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3 media companies share 3 paths to building data teams

By Ariane Bernard

INMA

New York, Paris

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This is the third and final part of my little series on the “Zero to 1” of the data team.

We looked at some of the stages of evolution of the data team. We looked at some org chart considerations. Today, I wanted to share some approaches I have heard from publishers going zero to 1.

These don’t claim to be statistically representative, but they actually all fit with the previously mentioned three-stage model: That is, the place and function of the data team changed in response to how the organisation re-envisioned how to use data and how it would be best able to serve the whole organisation.

And with that, I offer you three paths of transformation.

Path 1: The Blitz

José Meroño was hired in 2019 as the director of data for Prisa Noticias, the Spanish group that publishes the leading daily El Pais (José has just recently left Prisa). “I started from scratch, I was alone,” José told me. But he was also hired because he had built a whole data team at two previous companies. 

One path to building a data team is to pull it together all at once, ASAP.
One path to building a data team is to pull it together all at once, ASAP.

The mandate was clear: Spin a whole team, as fast as possible — build a team, infrastructure, and support Prisa’s new digital subscriber revenue effort. 

Now Prisa, of course, did have some data resources prior to José’s arrival: analysts living in other teams. And the first order of business was taking in these resources to locate them with the future team. 

But even so, listening to José, your head spins a bit — lighting the hiring fires here, creating a subscription model over there, building a data warehouse in another corner. 

“Eighteen months, five to 10 people. You get good results.” Obviously, this story is told a bit on the super modest side. (I have invited José to tell you the full story of this campaign at our Product & Data Summit in November, but he’ll probably still be modest even though this is quite the story of achievement.)

The model here: A very clear vision for how to get to Stage 3, as quickly as possible. Start all the fires all at the same time. The secret weapon: Someone who has burned this path before and given the means to accomplish the goal.

Path 2: Parallel paths and a merge

A few weeks ago, I was chatting with the director of digital publishing at a large regional media group in Germany about its transformation journey. One thing that was quite striking was that we talked far more about the patterns of organisational transformation than the specifics of how their data team needed to grow and change.

This was a leader who knew what new functions the data team should be able to serve across their organisation. They also had done a lot of the foundation work like setting up a CDP. 

(This publisher is anonymous because, as you’ll see, there is a politically sensitive moment up ahead for them. It’s still a valuable story, all the same.)

The complexity was more how to take existing participants in the data function — which supported sales — and augment these functions with some of the missing pieces including data science. In the end, the concern for the time-to-market it would take to transform the current team before it could be scaled was the driving motivation for the choice our German publisher made to build a parallel team and merge the two down the road. 

Much like Prisa’s story, the right people make or break the transformation: “To have the right person on board, it took us more than half a year to define the strategy, and it only really took off when we had a person taking it on as a challenge. And this person is an expert. The person with a vision.”

The model here: A path that’s definitely bolder than the incremental build out of the existing data function because there are two key moments: building the second team and the merge with the existing data function. But our publisher kept their eyes on the prize: Time-to-market to reach the mature team stage as fast as possible. You’ll need to drive this with two qualities: strong strategic vision and political sensitivity to navigate the merge.

Path 3: The organic build-out

Sanda Loncar was the head of digital for Kleine Zeitung in Austria. For her, each stage in building out the data team was reached organically: Needs changed, the company used data more and more, and the needs were getting ever more complex.

Another path to build a data team is to grow as you need to.
Another path to build a data team is to grow as you need to.

The data team was first conceived when Sanda made the call to bring over into her digital group (now the Product and Data group) the analyst who sat near her team but wasn’t quite “of” her team. 

“We started our transformation project three years ago,” she said. As the company leaned into its digital transformation, “that’s what triggered the need for data service: subscriptions, ad sales. We recruited additional analysts and data science. We also have our own data engineer.” 

But, Sanda explains, the expanding team really kept adding as the company maxed out on what was possible for the team: Dashboards were no longer sufficient, every product team wanted something from data, ad sales wanted to sell against segments …

The organic build-out is, in many ways, the most comfortable path. It’s the path of companies that fully grow into their own organisation and what it can achieve, and then set out for new goals and bankrolls the way to get there. 

Next for Kleine Zeitung: Continuing to build out their first-party data (to support ad sales and subscription folks) but also personalisation. 

The model here: A steady hand, and … the ability to prioritise. Sanda comes from product, where roadmapping is the bedrock of the practice. Still, she notes, when you grow organically, you’re constantly just a little bit too small for what you actually want to do. So prioritisation exercises are both a place of frustration (and management of stakeholders’ expectations) but also the place where the next goal to reach gets crystallized.

This is a path where questions of resourcing are also clearest — the data team added the roles it really needed based on direct observation of what the company was going for in terms of data services. Slow and steady wins the race.

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About Ariane Bernard

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