Modern Business Intelligence (BI) solutions such as Tableau and Qlik have brought vast levels of customization to the table. Yet, while most users appreciate the ability to design their own dashboards, the allure of convenience means that a majority of them still prefer that their dashboards be designed for them. Building a dashboard tuned to someone else’s preferences is thus a task that most data-driven organizations face. And while it isn’t the most simple task to achieve, the need behind the most effective dashboards remains the same: maximum information displayed on a single screen and the minimal amount of time.
Achieving the aforementioned requires the adoption of certain design practices. These are discussed below.
There is a very real possibility that an organization possesses large swathes of data stored in different locations. The first step requires that all data relevant to the firm’s workings be connected so that any information can be accessed by any actor. This “connecting” process can be made even more seamless by employing cloud-based solutions. The first step is incomplete without identifying the right data and users. Different users in a firm have different roles, and each user has different priorities. Grouping them based on their similarities helps in creating dashboards to best fit each group.
Once the data has been connected and the user groups have been sorted, the next step in ensuring effective dashboards is to ensure that each group of users is furnished with data that is relevant only to their roles and functionality.
Not all data related to a user group may be useful for critical for regular operations. Displaying too much data (clutter or irrelevant data) on the dashboard may prevent easy access to important data, or worse, not leave sufficient space for displaying the important or critical data. Thus, it is business-critical to take all data relevant to a user group and further refine it to ensure that only the metrics/data that truly matter to regular operations are displayed.
Business-related metrics, for example, may not be useful to the technical teams and so on. It is also immensely important to make sure that least amount of data with maximum relevance is displayed. Displaying too much data on the dashboard may prevent easy access to important data or worse - not leave sufficient space for important data to be displayed.
While it may sound counter-intuitive that “minimalist” and “diverse” visualizations are both desired, it is very much true. Cluttering the screen with too many charts and a wide variety of colors may seem useful at first, but they are often distracting and unproductive. The best dashboards use minimal charts with simple color palettes to ensure that they are easy to process.
Furthermore, the visualizations should be diverse: not every form of data needs to be fit into static pie charts or bar graphs. Depending on the data being displayed, a visualization that best conveys the meaning behind the data needs to be selected. Rather than displaying purely static charts, interactive charts may also be employed if they serve the purpose better.
Factoring in exceptional data is of paramount importance. Data representing any abnormal or exceptional occurrences is often more critical than data representing normalcy. Dashboards should thus be designed to ensure that abnormal occurrences are given their fair share of screen space and time on the dashboard.
Effective dashboard development demands an iterative development approach where requirements are gathered, prototypes are built, feedback is taken, and overall design is refined repeatedly until the design is completely satisfactory. It may be because the target individual/audience may have not correctly furnished developers with all their requirements, or it may be because of the changing nature of the business. However, considering change is inevitable, it also needs to be taken into account during development.
In addition to the principles discussed above, designing effective dashboards also means avoiding certain missteps. Dashboards should not try to be overly ambitious or try to provide too many details and drill-down options. Metrics that individuals accessing the dashboard may struggle to understand should be avoided. Finally, the maintenance efforts needed to maintain the dashboard should never be underestimated.