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Data Products and the Journey to Data-Driven
One consistent thing I’ve observed in writing about data product is that nearly everyone gives an uncomfortable reaction when talking about how far their company is from being data-driven. Yet at the same time we have more access to and understanding of data than ever before. What, exactly, is going on?
My view: we’ve mistaken building data assets as synonymous with creating a data-driven culture, but they are *not* the same thing. There is no “if you build it, they will come” for data products. In fact, this isn’t the case with almost any product, so why do we assume that data products are any different?
I think we’ve assumed that by having a bunch of really smart people create scientific measures and insights, that culture change will come along with it if we just try hard enough. In in 2010-2015, companies started to build data science teams. They built dashboards, models and insights. These were scientific, and provided definitive answers, so logically, good decisions should follow.
But we stopped short of asking “ are we actually getting sustainable value out of data?” with our investments. Yet when the time for justification of those investments came around, it seems like the explanation was that the path to being data driven is a long and challenging one.
Since I started writing this newsletter, I’ve talked to hundreds of data scientists and data professionals, the more I started seeing a graveyard of well-intentioned data products that could have been successful if they had a critical eye attached to them early on. While some of products weren’t sustainable because of the technical decisions the creators made (or did not make), the vast majority of these products did not survive because they were not built to evolve, they were built to solve a specific problem at a specific time - not for durability.
The answer to “how do we get long-term value out of data?” is “make a data product that solves a real problem for your customer” and not to stop until that customer finds value in what you’ve created. A big problem is that we’re generally impatient with data products. If it doesn’t work on first pass, we tend to toss it aside.
So, what does it look like to be patient? What does it look like to understand how a product could improve to create a more data driven organization? Consider the scenario and a few practical suggestions following it. (Note: I put this situation in one of my first newsletters):
Imagine a Monday morning where you walk into the office and the VP of Marketing is furiously preparing for a board presentation and asks you for a way to see conversion over time. You go heads down for 8 hours and send the dashboard over, perhaps never to think about it again. In that moment, you can’t possibly think about the long-term, you’re thinking about things in that moment.
If we want to make a data product that truly makes customers more data driven, we need to be very clear on how the product does that job, and that the customer knows what the product is supposed to do. Moreover, The customer (either internal or external) needs to understand that our goal is to help them. We need the customer to trust us enough to expose their problems and goals. We need to make being data driven feel like the natural course of things, rather than make it feel like working against the grain.
So, where does it make sense to start, and how do you get the customer involved in being more data driven?
Write down who you think your customer is (either internal or external). Write down everything you can that defines them as your customer. This includes characteristics, behaviors, areas of focus. Share this with a couple of other people on your team for feedback.
After sharing this with your team, share it with your customers. Ask them if you think it fits with who they are. Obviously this doesn’t work for every customer, but you’d be surprised at how many are willing to engage here.
Keep a log of how you think this customer profile evolves over time. Its not just important to understand who you think your customer.
Define the role of the customer in making decisions or helping a company be more data driven. What do they rely on to make that happen?
Define what your product is, from your customer’s perspective. What do they think the product does? Why do you think they choose to use it? What do they see as the limits of the product?
Be as specific as you can. General descriptions aren’t helpful here - press for more information.
Don’t assume you can understand how they think about the product just with a verbal or text description. Ask them if it is possible for you to see them use it, to understand how they believe it works.
Identify if they see it as connected to decision making. If so, how? If not, why not?
Define how that product fits into the customer’s workflow in a way that supports data driven decision making.
This is really, really hard. It is easy to assume it fits naturally into their workflow. In most cases, it doesn’t!
Seeing is believing. Don’t rely on someone’s description or acknowledgment of how they use a product. Get in situations where you can see how it actually contributes to data driven decision making.
The suggestions above this don’t make an organization data driven nor do they define a perfect product. But they paint a realistic picture of the challenge the product is intended to solve and where you are with respect to solving that challenge. Being open about where you are starting and having a realistic sense of the problem is incredibly important.
All this is to say, there’s a good reason being data-driven is hard. It requires products and tools that enable people to do so and making those products is challenging to do.
Want to start off on the right foot in being data driven? Think about the data products that enable it, and what problems they solve for the customer on that journey.