Don't let a fear of numbers hold you back. Today's business environment brings with it an onslaught of data, but leaving the analysis to others in your company just won't cut it. Now more than ever, managers must know how to tease insight from data--to understand where it comes from, make sense of the numbers, and use those findings to inform their toughest decisions. But how do you get started? Whether you're working with data experts or running your own tests, you'll find answers in the HBR Guide to Data Analytics Basics for Managers. This book describes a three-step process to get the information you need, study the data, and communicate your findings to others. You'll learn how to: Identify the metrics you need to measure Formulate hypotheses and test against them Ask the right questions of your data experts Understand statistical terms and concepts Create effective charts and visualizations Avoid common mistakes-- Provided by publisher.

Introduction.Why you need to understand data analytics -- Section 1. Getting started. Keep up with your quants : an innumerate's guide to navigating big data / by Thomas H. Davenport ; A simple exercise to help you think like a data scientist : an easy way to learn the process of data analytics / by Thomas C. Redman -- Section 2. Gather the right information.: Do you need all that data? : questions to ask for a focused search / by Ron Ashkenas ; How to ask your data scientists for data and analytics : factors to keep in mind to get the information you need / by Michael Li, Madina Kassengaliyeva, and Raymond Perkins ; How to design a business experiment : seven tips for using the scientific method / by Oliver Hauser and Michael Luca ; Know the difference between your data and your metrics : understand what you're measuring / by Jeff Bladt and Bob Filbin ; The fundamentals of A/B testing : how it works and mistakes to avoid / by Amy Gallo ; Can your data be trusted? : gauge whether your data is safe to use / by Thomas C. Redman -- Section 3. Analyze the data. A predictive analytics primer : look to the future by looking at the past / by Thomas H. Davenport ; Understanding regression analysis : evaluate the relationship between variables / by Amy Gallo ; When to act on a correlation, and when not to : assess your confidence in your findings and the risk of being wrong / by David Ritter ; Can machine learning solve your business problem? : steps to take before investing in artificial intelligence / by Anastassia Fedyk ; A refresher on statistical significance : check if your results are real or just luck / by Amy Gallo ; Linear thinking in a nonlinear world : a common mistake that leads to errors in judgment / by Bart de Langhe, Stefano Puntoni, and Richard Larrick ; Pitfalls of data-driven decisions : the cognitive traps to avoid / by Megan MacGarvie and Kristina McElheran ; Don't let your analytics cheat the truth : pay close attention to the outliers / by Michael Schrage -- Section 4. Communicate your findings. Data is worthless if you don't communicate it : tell people what it means / by Thomas H. Davenport ; When data visualization works, and when it doesn't : not all data is worth the effort / by Jim Stikeleather ; How to make charts that pop and persuade : five questions to help give your numbers meaning / by Nancy Duarte ; Why it's so hard for us to communicate uncertainty : illustrating -- and understanding -- the likelihood of events : an interview with Scott Berinato / by Nicole Torres ; Responding to someone who challenges your data : ensure the data is thorough, then make them an ally / by Jon M. Jachimowicz ; Decisions don't start with data : influence others through story and emotion / by Nick Morgan -- Appendix. Data scientist : the sexiest job of the 21st century / by Thomas H. Davenport and D.J. Patil.