Data Mesh Radio #64 The Crucial Value of Data About Your Data: Approaching Data with a Product Mindset - Interview w/ Sadie Martin
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Sadie's LinkedIn: https://www.linkedin.com/in/sadie-martin-06404125/
In this episode, Scott interviewed Sadie Martin, Senior Product Manager, Data Platform at Q4 Inc about applying a product mindset to data in general. This is really crucial to getting data as a product right but also in building out your data platforms and even some processes for data mesh.
Scott's summation of some key points:
- Anyone can apply a product mindset, not just the product manager
- Giving yourself the time before starting work to investigate and create you measurement framework, including your baselines, is crucial to measuring data work progress and choosing where to focus
- Approach your data work with intentionality
- Really understand what you are trying to accomplish and what your immediate customers/consumers are trying to use the data for to accomplish.
Sadie started as a data analyst where the team didn't have a product manager - they were doing a lot of work and weren't sure if things were likely to work or even if what they did had a positive impact after it was done. So she started to take on some of the task of answering those questions and transitioned into being a product manager for data.
So, what is a product mindset? For Sadie, the easy definition but with lots of hidden depth, is "it's all about really understanding the problem". For most organizations, really thinking about the problem you are trying to solve is new relative to data. There may be a data request but what product or process is that data contributing to and what is that product or process trying to solve?
Sadie believes measuring the problem is really crucial. Once you figure out what you are trying to solve, what is the scope of the problem? How are you going to measure if you are actually solving the problem? Especially is it better than what you were previously doing? She also talked about the importance of customer-centricity - really why are they making a data ask? Should this really be a one-off or a repeatable process? Did they ask for the complete set of what they need? Etc.
One crucial insight Sadie has brought from product management to data is to be willing and ready to throw things away. If it ain't working, don't be too precious. That's a very different mindset than we've historically had relative to data. There's also the idea that processes can devolve quickly so ensuring when you start a repeatable data process, understand the effort to keep it going.
While it feels counter-intuitive, Sadie laments that for most, it's often quite difficult to get the buy-in that you need data to measure if your data work is actually providing value. It's still worthwhile to do however. You need to take the time to do spikes and investigate ahead of time and slow down enough to set yourself up to measure results. Just continuing to go off assumptions and gut feelings is going to put you in a vulnerable spot to a competitor doing the work.
Sadie looks at measuring the success of data work in two ways. The first feels obvious once said but really isn't: start by measuring the baseline. Without that baseline, you can't measure if you're having an impact. And lots of data work proves to be low value or negative value - you tried a hypothesis and it isn't working so stop and move on. How do you get to that answer fast? You measure the incremental change for the effectiveness.
So what happens when you do look at your work and find out it's not been valuable? Per Sadie, you have to get away from the sunk cost fallacy. It's absolutely okay to make bets and they don't pay off and you move on. You need to really investigate if you are solving the problems you set out to solve. And by proving out the value of the product mindset so you can make better bets in the future.
A lot of the product mindset is also thinking about the return on investment, not just maximizing the return or value of data work. Can the simple get you where you want to go without doing the extra cool but complicated and/or risky parts?
Sadie mentioned a few things getting in the way of applying the product mindset to data. One is that there are often teams making promises on behalf of the data team without checking with them first. The other is many data consuming teams view the data platform teams as simply service teams, not partners.
While there has been a lot of hiring for data product managers in the last year or so, Sadie sees that often the companies aren't making the product mindset an actual priority and that feels like a waste of a good product manager.
There is a misconception that data work is all about facts. A large part of it is discovery work, much more than in most disciplines. Per Sadie, measuring a team's effectiveness should focus more on getting to an answer than getting to preferred answers. Evaluating a lot of hypotheses and proving them invalid isn't a bad thing - you prevented a lot of toil work that wouldn't have added value. Make sure to measure teams based on that.
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