The Data Curious
Or the “Informed Skeptic”
In a 2012 HBR article, Shvetank Shah, Andrew Horne, and Jaime Capellá discuss the idea of the Informed Skeptic:
“Informed skeptics”—the employees best equipped to make good decisions—effectively balance judgment and analysis, possess strong analytic skills, and listen to others’ opinions but are willing to dissent. They’re the kind of data-savvy workers every company should try to cultivate.
I like the concept a lot, I’m not so keen on the name (and not just because Brits spell ‘skeptical’ with a ‘c’). Terms like ‘skeptical’, ‘cynical’, ‘doubtful’ and ‘suspicious’ are bad ways to describe valuable employees, and in this case implies mistrustfulness of others’ data.
Instead, when people ask me what qualities they should look for in a Product or Data Science role at a startup, I tell them that the number one requirement should be being Data Curious.
Data Curious is my shorthand for someone who has an intrinsic need to understand a data point. They understand that a number on its own very rarely tells the full story. Rather than simply being skeptical, the Data Curious person looks for more information and context. It helps her to understand, and increase her trust in, that number.
In question two of their quiz to find out which of their data-oriented groups you would fit into, Shah, Horne and Capellá ask the following question:
Reviewing recent sales figures, you notice a spike in a division that’s been struggling. You:
1. Look up some data, run some numbers, and make a couple of calls to figure out why sales are up.
2. Are suspicious about the increase.
3. Congratulate the division manager for turning things around.
Can you figure out which is the response of the Data Curious (or Informed Skeptic)?
It’s (1) of course: someone that quietly checks a few things, and reassures himself that the data makes sense. The Visceral Decision Maker (2) simply stays suspicious and the Unquestioning Empiricist (3) seems to accept everything at face value.
The Data Curious and the Sales Forecast
Here’s how the mind of the Data Curious person works when she sees the quarterly sales number compared to forecast:
A little above or below forecast: one raised eyebrow
Does the number make sense in context: is it consistent with its recent trend; consistent with the other products or divisions; consistent with the overall market?
Significantly above/below forecast: furrowed brow
Something went wrong. Was there a problem with the forecast, or the actual sales figure (or both)?
Spot on forecast: two raised eyebrows
Some things are too good to be true!
The best science publications publish the results of studies that are both peer-reviewed and reproduced. That’s not skepticism, it’s just part of a good experiment process.
Something that comes naturally to the Data Curious.