There is probably a hard moment of transformation for many a legacy media company: When we say, “We want to be data-informed, data-driven,” do we mean it? 

How often is the data person in the room asked to join a project informed with a shiny PowerPoint, and the decisions made in the room are actually already made and not particularly informed by these data insights? 

If you’re the person in the room making a lot of the executive decisions, go back to your last big project and contrast the data you’ve been given with the decisions that were made. 

In my last blog post about how data teams should be centred around a powerful mission, I quoted Kendell Timmers, the SVP and head of data and insights at The New York Times, from the recent Monte Carlo Impact conference: 

“Curiosity, respect, collaboration, excellence — these feel very common to to any organisation within the company,” Kendell said. “So how does that work in the data world? Independence originally felt like a very journalistic focus. But in the data sense, we focus on unbiased analytics and fact-based recommendations. So I’m not going to provide something that’s the answer you want to hear. We need to provide the answer that’s the actual correct answer. Otherwise, we can’t make great data-driven decisions.”

When she says, “We can’t make great data-driven decisions,” there are two cultural angles to consider: 

• Whether the data team has a place of safety and independence to speak from.

• But also whether the rest of the company has a meaningful culture of accepting and leaning on data that may contradict long-standing assumptions.

This is a point I had heard in an interview with Robin Berjon (who was leaving his role as the VP of data governance at The New York Times and is now at Protocol Labs). He noted how much culture building was necessarily — from the data team and also from outside the data team. And that data wasn’t perceived to be a place to get your project rubber-stamped with “got data to back it up,” but rather a team that would constructively add value to whatever is being built or optimised.

“You need to have built basically a culture and a systematic awareness of what to do so that people come to you. And also you really need to make it clear that, you know, you’re not the data police, and you need to have a reputation for solving problems and making people’s lives easier,” Robin said.

“If you build that culture, then people will actually line up to come talk to you — just because you making their lives so much easier. And, you know, a lot of that has to do with being some kind of repository of institutional knowledge.”

But Timmers connected it to values that are also expressed in company values, writ large — those we convey to our ultimate constituents: our users.

“Integrity, building the readers trust in each other’s trust, from the reader standpoint, considering privacy and algorithmic ethics in our work,” she said. “That’s a really key part of integrity, curiosity — for everybody in the company welcoming up debates and different viewpoints. I think from a tech standpoint, a big focus and curiosity has been eager to try new methodologies and tools, new approaches, and also being welcoming of people who came to the analytical groups from different backgrounds.”

This is where culture and hiring start to dovetail — both in terms of making it more likely that hired team members flourish and contribute fully, but also in creating wider, richer pools. Particularly when it comes to diversity (a cruel problem in data), we can’t expect to diversify our workforce by plumbing the depth of the same small, shallow pools of candidates. And the cultural work to both prime the company to extend its understanding of what makes a great candidate and the cultural work to make your company a great place to work are very much two sides of the same coin.  

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