This is the second of three posts in a series that discuss best practices for designing data products. This post focusses on narrowing all of the “blank canvas” options down to the right design. Check out part one of the series here.
The Starting Line
Folks who have data of any size in their possession also typically have some ideas and goals for what insights they want extracted from that data. While a sense of curiosity about data is never a bad thing, it’s often too broad to hone in on important insights that should be extracted from the data. Think about it like an artist who starts with a blank canvas: transforming the canvas into a beautiful work takes expertise, focus, and execution. In the case of properly crafting a great data product, by adhering to a carefully crafted process that functions to narrow the focus of the story that the data will ultimately tell, they’ll be able to go from broad ideas to authoring their very own data story that has purpose and direction.
How to do it
We use a process called Guided Story Design™. This process takes the infinite "blank canvas" options that every data visualization tool offers and narrows the options down to the one that best enables the target audience to act on the data. We do this by helping the data product author see the reporting challenge from the perspective of their users/audience, and then put the data into a context that is easily understood and acted upon. This all-important process of narrowing the purpose and function of the data application is accomplished in 3 steps.
Step 1: Identifying the Audience
The author’s attention when thinking about his or her data must be narrowed to focus on how the data will be used for the good of the business. In order for authors to truly consider and understand how their data will be consumed, they must step into the shoes of their users. These should be users that will have specific roles and goals within the data application.
For instance, consider a data product intended to enable a state chamber of commerce to better plan for future economic development in their state. Clearly identifying users as policy makers as opposed to investors or target corporations introduces the critical nuance of full disclosure as opposed to only show people where I’m the best. This can dramatically impact the focus and purpose of your data story.
This laser focus on the user persona gives the author a sense of context as to what the purpose of the data application is and encourages them to consider who the audience of their data application will be. When someone is forced to consider what his or her users’ goals are, the metrics and dimensions that will be most valuable to their audience come to the surface.
Step 2: Designing the App
Now that the focus and the goal of the application are in place, the design is the next key factor to make the data story one with which users want to engage. When we lay out the scope of a design, it includes three components: content, layout and flow, and styling. All three play an important role in connecting with the users and deserve intentional attention.
Picking the right content typically starts with identifying the metrics that support the goals of the audience. Once metrics are defined, specifying how to reveal additional detail about each one is the next step. For example, a metric about sales revenue might be most useful when trended across time. Or perhaps it’s more useful shown as a breakout across regions. Try to avoid showing as many breakdowns as you can think of since your target user most likely prefers just a few (or one) of your options (and more breakouts frequently lead to more confusion.)
Once you have the right content, it needs to be laid out in the proper sequence with the proper visual and interaction connections so that the user can understand it. Think of it like writing a thesis: there’s an introduction (typically key metrics), a body (the break outs for each metric you’ve identified), followed by a conclusion (either a summary of findings or perhaps a listing of lowest-level elements such as students or transactions). The key thing to remember: there should be a flow through the content that seems natural and leads the audience to an action.
The purposeful styling of the application should invite the user to engage and seek understanding while supporting any branding guidelines that are necessary. Company logos, color palettes, and relevant images should be embedded into the app to fulfill styling guidelines and to make the application feel personalized.
Step 3: First Eyes on the App
Once the data application is ready, publishing it to a small group of actual users that fit the target gives you the ability to test and refine your design. From this test group, insights about the effectiveness and usefulness of the application will come pouring in. The subset of users should be given a window of time (the amount of time can vary, but it’s important that there’s a specific period identified to keep things moving -- we feel a few weeks is typically enough) to explore the data and give their feedback while being able to test this feedback with fellow users. The conversations and direct feedback generated through this process will make the path forward for final touch-ups to the final product very clear. The feedback given by the users also functions as an affirmation to the author that the application they’ve put together is one that is actually useful to its target audience.
The author has gone from having a blank canvas to a data application that users interact with and give feedback on, all thanks to the Guided Story Design process that puts the focus of the application’s design onto its actual end-users. In the next post, we will take you through the steps required to take the application from a small subset of test users to a living, breathing product that supports thousands of users.
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