Designing Data Beyond the Dashboard

When we think about Business Intelligence (BI) solutions, countless metrics, endless reports, and complex dashboards come to mind. Companies invest hours upon hours data-diving in search of the ‘game-changing insights’ that will drive growth.

Research predicts that by 2022 only 20% of analytic insights will deliver business outcomes. The other 80% of BI solutions will fail to produce the necessary insights for users. There is more than enough data to choose from, but pinpointing relevant insights for the end-user to make informed business decisions is a significant challenge yet to be solved in many organizations.

But what if we designed a way to approach data differently? A way that is much more… human. Making the end-user’s experience our central focus and improved team behaviors our goal.

As one of the leaders of the User Experience (UX) team at Salesforce for over 15 years, I have learned the importance of educating our customers about design. We have powerful tools and templates to get users started, but it’s essential to apply design strategy when putting those to use.

What gets me excited when working with our customers is their ability to increase their company’s design knowledge. Their eyes light up when they realize that investing in their design education can help make them a ‘Design Hero’ within their company.

By applying design best practices, we can humanize how we collect, prioritize, and display data. We can embed analytics into users’ day-to-day work and make it easier for them to create and act on data-driven insights. Ultimately, this allows them to focus on strengthening relationships with their customers.

It’s time to rethink dashboards as we know them.

Designing data experiences

When we think about traditional reports and dashboards, we think about individual static documents. Someone authors (or creates) them and pushes them out there for someone else to consume.

Many analytics projects fail because their outputs lack relevance and approachability. Dashboards are full of information spread across dense grids of charts, but they’re often too complicated for users to interpret and act on.

A typical dashboard looks something like this:

It consists of many metrics, charts, and graphs, but these complex dashboards place the onus to discover insights on the user.

The alternative? An intelligent analytics apps model centered around the user experience.

The best analytics apps are seamlessly embedded where your users already do their daily work and push the right insights at the right time. This model offers access to relevant data in context, enabling users to take action without leaving their workflow.

Apps have dynamic layers that provide information in smaller, focused bites with options for users to drill in where they need details, and zoom out for the big picture when required.

Apps are also device-appropriate, making them portable and available to users anywhere — desktop, laptop, smartphone.

The first step toward developing an analytics app experience is to understand the goals of business users clearly. It’s essential to understand who the data is meant to serve and what goals they are trying to achieve. You have to know what questions the data needs to answer to guide users towards the right answers and actions.

In short: get to know the person on the other side of the screen.

Understand your user’s goals

To design a user-focused experience and better empathize with your users, bring them into the design process. Through qualitative research, get to know them — talk to them, seek to understand their challenges and objectives. From there, you can craft personas that form a shared understanding across your team and a strong base for the rest of the design process.

Learn how to use UX Personas for Salesforce on Trailhead.

Utilizing personas will increase your success in building trust with your users because the data solutions you deliver will respond to their needs.

And when you understand what your users are working toward, you can also spot the obstacles they face in getting there. One of the biggest pitfalls to navigate is a lack of trust in the data itself. According to Experian, 40% of data-driven business leaders say individuals do not trust data insights.

Often the problem is that there’s no single source of truth; meaning, data isn’t always integrated and synchronized. So, it’s difficult for users to be confident in the information they find. Designing an analytics experience that offers users relevant insight in context, and allows them to take action in real-time, will lead to higher adoption and trust in data.

Once you understand your users’ goals, the next step is to figure out what questions they’ll ask the data to answer.

Shift from brainstorming to question-storming

The challenge is to get into your user’s mind to make sure you’re delivering relevant and helpful information. What do they need to learn from the data? What do they want to do with the insights?

One productive approach is something we call ‘question-storming’ (versus brainstorming). It’s the process of identifying the questions users need the data to answer to know what actions to take.

If you know the questions, then you can begin envisioning how the answers should appear. Switching from brainstorming metrics to brainstorming questions allows you to see through users’ eyes as they move throughout their day.

So, how does question-storming work?

With a target persona in mind, or by identifying an individual within a particular user group, collaboratively go through a series of questions around the data. You can do this activity informally with sticky notes or on a whiteboard.

After the questions are written out and agreed on, the next step is to group the similar ones under themes and then rank them under those themes.

After identifying and prioritizing the questions for the data to answer, you can then start to envision the answers and what actions the user will take with that knowledge.

Next, you’ll want to test this with users. Get their input both on the prioritization of questions as well as the proposed visualizations and actions. Complete this activity with paper and pen to avoid costly mistakes early in the design process.

If you find your designs feeling complex or trying to answer too many questions at once, refer back to your question prioritization and utilize techniques such as progressive disclosure and dynamic page layouts. These techniques hide or reveal more data with a click and only show the most critical answers first.

At the end of this initial process of question-storming and sketches, you’ll have the building blocks to develop an analytics apps experience. You can now dynamically embed discrete and targeted visualizations in the context of appropriate workflows.

Design around your user’s questions

When you shift from providing static reports and dashboards to actionable apps prioritized around your user’s top questions, you relieve the need to swap between systems. You offer more guidance in context and enable them to do their jobs better and faster.

The insights are contextual, timely, and relevant because you designed them that way.

When users are empowered to make informed decisions about what to do, when, and why, they build deeper relationships with customers and deliver more value to their organizations.

After all, analytics and the insights they provide are only as good as the organizational behaviors they are able to drive.

. . .

The role of analytics in business has grown and will continue to evolve. It’s becoming more fluid, more embedded in both workflows and the conversations between coworkers by which companies operate. All of this will help people and organizations make better data-driven decisions because their experiences invite data to become active members of every team, conversation, and action.

For more information on design best practices for analytics, check out this interactive training module on Trailhead for free! To discover how design can help build better relationships with employees, customers, and communities, check out the Relationship Design module.

Thanks to Eddie Picot, JD Vogt, Qianwen Dong, Madeline Davis & Crystal Garrett

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