This sequence of cases are a companion to the Becoming Data Driven in Business Series on Commoncog.
What is the purpose of data? To paraphrase W. Edwards Deming, the purpose of data is knowledge. Knowledge here means a very specific thing: “theories or models that help you better predict the outcomes of your business actions.”
Deming has a second idea that is relevant to this sequence of cases: “management is prediction.”
The underlying argument that Deming — and all the other practitioners you’ll see in the following series of cases — is that in order to run your business properly, you need to be able to predict — within limits! — the effects your actions have on business outcomes. You need to know things like “oh, so if I run such-and-such marketing campaign, my leads should go up by around X% over the next quarter”, and “if we launch this feature, we should expect to see higher engagement from our core users, which may be measured using the metrics A, B and C” ... plus you need to be correct about those things.
Such things are easy to say, but hard to do. The flip side of running your business on knowledge is to run your business on superstition — unverified beliefs, if you will, of how your business works.
So, to put this into a single sentence: the purpose of analysis and data in a business context is to yield knowledge. You’ll know you’ve acquired knowledge if you find yourself operating your business with better prediction over time. Many of the techniques we’ve examined in the Becoming Data Driven series are in service of this pursuit.
The following sequence of cases should illustrate what this pursuit of knowledge looks like ... when it works.