Business analytics is becoming a hot trend. Many companies are embracing analytics, and pushing their internal teams to adopt it to glean insights and inform their strategies. Nevertheless, if we take a closer look at these implementations we find that most of them are failing to achieve that goal. Upon investigation, it has been reported that the challenges are mainly in user interaction; users cannot make sense of analytics and therefore do not act on these insights.
In order to help users easily understand the analytics output, we need to understand the way users organize and process information (cognitive style), then present analytics outcomes in a way that matches their cognitive style. A user’s cognitive style is either intuitive or analytical.
- Intuitive style: users think about the big picture, their thinking process is automatic and comes without any deliberate effort since they are unaware of the way they arrive at a decision.
- Analytical style: users are aware of their thinking process, they spend deliberate effort to assess the element of the situation, and they focus on parts of the situation.
Research shows that intuitive users can relate better and easily understand visual graphical elements, while analytical users can relate better and easily understand tables and numeric elements.
If you want to be serious about designing a dashboard that users can understand easily then you need to do advanced personalization. When designing the elements of your dashboard, do not make the decision to use a table, graph, chart, etc. just based on what fits the data best, but rather on what best fits the users thinking style. Take personalization an extra step and become more effective by letting users choose the dashboard style that fits the way they think and that speaks their language.
There are many ways you can implement this. One approach is to survey users before designing your dashboard, try to understand their cognitive style and the way they think. Cognitive style research shows that users working in the same functional area have similar cognitive styles, for example finance managers are known to be analytical rational users while marketing managers tend to be intuitive users. Then design dashboards that fit best the majority of your dashboard intended audience.
What if you do not have a dominant cognitive style among your users, then use our second approach. The second approach is to give users the flexibility to switch between different output styles. Give users a button where they can flip dashboard element from a chart into a table as illustrated in the example below.
Understanding users’ cognitive style and designing dashboards that fit their cognitive needs has many benefits. First, it enhances users’ problem solving efficiency and decision making process. Second, it can increase users’ acceptance of the dashboard and adoption of your tool. Third, this can shed light on why users abandon a dashboard or a BI tool.