One of the topics that cross my inbox or Zoom calls with publishers touches on the question of the transformation journey for an organisation that wants to take data from a support function to a key function. Or even, the zero to 1 of the data team.

Two common presentation scenarios are:

• A: “We don’t have a data team to speak of and we want to know how much resources and time we need to have something valuable.”
• B: “We want to know how we compare to others our size in terms of how our team is built and how it works”

These are fascinating stories. And I’ll be honest, when I first started to consult for INMA earlier this year, I thought there were probably as many different configurations of data teams as stars in the sky. And that the stages of evolutions of these teams were probably quite diverse because no organisation really evolves quite like its neighbour.

Well, dear reader, I’m now eight months into this engagement, and I’ve learned that I was wrong. Publishers are really very diverse and they grow (or contract!) in their own different ways often enough. Their markets are so different, therefore their businesses are so different, too. Yet the story of how their data team evolved often resembles each other, and some of the milestones tend to be reliably present in each organisation’s history. 

What these stages of evolution tell us is how data gets metabolised in our organisation: First, as a nice to have that is there in a supporting capacity for specific functions, and, over time, as a driver of innovation and growth that touches many more corners of the company.

Capacity at each developmental stage of data function

I was working on a presentation for INMA’s North America board and trying to synthesize some of the stages of publishers’ data journey. Here’s what I came up with, understanding there are probably other ways to group organisations (very keen to hear how you’d swim lane this!).

Stage 1: Content analytics

Most publishers have some in-house support for analytics (data analyst or a fluent marketing person who will “run the numbers”). 

The data that is recorded is mostly around content and with simple product analytics that are recorded by default in tools like Google Analytics or Adobe.

In my observation, publishers at this stage do not have an established team called Data. The data function is wrapped in with a team that has the most use for the analytics function.

Stage 2: Subscriber data and customer-oriented business analytics modeling

My non-scientific survey is that most publishers in a subscription journey collect user data, and those that don’t have a CDP are considering it. The data department may still just be analysis, but this is the stage where publishers lean into more advanced business analytics to support their new or growing subscription business.

At this stage, there is a team called the Data team. It’s mostly analytics with a little bit of product management.

Stage 3: Product and user data, modeling, predictive analytics

More advanced publishers have evolved strategies for making their products “talk” with data engineering and data science to level up. Specialised data roles in advertising and CRM also are present. In my observation, the marking moment for when a publisher moves between Stage 2 and 3 is when they hire their first data scientist. I’ll explain why this is in fact a pretty big turning point.

When I chatted with Sanda Loncar, head of product and data at Kleine Zeitung in Austria, she confirmed this was very much their transformation journey, too. Sanda built the Data team at Kleine Zeitung. And for them, going from Stage 1 to Stage 3 took two years, building the team from one resource to five. 

What’s interesting is that the company quickly wanted more than what the team could deliver: “It was never about being able to get the budget,” Sanda told me. In other words, the transformation was organic — the team grew, adding new competences as the business asked more of the team. It is the opposite of transformation led by mandate or by external pressures like cost cutting or a deeply changing business landscape.

This model doesn’t apply to everyone

I am not addressing here the case of the digital natives — the Vox, BuzzFeeds of the world. This is because their data teams were usually there from day one, fully formed. When you’re born on the Internet, you’re going to do marketing and sales with quantitative data rather than with market surveys and user interviews (which is where most legacy media groups of course started their “data” journey — on the qualitative side of things).

So while the data teams of these organisations are interesting, they aren’t transformation journeys. For many publishers, the transformation is the tricky bit. An established data team is a great achievement. But once it’s established, their problems look like the problems of most other teams: how to scale, how to work cross-functionally, how to prioritise. 

I have also asked this type of question to data folks who are larger, multi-country organisations or groups that were born from successive acquisitions. These are interesting cases too, but their development looked different. By and large, the data functions were born earlier in certain parts of the org — say at a specific news property, or they were already present in a group that was being acquired) — and were either merged to become a group-level resource or used to be the foundation of a group-level resource.

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