Data Science: A Business Lynchpin, Not an Experiment

Too many organizations still view data science a technical skill, acquired simply by hiring a few data scientists. By doing so, they are not building the critical organizational muscle necessary to leverage predictive models as an enduring competitive advantage. Today’s leading companies didn’t get there by accident. They got there by putting models at the center of their business. They release innovative products powered by models, such as Netflix’s recommendation algorithm or Amazon’s Alexa assistant. What’s less obvious is how these companies are weaving models into their business operations to drive constant, iterative improvement. Whether it’s fraud detection, supply chain optimization, or employee retention, model-driven companies are leveraging the power and scale of models in ways often invisible to the end customer or competitors. This transformation is not limited to the technology sector. It is akin to what began 30 years ago with the emergence of software development as a key organizational discipline. At first, there were only a few “software” companies. Software teams were relegated to the dark corners of the enterprise. Now, software engineering is a defining organizational capability across industries. Those companies who did not fully embrace the change have ceased to be relevant, if they exist at all. The same dynamic is happening with data science. Companies that embrace it as a discipline will become leaders, and those that do not will cease to exist. According to a recent survey of data science practitioners and managers, 90% of companies view data science as a key source of innovation, but less than 30% have more than five models driving production business processes. The reality is most organizations still view data science as a technical practice of talented individuals, while model-driven companies view data science as an organizational discipline.