Five Principles for Creating Effective Data Visualizations

by   |   August 14, 2014 5:30 am   |   1 Comments

Linda Luu Five Principles for Creating Effective Data Visualizations

Linda Luu, ThoughtWorks

In the race to make data consumable, we’re experiencing an explosion of highly effective, engaging, and creative visualizations that connect with consumers in unique and interesting ways.

The need to make data accessible to decision makers raises the question of how to make visualizations as effective as possible. And how do you know if you are on the right track to making an impact?

Lean User Experience (UX) techniques enable teams to focus on the right thing to build using an iterative and user-centered approach. Fundamental to Lean UX is a culture of collaboration between cross-functional individuals and the use of fast user feedback loops to arrive at a solution that meets the needs of business, customers, and the technology platform it’s designed for rather than pixel-perfect pictures that describe the user experience in detail. Typically, pixel-perfect pictures require months to produce and are handed over to development teams without consideration for the technology platform. In addition, user feedback often is incorporated only after months of development is complete, which results in costly rework.

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Here are five principles for creating effective data visualizations using Lean UX techniques, told through a recent visualization project I worked on that involved taking years of hospital spend data to find areas for cost optimization at hospitals.

1. Be Open to Discovering New Insights

Effective data visualizations enable the user to discover unexpected patterns and invite a different perspective of the data. During the first few iterations of looking at the data, we used visualization tools to quickly identify patterns and decide on a direction.

During this discovery process, it was tempting to focus on the goals of the iteration and avoid being distracted by unexpected insights. For example, in the early iterations, we found an issue in the data-collection process for one customer that opened up opportunities to improve the type of data being collected. This resulted in further operational savings that wouldn’t have been discovered if the team was focused only on the task at hand.

Having a clear and disciplined prioritization process enabled the team to feel comfortable exploring new opportunities and insights that weren’t directly related to the scope of the work. As new insights were uncovered, we huddled as a project team to understand the impact and prioritized any additional scope based on quantifiable business value.

2. Think Big, But Start Small

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User journey wall. The horizontal lines separate features into priority buckets across the user journey. (Click to enlarge)

Visualizing years of hospital spend data was no easy feat. Our users needed quick solutions to a very painful process that was costing time and lost business. We kick-started the project with a collaborative workshop to understand the quality of the data, the business objectives, and what users needed from the visualization. Together we sketched out the high-level, end-to-end user journey and identified a thin slice of the project by answering the question: What’s the smallest visualization we might build that gets the data into the hands of users and will generate the most learnings for the project?

We kept this high-level journey alive over the course of the project to remind ourselves what we were creating and to avoid going deep in areas that weren’t necessary for the specific iteration goals.

3. Design for Your User

User journeys and prototyping helped us define the user goals, as well as which vital pieces of information were needed and at what point in the process. During the first iteration of the visualizations, we realized how little screen real estate we had. Information was tightly packed – the screen had too much information and the fonts were too small. We created a monster. It was back to the drawing board.

We explored tree maps and pie charts as ways to visualize the potential cost savings by product category. Lots of discrete clusters made pie charts ineffective. When we tested the tree maps, users found it difficult to arrive at a clear decision when comparing across product categories. Users also grappled to understand more complex visualizations, as they were mostly accustomed to Excel-type visualizations.

4. Prototype to Identify Needs

Each visualization was designed and created by a small cross-functional team that set out to find a solution based on three questions:

  • Will customers use it?
  • Can we build it?
  • Does it meet business needs?

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Prototyping and evaluating ideas based on three lenses of Business, Customer, and Delivery. (Click to enlarge)

We used a technique called “sketch to code” to get the ideas and opinions of users and business stakeholders into a visual story that became the starting point for designs. Sketch to code is a low-tech design technique that can extract ideas quickly. It facilitated the requirements analysis because visuals helped create a shared understanding of what we were building beyond words. We then took those sketches and iterated on the design in code. This enabled the team to see a working version on the day the sketches were created. We had short, quick feedback huddles with the product manager and users to refine the visualization. Sometimes we threw it away and chose a different visualization. This process enabled the team to learn early and pivot to a different direction when we discovered new insights from the data.

5. Obtain Feedback Early and Often

One of the common misconceptions about user testing is that the user will have all the answers to exactly what they should see and how the system will work. In the words attributed to Henry Ford, “If I had asked my customers what they wanted they would have told me faster horses.”

So why do we get feedback from users at all? User testing is about going beyond what your users tell you to uncover their needs, both met and unmet. It’s up to the project team to come up with solutions based on the needs identified during user testing.

For example, our first user sketching session resulted in Excel tables with lots of filters and sorting. At the risk of rebuilding Excel, we revisited the user goals and did a collaborative sketching session to define the types of data points required. Then we did a power-ideation session to come up with alternative visual ways to arrive at the user goals that were more effective than tables of data.

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Visualizing hospital spend. (Click to enlarge)

From user testing, we learned that the level of technical savviness influenced the visualizations we adopted. Visualizations had to be intuitive – users needed the ability to show visualizations to aid a sales conversation. Discoverability of insights was less of a concern for the visualizations. The ability to scan the results across product categories to make fast business decisions was more important for the first release.

After a few iterations and user testing, we landed on stacked bullet charts as the most effective way to visualize product spend across categories. Vertical alignment enabled users to quickly understand which product would generate the most cost savings. Leveraging the vertical scroll enabled users to quickly compare results across many product categories without the need to constantly flip across screens.

Successful visualizations consider user needs, business needs, and the technology platform. It’s easy to create visualizations that are interesting but not effective for the users who are consuming the insights. I hope these five principles get you on the right track to creating effective visualizations.

Linda Luu manages ThoughtWorks’ Big Data Analytics practice across North America. She applies design thinking to solving complex business problems. Connect with her on Twitter @lindakluu.


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One Comment

  1. Sunder
    Posted August 23, 2014 at 3:37 am | Permalink

    Hi Linda, thanks for sharing a very insightful information relevant to topics of such importance. appropriate Data Visualization adds great value to complex analytical operations…here is one more articles i have come across on data visualization.

    Infographhics in Business Intelligence

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