Ikechi Okoronkwo, director of marketing sciences at media and marketing services company Mindshare, said bs comes from data scientists inadvertently allowing their own assumptions to influence the output—or an analytical approach that is one-dimensional.
In addition, data scientists need to be transparent about assumptions so stakeholders can provide feedback. For instance, “if you believe that TV spend is going to perform more strongly based on previous campaigns, explain that hypothesis upfront to your clients and teams so that the other stakeholders can chime in with other viewpoints to consider,” Okoronkwo said.
Okoronkwo added another common misstep is not being upfront about other limitations in methodology, pointing to how predictive models have some degree of error, inherent bias and assumptions that fill gaps in data. Transparency, on the other hand, enables data scientists to work with clients to identify new data sources or to challenge assumptions. “Properly managed predictive analytics uses a system where the model can take on new inputs and learn as time passes,” he added.
Okoronkwo said it is also important to use different approaches for measurement and predictions and to have a culture that questions assumptions and tests if there are outputs that could conflict with initial findings.
“It’s all part of the scientific method. It’s good to have a hypothesis of what you think you’ll see in the future—the key is to then test that hypothesis in multiple ways,” he said. “You want to gather as much data as possible and test many different options of explanatory variables.”
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