First of all, a starting point definition of “data products” is important, so here is mine.

Data products are any created tool that include a couple distinct qualities:

• They typically gather and combine two or more distinct, external data sources, that previously were not easily combined.
• They perform a few specific jobs targeted at very few audience profiles.

Data products share many attributes with other data tools, but I wanted to point out a couple that are different. I’d enjoy hearing others’ thoughts on the distinct qualities they have in mind.

Now, how are they used?

This question is incredibly broad. It is like asking, “What is a tool for?” I’ll share what comes to mind. I’d say data products are best used for people more interested in accomplishing tasks than exploring data. Similar to how smartphones and mobile apps have focused an incredible amount of data into bite-size tools, data products are there when you need them, and you know exactly what you are going to use them for.

Data products have a clear task in mind and integrate complementary workflow tools to accomplish it.

The more broad the potential output of the tool (think Photoshop and Tableau), the less likely anyone can open it and get immediate usefulness from it. In that sense, tools with broad outputs are great for experts, but they will never accomplish one or two specific tasks as efficiently and consistently as another tool specifically designed to do those two things.

Data products enable tasks by supporting next steps. Consider a mobile app for directions, where the task could be directions to a restaurant for dinner. The overall goal of eating out is more achievable and seamless by including a “Create reservation with OpenTable” button. But what if you were a teacher and an app was about “finding directions” to the best plan for the school year? While being navigated through useful data views, you would want a “Save to Plan” button that ties the findings to the next stage of prioritization. These connected actions create a clear and useful workflow in the user’s mental model.

With clear tasks in mind, data products are used to expand your available workforce to accomplish the job.

Knowing the few tasks allows data product authors to present those tasks in a way that scale beyond the few people that were originally accomplishing the tasks.

Before GPS, who could reliably navigate a map to a location? There definitely used to be some people you didn’t want driving the car! Now, at least when it comes to sense of direction, anyone can drive. Likewise, a data report that delivers all the updates on how your school is performing may be less useful than a data product that informs a teacher on the best room layout for their room and student profiles.

Because the workforce is expanded, data products enrich insights from people on the front lines, with the most direct impact on your business.

Many businesses spend an incredible amount of money inefficiently solving problems from the top down. They are trying to answer questions their workers likely already have the answers to, yet there are practically no viable communication channels that invite these insights, at the time and point of need. Delivering specific data products to frontline workers delivers more than an answer. It sheds light on their situation with context and organizational impact for the decision they are about to make. This context provides recommendation and freedom to make the best choice for their scenario. A growing sense of ownership, responsibility, and value is transferred back to workers.

A key here is viewing data products not purely as an output tool for findings, but also an input tool for enriching those findings with the eyes-on-the-ground knowledge that workers have. This is a fundamental cultural shift from how reporting is viewed today in it’s typical one-way approach. This shift is towards two-way conversations.

Data products introduce communication channels for unforeseen data-driven insights.

In the earlier example, a teacher could have a data product that is focused on student seat assignments and room layout. The goal here is to improve classroom participation, but what if it also gets them thinking about the layout and positioning of content on their walls? And what if reimagining the walls stirs them to engage students directly for their input on what is engaging? The teacher and students could collaborate in solving classroom engagement in ways unforeseen by the original solution that was only focused on desk assignments and layout. Still, because the data product is clear about the problem it was trying to solve (classroom engagement), it provides an on-ramp for solving that problem in unexpected ways.

Finally, data products are ripe for collaborative problem solving.

Let’s continue the school example. Teachers, students, and staff could have a new communication channel through very specific data-driven initiatives, tied to broader strategic goals (i.e. student engagement). With this in place, leadership doesn’t need to know all the answers or even questions to ask to start making and tracking impact on this goal. They simply need to set goals, maintain an open data-driven collaboration channel, and review impact from strategies and insights bubbling up from their workforce.

When the goals are clear, collaboration channels for new insights are open, and the product is connected to workforce peers, solutions to specific and unforeseen problems can be addressed by frontline workers. Leadership spends less time identifying all the needs and solutions, and more time moderating and steering workforce solutions towards organizational goals. Problems are solved locally with a personal touch and creative solutions are more quickly tested and shared with peer departments.

In summary, data products...

• Have clear tasks in mind and integrate complementary workflow tools.
• Expand your available workforce to accomplish those tasks.
• Enrich insights from people on the front lines.
• Introduce communication channels for unforeseen data-driven insights.
• Are ripe for collaborative problem solving.

I’d love to hear how others perceive data products, and in particular, how people address the related cultural shifts in organizations.