Differentiating via data science

While companies often use data science as a supportive function, the emergence of new business models has made it possible for some companies to differentiate via data science. Eric Colson explores what it means to differentiate by data science and explains why companies must now think very differently about the role and placement of data science in the organization.

Companies employ various means of differentiation in order to gain a competitive advantage in the market. Traditional differentiators include network economies, branding, economies of scale, and so on. But the availability of data and compute resources, combined with the emergence of new business models, have enabled data science to become a strategic differentiator. Eric Colson explores what it means to differentiate by data science and explains why companies must now think very differently about the role and placement of data science in the organization. The traditional organization needs to be changed if a company is to differentiate via data science. Data science needs to be a top-level department reporting to the CEO. Further, it needs a completely different workflow. It can’t thrive with top-down requirements or if it is forced to submit to upfront ROI calculations. Data science needs more fluidity, more experimentation, and more iteration. Innovation is born out of curiosity, often the the type that can only be come from a data scientists. Don’t go to data scientists with a problem to solve. Let them come up with things that you wouldn’t have dreamed of. You can still hold them accountable for business impact—and it will be bigger than you think—but don’t expect it to be from your vision. It will be from theirs.