Mastering data as a product with Hudl's Lucas Smith

Data as a product (DaaP) has become a buzzword over the past few years, but if you ask anyone what they mean when they say it, they’ll probably talk in circles a bit about dashboards, business insights, etc.

To get some clarity, we talked to Lucas Smith, Sr. Manager of Data Analytics at Hudl, to talk about how his team ships data products, and some of the key frameworks that guide that data strategy.

So, when does data become a product?

To get a good idea of what DaaP means, it helps to step back and think about the question, "What is a product?"

“A solution that can be commercialized for a problem” is how Lucas thinks about products. Those solutions can be internal and external within a company. Internally, that might look like data models in your warehouse or a Tableau instance. Externally, that might look like building your data into your software tool the way Ramp does with their price intelligence tool.

Lucas even goes so far as to say that a service your team provides is equivalent to a product. Anything that focuses on a specific problem and meets a specific user need.

To be clear, though, data on its own is not a product. It’s more like the raw material that can be molded and transformed into a product. Data teams’ jobs are to take that raw material and turn it into a product that can be used.

Figuring out who your market is

Productizing data sounds great, but creating a product sort of implies that there’s a market out there that will use that product. If you’re working on an internal-facing data team, though, who is your market, and how do you find product-market fit?

There’s a tendency among data teams to focus on data literacy as their KPI of success, and the whole company as their market. But Lucas says that approach is too unspecific.

“Your market should be focused on the problem space you’re trying to solve as a centralized data team and the solutions you can bring to them,” says Lucas.

That means, rather than blindly serving up data in the hope that someone in your org will use it, focus on their problem and the specific use case they have, then go about building what you need to build.

Product management principles for your data team to follow

“You need to be good at product thinking” is a common piece of advice that Lucas would come across when thinking about the idea of data as a product. And while it makes sense on the surface, it left him with a question. What does that actually mean?

For Lucas and his work at Hudl, there are two different frameworks he’s learned from product leaders that have helped guide him and his team as he prioritizes their work.

The first is the North Star Metric Framework—defining a North Star metric that serves as a guiding light that aligns data projects with business objectives.

Lucas has also seen success with the Strategic Bet Framework—a way to make strategic bets on initiatives that are expected to drive value. 

As an example of combining these two together, if your North Star metric is the percentage of the sales team using product data to make better-informed decisions, your “bet” could be that bringing in a BI tool like Tableau or Mode will help you do this. Knowing what you are placing your bets on will help you prioritize what you work on and how you deliver it.

These frameworks also help set up the ROI conversation. When you choose to focus on improving a specific metric, you can frame the value you’re delivering and what to expect in return for the time and resources you’re investing.

Treating data as a product goes against some of the more traditional ways of thinking in the data world, but for Lucas and the team at Hudl, that’s been a good thing. They’ve been able to set more specific goals and see more tangible results.

To catch our entire conversation with Lucas, head here!