In this chapter, you’ll learn the methodology for doing analytics and data in your business, the metrics that matter most, the lingo you’ll use to talk about it, and the teams or roles that should be responsible for it.
But first, let’s talk about why data and analytics are so important to a successful business.
Why Data Matters
Data comes in two flavors: not enough and too much.
The challenge most people struggle with is how to turn numbers into meaningful decisions. Static numbers, in and of themselves, are meaningless.
So why would you want to do analytics?
To understand the answer, let’s review some examples:
The Oakland A’s Athletics Club
Beane’s approach was to focus on specific metrics, such as batting and runs, to find undervalued players no one else was noticing. This approached made the Oakland A’s one of the most cost-effective teams in baseball and carried them through 20 consecutive wins, playoffs, and even the world series.
Essentially, data made the A’s competitive with much bigger clubs while working on a budget a third of the size.
Netflix’s core belief is that customization wins customer loyalty, a belief that puts data at the center of their corporate strategy.
When they were still a DVD rental company, Netflix invested heavily in data mining technology to develop a movie recommendation algorithm, leading the way in using data to provide a great customer experience. And it worked. Recommendations drove 50% of their traffic.
After adopting a streaming model, this data-first approach continued, and it’s made them one of the top streaming video-on-demand services available.
We’re no strangers to data either. I’ll get into more detail later in this chapter, but here at DM, we rely on data to help us make business decisions that are all but guaranteed to work.
Our belief? Gut instincts may be good, but data never lies.