As data analyses get more complex, how can companies best communicate results to ensure that decision makers have a proper grasp of the data’s implications?
In an increasingly complex economic and social environment, access to vast amounts of data and information can help organizations and governments make better policies, predictions and decisions. Indeed, more and more decision makers rely on statistical findings and data-based decision models when tackling problems and forming strategies. Scientists, researchers, technologists and journalists have all been monitoring this tendency, trying to understand when and how this approach is most useful and effective.1
So far, discussions have centered mainly on analysis: data collection, technological infrastructures and statistical methods. Yet another vital issue receives far less scrutiny: how analytical results are communicated to decision makers. As the amount of data gets bigger and analyses grow more complex, how can analysts best communicate results to ensure that decision makers have a proper understanding of their implications?
Communicating Statistical Information
However well executed, the usefulness of an analysis depends on how the results are understood by the intended audience. Consider a patient visiting a doctor about an illness. Arguably, the most important task is the diagnosis of the disease, as this can lead to choosing an appropriate treatment. Yet even if the final decision lies with the patient, the chosen treatment may depend on how the doctor communicates different options to the patient. The same is true when an investor consults a financial expert or a manager seeks the services of a consulting firm.
Data science, like medical diagnostics or scientific research, lies in the hands of expert analysts who must explain their findings to executive decision makers who are often less knowledgeable about formal, statistical reasoning. Yet many behavioral experiments have shown that when the same statistical information is conveyed in different ways, people make drastically different decisions.2 Consequently, there is often a large gap between conclusions reached by analysts and what decision makers understand. Here, we address this issue by first identifying strengths and weaknesses of the two most common modes used for communicating results: description and illustration. We then present a third method — simulated experience — that enables intuitive interpretation of statistical information, thereby communicating analytical results even to decision makers who are naïve about statistics.
Description is the default mode of presenting statistical information.