How do we build for customers: “internal” data team members who need to work more effectively with data, and “external” data consumers who use products created by the data team?
The JTBD framework – one of the most popular frameworks used by product teams – helps you build products that people love. For example, a product manager’s JTBD might be to prioritize different product features to achieve business outcomes.
How does this idea apply to data teams? In the data world, there are two main types of customers: “internal” data team members who need to work more effectively with data, and “external” data consumers from the larger organization who use products created by the data team.
We can use the JTBD framework to understand these customers’ jobs. For example, an analyst’s JTBD might be to provide the analytics and insights for these product prioritization decisions. Then, once you create a JTBD, you can create a list of the tasks it takes to achieve it.
✨ Spotlight: What is the JTBD framework? And why is it important for data teams?
JTBD is all about asking “What makes a product successful?” for your customers.
There is a lot written about this concept. But at its core, it’s about asking, what is the job you’re trying to do in every action you’re taking? This framework was popularized by Anthony Ulwick in 2005 with his paper “What Customers Want”. Later, Clayton Christensen connected this to business innovation through his experiment, the Milkshake Theory.
A fast food restaurant wanted to increase the sales of their milkshakes, so they tried a bunch of stuff: making them more chocolatey, chewier, cheaper, giving free samples, etc. Can you guess what worked?
Making milkshakes more convenient and filling! 😋
And this only happened from learning about the job that the milkshake was actually fulfilling – providing the customers with a satisfying, convenient breakfast during a commute. Here’s a short video from Clayton Christensen on this experiment and learnings from it about understanding the job to be done.
The JTBD framework is focused on understanding a customer’s specific goal, or “job”, and the thought process that leads customers to “hire” a product to complete that job.
Example of what a job looks like:
Bad job: Brush my teeth in the morning → action/activity
Good job: Keep my teeth healthy → solution
JTBD is a hard framework to get right, but it can help you build the kinds of products people love – whether it’s a milkshake for breakfast or dashboards for tracking your monthly metrics! For example, a Product Manager’s JTBD could be making product prioritization decisions to build a product that achieves business outcomes. Similarly, a Data Analyst’s JTBD could be providing analytics and insights to make product prioritization decisions.
Our team at Atlan spends a ton of time talking to data teams and practitioners, and a question we typically ask is, “What is the purpose and strategic imperative of what you’re doing?” And unfortunately, we rarely get a clear response. Often teams talk about running a refactoring project or implementing a new tool. These aren’t the “real JTBD”. A real JTBD is linked to business impact. For example, an analyst’s purpose is not to create a dashboard — instead, it’s to help the product make prioritization decisions.
There’s been a lot of talk about the ROI of data teams. We believe the starting point is driving clarity across the diverse personas in a data team about the purpose of their roles. Shout out to Emilie Schario for an excellent article on JTBD!
In a recent conversation with some of the data leaders as part of Atlan’s DataOps Leaders Program, we talked about this concept and how it can be applied in the data world. In this 8-minute video, I share some examples of implementing JTBD for data teams.
📚 More from my reading list
Data person: attorney at law by Stephen Bailey
Complexity: the new analytics frontier by Anna Filippova
Onboarding for data teams by Ben Rogojan (Seattle Data Guy)
The difficult life of a data lead by Mikkel Dengsøe
I’ve also added some more resources to my data stack reading list. If you haven’t checked out the list yet, you can find and bookmark it here.
See you next week!
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