Software, IBM, KXEN, Oracle, Revolution Analytics, Salford Systems, SAP, SAS, StatSoft, and Tibco Software. Forrester expects the market for big data predictive analytics solutions to be vibrant, highly competitive, and flush with new entrants over the next three years. Why read: These vendor solutions can jump-start your predictive analytics program.

Happy reading, and please recommend some other good predictive analytics reads.

This blog originally appeared at Forrester Research.