In this exclusive interview, Richardson points out four of these approaches that data teams industry-wide should watch out for, and how leaders can break from the pack to better serve their strategic goals.

Interview w/ Harshjit Sethi — Sequoia Capital, Dropbox, McKinsey

In this exclusive interview, Tabb shares how to abide by metrics that generate direction — not pats on backs — so that your company can act on what’s most essential to a business. He contrasts vanity and clarity metrics across a handful of types of companies — including service, advertising, software and ecommerce — to demonstrate how to find proxies that better predict behavior over time. Lastly, he emphasizes how to prevent teams from getting seduced by vanity metrics — and tips on honing their instincts for real, productive measurement.

Because good ideas can come from anywhere – no matter if your business is at a startup, growth or mature stage. If anyone on your team, even new hires, do not understand the meaning of key SaaS metrics, you can be missing out.

The goal is always to make data make sense to everyone in the company.

You need a metric that specifically answers this. It can be “x people did 3 searches in the past week”. Or “y people visited my site 9 times in the past month”. Or “z people made at least one purchase in the last 90 days.” But whatever it is, it should be a signal that they are using their product in the way you expected and that they use it enough so that you believe they will come back to use it more and more.

Ebay's VP Design and Creative Director, Dave Lippman, shares his experience designing at Apple and Ebay, the differences in their approach to data vs. intuition, and how he's learned to dance between the two.

Ben Foster and Bob Moesta share stories and insights about how the key to gathering customer feedback is through the deeper, often unstudied observations. Leslie Bradshaw then joins in with the idea it's never too early to start testing a new product, we just have to be willing to put it to paper. Then Ben Foster closes the interview with a story about how a company was able to pull data from bike thieves in a creative way, and how the company used that data to create the ideal product.

Examples of companies using big data in unique ways: Yelp is using modern search terms to find the perfect restaurant, businesses are changing their marketing depending on the current weather, Google tracks their employee's patterns to see if they are thinking about quitting, and Joel Selanikio discusses about using big data to transform global health policy.

Jeff Feng, Product Manager at Airbnb, shares how he launched “Data University” to empower employees to make data-informed decisions and how he got over a third of employees to attend a course.

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Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.