From dashboards to decision boards: what growing data teams need to know

The fundamental question that all scientists—from elementary science students to NASA engineers and PhDs—want to answer has evolved little since early philosophers began questioning the world around them. Evident in the constant chatter of toddlers exploring their surroundings with new eyes, it is human nature to want to know ‘why’.

This curiosity does not leave us as we grow up; it changes and evolves as the scale of our problems changes. In business, we don’t ask our teams why the sky is blue, but we do ask why a certain combination of strategies is the best approach to achieve our desired goals. We start with ‘why’, plot the best course of action, track and analyze KPIs and adjust them based on the insights we find, before doing it all over again. In our ever-changing business environment, executive leaders strive to understand their business data, quickly process it and execute strategies without slowing down innovation. But this process cannot happen without the support of data-aware teams.

As companies mature in their analytics journey, their teams must evolve to present data in succinct ways that fit the context and message of the information being conveyed. To help business practitioners understand when it is appropriate to use which type of data visualization, we will break down each type of data visualization. We’ll also explain when is the best time to implement it as you build a dashboard and strengthen your visual vocabulary – all in the context of the distinction between decision boards and dashboards.

This practice is not limited to data science-heavy industries and vertical markets. CIOs, CFOs, CMOs, and even Chief Data Officers can benefit from improving the way their teams present and interpret data.

To understand how to work towards implementing decision boards, we need to understand where we started: dashboards. By now, we’re all too familiar with analytics dashboards, including the standard integrated reporting platforms of the digital tools we know and love, such as Google Analytics and Hubspot. They are effective at providing a high-level snapshot of performance broken down by category (day of the week, location, age, gender), and they are visually appealing, but require a presenter to contextualize the data to basic question to answer: Why does this matter?

Decision boards, on the other hand, are fluid. They aggregate the data from cross-organizational channels to paint a clear, easy-to-follow picture that goes beyond descriptive statistics. These are often custom builds designed for the specific needs of an organization. Depending on the level of analysis maturity and design resources, decision boards can also illustrate diagnostic statistics, or why something happened; predictive statistics, or what is likely to happen; and prescriptive statistics, or what to do next. Making the jump from dashboards to decision boards requires a basic understanding of design thinking, which when integrated into an organization’s culture can enhance its analytics and reporting capabilities.

Building decision boards

The most effective decision boards are created when we implement design thinking. Loved by big companies like Google and Apple, and old academic institutions like Harvard, the methodical process of design thinking means we always get to the heart of the problem quickly. It’s efficient and built around the people who will use it – two main components of the insights we’re trying to build. As part of design thinking, teams can assess which of the four major metric types (or combinations thereof) are needed to build a decision board.

• Descriptive statistics: While not inherently valuable for decision making, descriptive statistics provide a snapshot of what has happened or is currently happening. They are a real-time look at how multiple variables work together. Charts and graphs that illustrate descriptive statistics include:
  • Distribution (box plots, histograms, dot plots)
  • Partial (pie charts, waterfalls, stacked column charts)
  • Correlation (scatter plots, XY heatmaps, bubble charts)
• Diagnostic Statistics: Diagnostic charts enable decision makers to climb from the descriptive statistics to the ‘why’. In decision boards, diagnostic charts are coupled with the associated descriptive statistics so that users can draw logical conclusions when they click on the data. Displaying diagnostic information is more about the data flow than the structure of the graph. When choosing which chart to use, it is important to evaluate what specific questions you are trying to answer. The following structures are most often used for diagnostic charts:
  • Flow (chord diagrams, networks, Sankey diagrams)
  • Distribution (barcode plots, cumulative curves, population pyramids)
• Predictive Statistics: Perhaps the easiest to understand, predictive charts predict what will happen based on the existing data set. These metrics are critical in making the transition from dashboards to decision boards and, if done correctly, should map out a clear path to the next steps.
  • Correlation (line+column, scatter chart, bubble chart)
  • Change over time (line chart, connected scatter chart, area)
  • Deviation (diverging bar, surplus/deficit)
• Prescriptive Metrics: The drift in prescriptive metrics is tilting the scale from dashboards to real decision boards. These views of data indicate the next steps for business leaders. These charts require the most advanced data science knowledge and use AI and ML to optimize performance.

When building decision boards, focus on flow. Think about how your information will be processed and try to create the most logical structure for your boards. This is where the basics of UX/UI design benefit your teams the most.

The learning curve for creating charts can be difficult, but not so difficult that a general business user cannot master it over time. To help you build your decision board, LatentView has a Visual Vocabulary, an open source guide to creating custom charts in Tableau. LatentView will periodically release step-by-step tutorials that guide users through the use of Tableau filters. The first episode includes data source and extract filters.

As your business progresses on its data analytics journey, there are a few key pillars to remember. First, make your decision boards easily accessible to the right stakeholders. When properly executed, these boards serve as an ongoing resource intended to be consulted on a regular basis rather than presented at quarterly meetings. This is the main reason why decision boards are a more effective tool than previous iterations of data visualization.

Second, keep asking for feedback and fine-tune the structure of your decision boards. The composition of your boards will evolve according to your business needs.

Finally, be relentless in your search for the ‘why’. It will make your predictive charts stronger, more intuitive and more durable in the long run. And by the way… the sky is blue because the gases of our atmosphere refract white light from the sun and scatter blue light waves (the shortest and fastest of the color spectrum) across the sky during the day.

Chart types index

Descriptive Statistics

Bubble chart: Gives us a glimpse of the current state of the company. This chart summarizes sales (on the y-axis) versus profit (on the x-axis) for various subcategories. The size of the bubble is proportional to the size of the sale and the color represents the respective category to which each subcategory belongs. A quick look shows that the ‘Tables’ subcategory is at the bottom of earnings despite a reasonable number of sales.

Waterfall Chart: Another way to show positive and negative factors that affect the overall profit, broken down by subcategories. Using the key as a guideline, the example below shows that the ‘Bookcases’ and ‘Tables’ subcategories are largely responsible for lost profits.

Diagnostic Statistics

Sankey Chart: Visualizes the data flow. In the waterfall chart example above, we saw that both sales and profits for categories that fall under “technology” were higher compared to other office supplies. To understand the main contributors to this category, the following chart clearly shows that phones and machines are responsible for the majority of sales. (Note: The width of the arrows represents the size of the metric being discussed.)

Funnel Chart: Helps with drill-down analysis and answers the ‘why?’ Funnel diagrams help us understand things such as where the spill is and which stage of the process we should focus on for process/product improvement. In the example below, we see a 20% drop in marketing to qualified leads in the funnel and an approximately 56% drop (indicating high leakage) in pursuing those leads through closure.

Predictive Statistics

In the snapshot below, quarterly sales show an exponentially increasing trend over several years. It is also good to know what the future trend might look like. Therefore, the forecast chart plays an invaluable role in certain cases. Sales forecasting helps companies estimate factors such as resource allocation or growing markets.

Prescribed Statistics

Cluster diagram: Helps us understand different types of clusters formed based on Tableau’s backend k-means algorithm. With the revenue versus profit illustrated below, cluster 1 shows low profit and low revenue, usually with the most number of data points; cluster 2 shows moderate turnover and profit; and cluster 3 represents maximum profit and turnover. Further in-depth analysis of the cluster 1 data would provide clarity for further action needed, such as improving marketing strategy or financial management.

Boobesh Ramadurai is the director of data and analytics at LatentView Analysis.