One of the key questions I typically ask clients is “What business decision do you need to make with this information?” or “How does this information shape your decision-making process?”
Sometimes clients bristle at this question. They may not quite know how to respond – either because they are simply used to seeing what they are used to seeing or because the question comes across as though I am questioning the value of their role. Other times, clients know exactly how to respond. They say something like, “We need to decide if our new marketing efforts are yielding results. If they are not, then we need to change course – this data should be able to inform whether or not our targets are being met.” Another example might be, “The data we hope to see on this dashboard will tell us if staff billable hours are in line with expected workloads. We hope to use this information to allocate staff resources appropriately.”
I’ve realized that while this is still an important question to ask, if the client doesn’t have an answer ready, it doesn’t necessarily mean that the data they are asking for is irrelevant. Instead, I’ve found that the need for data can typically be grouped into one of three categories. Each of these categories provides a different perspective based on how the data may be used.
The 3 Types of Needs from Dashboards
The first category is what I call “contextual.”
This is when the client or end-user can’t really articulate why they need the data on their dashboard, but they know it needs to be there. I call this a contextual need as the end-user is likely to keep some aspect of the data in the back of their mind while looking at something else. Perhaps the user won’t actually use the metric “number of website visitors” for anything, but this figure may provide helpful context for a metric such as conversion rate.
Opposite this category is what I refer to as “functional.”
A functional need is data that has a specific and functional purpose – the data is directly informing a decision or is part of an ongoing performance goal. An example of this would be the specific return on investment from a marketing campaign. Looking at these ROI points over time can tell you if your marketing strategies have been effective – or if something needs to be revised. The functional aspect is that these data points provide the information to determine if a specific action should be taken.
Lying somewhere between these two categories is “strategic.”
Strategic data points are the Goldilocks of dashboard elements. They typically aren’t so specific as a functional element but do go a layer or two deeper than something providing background context. In a medical setting, an example of a strategic data element may be a patient’s weight. By itself, a patient’s weight isn’t something so specific as to be a problem (unless it is well outside the bounds of normal) – but at the same time, weight is likely not a purely contextual or environmental data element. This is likely why whenever you have a doctor appointment, some of the first measurements taken are weight and other basic vital statistics. Then during the appointment, you may discuss specific ailments (functional data) as well as typical behaviors (contextual). Strategic data, therefore, serves as an important connective tissue between broader contextual data elements and the fine details of functional data elements.
Consider the following diagram:
Final Thoughts
I find that this framework helps when assembling various dashboard components. When working with clients, I try to bring balance to their dashboard requests. If a client wants mostly functional data points, I may ask them about the different contexts in which the data points could yield different interpretations. Similarly, if a client seems focused on seeing only the high-level metrics associated with their company or department, I will likely ask them about the specific drivers or outputs directly related to those KPIs.
This paradigm isn’t meant to be used as an exclusive approach when considering your next dashboard build. However, when used with other techniques (such as the dichotomy between ‘exploratory’ and ‘explanatory’), these terms can help you design a more insightful dashboard that users find just what they need while providing the right level of context and background.
Ready to add expertise to your data visualization projects? We’re ready to help!
