Hamel Husain says data scientists should work like investigative journalists, mixing interviews and anecdotes with numbers to find the real story behind problems like margin leakage or churn spikes. Pure statistics miss what's actually happening. The goal is telling stakeholders a narrative they can act on.
What I mean by story is, in a lot of ways, the good data science in businesses— yeah, looks like investigative journalism.
Good data science in big businesses looks like investigative journalism. Tell me more.
If you're having margin leakage, or your churn rate has all of a sudden skyrocketed, or you have a leaky funnel of some kind, or customer satisfaction is low, or whatever the hell it is, there's a lot of skill in figuring out, teasing out the story. This event happened. What can I learn from both qualitative and quantitative analysis of what exactly happened here and what can I learn? What does that sound like? That sounds like, like if you pick up the newspaper and start reading some breakdown of something, you will often find both qualitative and quantitative analysis of a situation. They'll tell you some story, some anecdotes, some interviews, then often there's some data if you analyze this thing, and this supports that qualitative gets more color. Sometimes it actually adds some things they can't explain with data. It gives you a holistic understanding of a problem. So that's one part of it. You communicate it in a way to everyone else that they can understand to like make some kind of decision.
Yeah.
And you can take it further. You can't reduce data science only to investigate. That's a good— that's like a good— yeah, sort of people don't think of it that way.
They're like, oh, it's just about doing statistics and run some code on some data and whatever.
