Any company, given enough time and investment, can stand up the technology needed to execute analytics. But how does a company shift from simply executing analytics to being an analytically oriented company, and consistently exploiting analytic insights within business processes?
Many vendors sell technology and offer services that aim to take enterprise data, integrate its many siloed sources and enable insights based on that information — Teradata included. But tackling the second part of that equation is tougher — it requires not just a technology investment but a change in company process and culture. Finding a service provider that helps solve for that last-mile analytics problem is something Teradata specializes in, and research I’ve recently been involved with sheds some light on where the state of the enterprise is today in pivoting to an analytics-driven culture.
Internally at organizations, the role of transforming a company into a data-driven powerhouse tends to fall to the data scientist. It is their gargantuan job to distill new business insights from data, but it’s no secret that finding the perfect data scientist that understands computer science as well as they do business management is often elusive for companies outside of today’s top tech giants. Thankfully, this state of affairs may slightly shift soon, as many of the younger generation coming out of college now come into the workforce with a more technically and analytically savvy mind. They may not be able to write Python code, but they have a good grasp of analytic techniques, even if they are pursuing careers in marketing instead of data science.
To solve the last-mile analytics gap, companies must create repeatable processes that allow an enterprise to use their analytics systemically and operationalize these insights at scale. But often this scenario is more like capturing lightning in a bottle — it’s very powerful when the stars actually align, but there’s no built-in corporate methodology that enables this to occur over and over.