In my latest newsletter and blog, I shared my finding during my eight months leading the INMA Smart Data Initiative of the stages news media companies around the world are in around data organisation:
- Stage 1: Content analytics
- Stage 2: Subscriber data and customer-oriented business analytics modeling
- Stage 3: Product and user data, modeling, predictive analytics.
Today, I want to dig deeper into these three stages.
Stage 1 and 2: The birth of the data function and its growth
Data analytics in my observation is present at most companies — at least most INMA-member companies, though INMA’s membership skews towards the medium-sized org and bigger.
The reality is analysis done from the modern tools we use (Google Analytics, Adobe, etc.) requires a good mind that is versed in understanding some of the concepts of traffic and funnels. But the tools have improved in their user experience, so you do not need to go through a six-month training to get going with GA.
That’s not to diminish the essential function of a data analyst (good ones are in demand!). But it explains why professionals with various backgrounds can find themselves in the role of a data analyst without necessarily having formally learned the trade.
So analysts can be folks who’ve become the resident expert of the organisation’s analytics platform, or they can be a more specialist role. And they are attached to whichever function first formulates the need for analysis: marketing, sales. It varies.
In the past few weeks, I have interviewed folks at a large publisher in Spain, a medium-sized publisher in Austria, and a regional publishing group in Germany to focus on their zero-to-one journey. All three organisations share the trait of being significant players in their market, mostly anchored in one country.
They also share the traits of being organisations that are now in Stage 3 — but did it using smarts rather than fat piles of cash doled out by their rich corporate parent (to be fair, all three organisations are also profitable businesses). As it says on the tin, we’re the Smart Data Initiative over here, and not the Rich Data Initiative. These three orgs were smart, first and foremost.
So going back to our more traditional publisher type (the national media group or single property publisher) — going from “There’s data somewhere in our org” to “We have a data team” usually came from a place of strategy.
Sanda Loncar, head of product and data at Kleine Zeitung in Austria, for example, said: “We knew we had enough of a need for data for Kleine Zeitung that just using a shared group resource was not enough.” She made the case to bring the existing analyst over in her product group (then called the Digital group) and form a proper team there.
The Stage 2 of the data team seems to often get reached when analytics moves from being strictly a content engagement play to supporting a plan for direct reader revenue or complex advertising sales. A diverse group of constituents needs more dashboards, and the data team now has to scale itself.
In addition, breaking down complex funnels and acquisitions means adding tools like Customer Manager Platforms and generally leveraging more first-party data. This is why Stage 2 seems to have more dedicated engineering resources for data than in the earlier phase — where work to support analytics wasn’t done specifically by an engineer specialising in data but instead by a front-end team following an analytics tagging plan.
Stage 3: From data that answers known questions, to data that tells a story we don’t know
I’d like to focus on what I think is a turning point in the maturity of data teams: the hiring of the first data science member of the team. All three of my interviewees articulated something that can best be described as: “We wanted to do more than what we found in our dashboards.”
The role of the analyst is to hear a question and frame a method to get an answer from a pile of numbers we gathered. The data scientist, on the other hand, looks for new information in the data (doesn’t come from a framed question necessarily) and attempts to see if predictions and a model can be driven from this data to uncover new truths we didn’t know about. (This whole post from Harvard Business School online contrasts these two functions.)
A thing that makes understanding both our content and our users tricky in our news publishing business is that even very vertical publications have diverse audiences. New articles and old articles get read. Navigation comes every which way. There are superfans and a bunch of drive-by readers we see once and never again. Or maybe we see them again but on another device and do we even recognise them? It’s a lot.
The reason I mention this is the resulting data we generate is also a lot. And the approach where we list all our questions about our audience and business and content could keep a large team occupied ‘til the end of time.
But the problem is, even if the question is interesting, there is no guarantee we will get an interesting answer. Data science, on the other hand, approaches the pile of data and says: “Let’s see what are the most interesting things we could find in there.”
This shift in mindset leads to that first data science hire. And the team there also reframes its role from being a service to the questions of other groups to taking the lead on bringing the company insights and predictions — letting the data tell stories about your users or business and giving answers to questions you were not asking.
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