A dashboard is a visual snapshot of business performance using KPIs (key performance indicators) to help users make smarter, data-driven decisions. An effective dashboard simplifies the visual representation of complex data and helps stakeholders understand, analyze, and present key insights at a glance. At the core, the objective of a dashboard is to make complex information accessible and easy to digest. But building a good dashboard for the business involves addressing both the design patterns and anti-patterns associated with four key building blocks: KPIs, data, formula, and visuals. A pattern is an idea of how to solve a problem. It is simply a known-to-work solution. The anti-pattern is the opposite of a pattern. It is an idea of how not to solve a problem. With this backdrop, this article looks at three key commandments for having effective dashboards. 

Commandment 1: Understand the users and their insight needs.

The first key commandment of an effective dashboard is to understand its purpose. Purpose comes from knowing your stakeholders and their objectives. What kind of business questions do they care about? What decisions are at stake? What is the entity needed to measure performance management? In this regard, knowing the interests and drivers of the business users who are consuming the dashboards should be based on the KPIs. In dashboard design, simplicity is the key. Less is more in dashboard design, and the starting point in building an effective dashboard is deciding on the number of KPIs. Research has shown that the number of information pieces that the human mind can hold and process is seven +/- two. In other words, most adults can store and process between five and nine items in their short-term memory [1]. Hence, keep the number of KPIs and the associated visuals on every screen in the dashboard to seven +/- two using a combination of leading and lagging trends.

Commandment 2: Leverage visuals.

They say a picture is worth a thousand words. This is because the human brain processes data and insights using charts or graphs much more easily than spreadsheets or reports. Basically, humans process information based on what we see. Sixty-five percent of human beings are visual learners [2]. Selection of visuals should be based on two parameters:

Data type: The data types to be used in the visual must be nominal, ordinal, and numeric. 

• Nominal data are used for labeling or categorizing data. It does not involve a numerical value and hence no statistical calculations are possible with nominal data. Examples of nominal data are gender, description, customer address, and the like.

• Ordinal or ranked data is the order of the values, where the differences between each one are not really known. Common examples here are ranking companies based on market capitalization, vendor payment terms, customer satisfaction scores, delivery priority, and so on. 

• Numeric data that are numeric values are amenable to statistical techniques and help in consistent measurement.

Purpose: The purpose is based on distribution, composition, relationship, trend, and comparison.  

• Visuals on data distribution on a single variable including how items are distributed into different parts. For example, in a healthcare organization, patients treated every month can be shown using the bar chart.

• Composition is single or multiple variables in reference to the whole. For instance, the percentage of male and female patients can be shown on a pie chart.

• Relationship is to show the association or correlation between two variables. The association between patient age and cholesterol levels can be depicted using the scatter plot.

• Trend visuals are for indicating the trend of a series of data in a given time period. For example, the amount of time spent by clinicians every day on training patients can be presented using the line or run chart.

• Comparison visuals are used to compare data sets pertaining to different entities, categories, and events. The number of patients treated by the facility in 2021 and 2022 can be compared using the spider chart.

While there are many types of visuals, avoid using fancy charts. Bar charts, pie charts, histograms, spider charts, and run charts can meet the majority of your needs. Aesthetics – the use of the right visual size, typography, flow, and color – can significantly bolster data visualization. Some recommendations include:

• Apply the golden ratio (1:1.62), the proportion at which different-sized elements are found to be the most aesthetically pleasing for human eyes.
• The recommended typography is sans serif (such as Trebuchet) for headings and serif (such as Georgia) for details.
• The positioning of the visual object should mimic the way people read: The “F” pattern, starting at the top of the page, moving across it and then down.
• Too many colors can be confusing. Stick to about three and five colors.

Commandment 3: Strive for self-serve analytics.

Once you address the design pertaining to the four key building blocks of the dashboard – i.e., KPIs, data, formula, and visuals – the next step is rendering or delivering the dashboard to the users. If the need is quick ad-hoc reporting, the self-serve analytics platform can help business users perform queries and generate insights with minimal IT support. Basically, any analytics platform is only as valuable as the data available to it. If the data is sourced from multiple transactional systems, it is better to implement a data warehouse and source the data for the dashboard from this canonical system. However, self-serve analytics, which is often associated with data democratization, needs to be balanced with data protection. Finally, the dashboard needs to be optimized for multiple devices, i.e., desktop, mobile, or tablet.

In today’s data-centric economy, analytics is a key enabler that transforms data into a business asset by providing insights to measure and improve business performance. Organizations of all types are looking at leveraging data and analytics (D&A) for improved business performance and outcomes. D&A can provide EBITDA (earnings before interest, taxes, depreciation, and amortization) increases of up to 25%, according to a McKinsey report [3]. Analytics is enabled by reports and dashboards. While reports are used for in-depth analysis and exploration of data to answer complex business questions, dashboards are used for high-level monitoring, often in near real time, providing a consolidated view of business performance.

References

1. Miller, George, “The Magical Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information”, Psychological Review, 1956.
2. forbes.com/sites/tjmccue/2013/01/08/what-is-an-infographic-and-ways-to-make-it-go-viral/?sh=569f0a6d7272
3. mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/insights-to-impact-creating-and-sustaining-data-driven-commercial-growth