In modern product management practices, you go out and investigate your user’s pain points — try to understand the specific circumstances of these pain points and imagine what remedies could come to solve these pain points. 

Some aspects of the process also involve trying to size the problem (in terms of how many users have this problem, but also in terms of how bad the problem itself is to these users). And this becomes the size of the opportunity — the size of the opportunity being the flip size of the size of the problem. 

In data, we often approach projects where we assume that because data is a utility rather than the end goal, this work of product management really happens outside of our own department.

If a product team tells you, “Hey, I need data to learn X or to better under Y” — the issue for the data team becomes how to answer the question. And if the data team has several stakeholders with requests for new work, the data team has to figure out some way to prioritise this work.

This has long been an issue in our publishing space because the type of work the data team may be asked to do is often quite diverse. There’s CRM-related work for the marketing team. There’s product analytics work for the product team. There’s audience analytics for the newsroom. There’s funnel and conversion analytics for the revenue team.

You get my drift. 

Now, when a team has several very different internal clients like the above, it’s quite complicated to prioritise one type of work over the other. Who says the audience analytics work should be more highly prioritised than the marketing work? Every one of these stakeholders will have deadlines and real reasons for why they want what they want. But unless you have unlimited resources in your team, you most likely can’t do everything all at once.

I was chatting with Mehul Shah, the interim chief data officer of The Guardian, who said he considered one of the most important jobs of the data team is “making sure that whatever insight we are creating is reaching the maximum number of people across the organisation […] which is important for us to create value, because data is not directly the kind of initiative that generates value.”

So this idea — to try and make sure whatever data does get created is used in as many ways as possible — is analogous to the opportunity sizing exercise of mapping your proposed solution to all the personas and problems that you think your new product could solve. While the product may have been created to solve a core problem or intended for a specific core audience, it doesn’t mean that part of the opportunity that exists for you is actually lying elsewhere — in more tangential markets and solving more tangential problems. 

“We have to make sure that we start capturing where data is going to add value. So we started using an approach where we started talking to people who have an interest in certain new data or using it in a new way, and we try to understand what are they doing today? What will it will do for them if we have provided XYZ data insight and what value it will bring to them?” Mehul said.

To be clear, this is hard for the data team because it means collecting information we don’t always collect, which is trying to formulate and size the impact of data into a given application. 

In fact, as a utility team, data is often infantilised into not asking this type of impact questions precisely because it, as a team, does not have specific revenue goals to pursue. While it may be seen as a bit of a relief not to peg every single goal or decisions to a specific dollar number, pursuing value is a method that serves many parts of a company: Product teams decide what to build after sizing an opportunity; marketing teams decide where to focus campaign dollars based on expected returns; sales teams decide on pricing tactics based on volume projections, and therefore on revenue projections …

Now, I’m not suggesting the data team should have to assign dollars and cents to every goal. It would pretty difficult to do this in a number of cases, especially when it comes to building out core capabilities. But when it comes to the method Mehul is pursuing, this team at The Guardian is looking at value created — not so much because the team has to report on value created, but because it helps them make prioritisation decisions and can also help support funding or investment requests. 

“Every time now I have to go out and ask for investment, I can actually go out and say that, by the way, by investing in data initiatives like this, you should get the value out of it — whether it’s time efficiency, whether it’s cost efficiency, whether it’s revenue generation,”  he said.

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