Competing With Data & Analytics
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Managers today similarly crave facts. The potential positives of working from objective facts are enticing. It’s expected that improved performance follows from basing decisions on facts, whether in traditionally heuristics-based industries such as health care or in causally imprecise contexts such as business strategy.
But our world is awash in data, and data is not the same thing as facts. Facts are much harder to come by than data. While data seems to promise objectivity, instead it requires analysis — which is replete with subjective interpretation.
Assuredly, having data is a necessary step toward making objective decisions. Yet the objectivity of data is a myth. Modern analytical methods afford creative and flexible uses of data that can support multiple perspectives and competing analyses about the same data sets.
For instance, more data makes it easier to find support for virtually any position — because more data provides more options, limited only by the creativity of the analyst. Analysis could choose to focus on a subset that shows the “correct” results. Or, data that counters a desired position could be filtered out as “erroneous.” Sophisticated tools support many different modeling methods and options; one is bound to find the “right” answer. Just keep adding and dropping variables or observations until the known “truth” shines through.
Using data and analytics to support pre-existing beliefs is called “confirmation bias.” This is a particularly acute problem for modern analytics due to the potent combination of access to massive amounts of data, sophisticated methods and the seeming irreproachability of data-based decisions.
Confirmation bias can advance personal and political agendas or technical outcomes in ways that are difficult to detect. It can take the form of looking only for evidence that supports a desired outcome.
Alternatively, another way confirmation bias manifests itself is by having a preconceived idea about when to stop data analysis.