User needs and data tasks

Understanding your audience and what they want to do with the data (their tasks) is a crucial step in designing a good data product.

Understand your audience

Ask yourself: Who will be using the data?

Because Health Infobase is public, your intended audience may not be your only audience. For example, maybe you intend for health specialists to use the data. But, if the topic interests the general public, they may also use your data products. Make sure your data products work for all audiences, and not just your intended one.

Data tasks

These questions can help you explore the tasks your product needs to help people accomplish:

• What will people try to do with the data?
• What do they want to get out of the data?
• What would they be able to do with the data that they otherwise couldn't do?

Understanding this is crucial to select the right visualization technique.

Job stories

Job stories are helpful to identify how, in certain situations, people can use the data you're publishing to arrive at a desired outcome.

Job stories follow this format:

“When I (situation), I want to (motivation), so I can (desired outcome).”

Do a job stories workshop with everyone involved to reinforce your understanding of how and why someone may want to use your data.

A wide range of data tasks

Different audiences may have different tasks they want to perform with the data.

We can visualize that wide range of tasks on a continuum, from top-down explanation (provided by us) to self-directed exploration (performed by the user):

A range of top tasks

The range of data tasks can be understood as a continuum, from simple to complex.

On the left of the continuum are simple and explanatory tasks likely to be performed by most people, including the general public:

• Key insights
• Data storytelling
• Infographics and blog
• Top-down interpretation

Tasks performed by a significant portion of users are shown in the middle of the continuum:

• Interactive maps and charts
• Self-exploration of data
• Complex data visualization
• Self-interpretation

Complex and exploratory tasks performed by a minority of users (such as experts) are shown on the right of the continuum:

• Raw data
• Technical details
• Sources
• API

Tell a story with data

We have the responsibility to contextualize the data we provide to the public, while being transparent about the data itself. What’s the story in the data? What are the key takeaways?

It’s important to extract key insights from the data and present it to people.

Start your product with the key insights (the story the data tells), move on to data exploration tasks (if applicable), and finish by making the raw data available.

Be responsible: Don’t tell a story not supported by the data.