In my last blog, we looked at the evolution of the data team across three stages — from having support from data in the company (though necessarily, in fact, a data team in name) through the third stage where the first data science employee joins.
This week, we’re looking outside of the data team itself to focus on how the data team lives in the media organisation.
When Sanda Loncar, the head of the product and data team at Kleine Zeitung in Austria, dropped by my inbox a few weeks ago, she was interested in discussing whether her data team (five people) was positioned in an optimal way in the organisation. Is there an org chart that suits data teams best? I’d say this is somewhat above my pay grade, but there definitely are certain trends and they speak both to the company’s past, but also its culture.
Without a doubt, data teams live in various places at media organisations. The below analysis was done without a proper benchmark but is observational based on companies I have interacted with in the past year, mostly in Europe and the U.S.
Two broad organisational models
Data is part of the product organisation. In my observation, this means that engineering is also under product. This is the product-led organisation, and it’s getting more prevalent (in purple in diagram above).
Data is part of the engineering organisation, and this seems to align with product also being under engineering. This is the engineering-led organisation and it’s prevalent in tech but not in publishing (in yellow in diagram above).
In both cases, there is a variant where the data team is split between its functional responsibilities (insights) and its enablement (engineering). You’ll find this variant in both product-led and engineering-led organisations. We will talk about this in a little bit.
Now, back in the day, marketing-led publishing organisations were actually a dominant model. Marketing owned product (basically, all your big media companies 10 years ago were in that model), and insights/survey, etc., were very much tied to marketing. You still do find data under marketing, but I would say this is on its way out. For example, The Economist used to be organised like this several years ago but then elevated both a chief product officer and a chief data officer. Marketing stayed in that direct link to the CEO, but the data and product functions moved from under it.
Why is there not a more dominant model?
I think the biggest reason is that data teams at most publishers are still on the smaller side and still fairly young (most data teams were not around 10 years ago). Therefore, the way they emerged — and what part of the company drove their emergence — is the proverbial tree under which the apple falls.
Unlike other parts of our organisations that have been the subject of deep reorganisations in the past decade — certainly hastened by the revenue changes we’re seeing around advertising and print — data was built mainly in support of the digital product and, from birth, oriented at digital revenue.
But two things seem to hold up:
Data, by and large, is where it first started and grew. This is why you’ll see data under product or engineering at media companies that have equally well-demonstrated data chops. There isn’t a “when you are real about data you do X, and when you’re still junior you do Y.” (We’ll look into examples of this in just a moment). My analysis is that this is because data is still young at most companies and, more and more, data will move to join up with the group it most supports.
For orgs where data grew from analytics and business intelligence, they have gone closer to product. For teams where data came from engineering and where analytics and BI haven’t been merged and stayed with marketing, data is still a bit split with insights sticking with marketing and data with engineering. My analysis is that this model is on the way out and that data-in-marketing will get more rare.
But there is a later stage model where data runs under its own steam, on the same level as product and engineering. It may look like this (above) and this speaks (counterintuitively) less about the data team itself than it does about the way the company uses and considers data.
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