How Adopting a Product Mindset Can Improve the UX and ROI of Your Data Science and Analytics Work

The work of enterprise data science and analytics teams is often experienced in software—whether it be via custom apps, dashboards, or BI tools. As such, data teams are software teams—but many of them do not build solutions the way the best software product teams do. What can data leaders learn—and steal—from software teams who put product, users, and customers first?

What does it mean to integrate a "product mindset" if you're working in an internal enterprise data science or analytics team?

For one, a product mindset usually means you're thinking about designing a reusable solution that meets—and hopefully exceeds—the needs of the people who matter. In the software product management space, this usually means a relentless focus on the customer.

One framing I like to use is this: "How good would this solution have to be if we were charging our users to use it? What would we do differently if our users were paying us to use this?" 

In enterprise data teams however, "customer" often gets mixed up with "stakeholder."

You might even have a third party called a "user" who is neither a customer nor a user.

Design makes us consider all the humans in the loop so that any product that is created stands a chance of being successful.

The act of making a data product viable and valuable occurs through design—whether we design with intention, or the resulting design is just a byproduct of technology decisions. (The latter is often the case with teams who have never worked with design or product designers.)

A product is not just "any piece of tech." A piece of technology that is technically right, and effectively wrong is just an "output."

No user or stakeholders wants outputs from you—even if they asked you for a dashboards, ML model, report, or application.

Products become viable when they help users achieve their desired outcomes.

User Experience with a product is all about making the life of the user better. Sometimes we need to make the lives of internal employees and colleagues easier in order to ultimately bring value to a customer.

Sometimes the work isn't benefitting the customer at all, and while design can help satisfy the needs of a stakeholder, a product-oriented team should be asking, "how does driven value for the customer?"

One of the biggest leaps that non-designers in the data space need to make is that design is a methodology to make products that matter for both users, customers and the business. By definition, human-centered design should be considering all of the humans in the loop. Often times, the linchpin in ensuring a data product creates value is getting the target audience to actually use and adopt the solution.

When data products aren't used by the target audience, it's often because they are too hard to use, too confusing, have opaque value, not trustworthy, the status quo is too strong, the switching cost is too high, or they are simply undesirable.

If any of these are true, it's extremely difficult to get "business value" out of the products. When this occurs, your data and engineering effort is simply a "cost."

Focusing on solving the right problem, and making the data products useful, usable, and desirable is intrinsically tied up with "product mindset."

In my worldview, product design aka human-centered design, is inherently wrapped up in product management and the idea of "product" in general. You cannot decouple these. Products without design usually equate to "data outputs" that nobody uses.

As such, if you want to adopt a product mindset when doing analytics and data science work, it may be easiest to begin by adopting human-centered design. You definitely can't get to "value" if the users of your solution don't use your solution, so start with usability, utility, and desire.

There's more to product than this, but if you get this right, better data products—and business value—will likely follow. And at that point, you're probably well on your way to adopting a product mindset.

Here are some more articles and podcasts I have recorded that may help. If these are helpful, you can join my Insights Mailing List to get notified when new ones come out:


Articles I've Written:

My Top 10 Predictions for Data Product Leaders in 2022

10 challenges internal data leaders will face creating a revenue-generating data product

Does an analytical mind block your innovation and creativity?


Solo Podcast Episodes:

049 – CxO & Digital Transformation Focus: (10) Reasons Users Can’t or Won’t Use Your Team’s ML/AI-Driven Software and Analytics Applications


Podcast Interviews with Guests:

078 – From Data to Product: What is Data Product Management and Why Do We Need It with Eric Weber

068 – Why User Adoption of Enterprise Data Products Continues to Lag with International Institute for Analytics Executive VP Drew Smith

061 – Applying a Product Mindset to Internal Data Products with Silicon Valley Product Group Partner Marty Cagan

044 – The Roles of Product and Design when “Competing in the Age of AI” with HBS Professor and Author Karim Lakhani


Photo by Jess Bailey on Unsplash

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