Executives love dashboards, and why wouldn’t they? Single-screen “snapshots” of operational processes, marketing metrics, and key performance indicators (KPIs) can be visually elegant and intuitive. They show just-in-time views of what’s working and what isn’t — no need to wait for weekly or monthly reports from a centralized data center. A quick scan of a dashboard gives frontline managers transparency and, ideally, the opportunity to make rapid adjustments.
But dashboards aren’t the magic view some managers treat them as. Although they can convey snapshots of important measures, dashboards are poor at providing the nuance and context that effective data-driven decision making demands.
Most dashboards, though, only cover the first — describing what has happened. Moving from description to prediction to action requires knowledge of how the underlying data was generated, a deep understanding of the business context, and exceptional critical thinking skills on the part of the user to understand what the data does (and doesn’t) mean. Dashboards don’t provide any of this. Worse, the allure of the dashboard, that feeling that all the answers are there in real time, can be harmful. The simplicity and elegance can tempt managers to forget about the all-important nuances of data-driven decision making.
To get better at creating and using dashboards, think about these three drawbacks to data dashboards.
Every dashboard is built on a set of priorities and assumptions about what’s important. Many times those priorities are defined by IT, a design expert, or a consultant who deploys dashboards and doesn’t know the company that well. Sometimes, the priorities may even be the default measurements provided by the dashboard software.
In many of these cases, companies end up with official-looking views into data that doesn’t align with business priorities.
For instance, a small-business owner may have a dashboard that shows a moving average of his customers’ inter-purchase times. Is this information worthy of “front-page attention” each day? Probably not. Not only does the metric itself require significantly more information to drive action, but it simply doesn’t align with his goals and business model.
It should go without saying that all elements of a dashboard should be relevant and important. If the choice of what information to present in a dashboard is made without the input of those closest to the business context — whether through default software settings or what one person building the dashboard happens to think is important — it is highly unlikely that the dashboard will be maximally useful.
Too often, we think of analytics as representing some sort of unbiased and dispassionate truth. We equate “empirical” and “quantitative” with “objective.” This dangerous belief leads managers to track and even act on metrics simply because they appear on a dashboard — and, well, dashboards don’t lie, right?
Consider the manager tasked with maximizing sales leads. He helped design a simple view on his dashboard to see how leads are coming in to the company over time. He sees an upward sloping cyclical pattern:
Based on this data, the manager might focus on the period when leads coming in were highest — here, the second-to-last peak — and try to understand the conditions present during that peak period.
