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.
I liken this state of the enterprise to the old days of enterprise resource planning (ERP) software. When these solutions came out, they automated and systematized back office business processes. Then customer relationship management (CRM) software did the same for the sales force. Now we are approaching the need for this type of automated, repeatable process in advanced analytics.
Through work with the University of Virginia Darden School of Business, Teradata surveyed businesses on their analytics capabilities. The results showed that businesses are more sure of their technology but less confident in the maturity of their people and processes. The systemic analytic innovation process is not institutionalized in companies. Part of the problem is that data scientists don’t have enough bandwidth, or even the right skill set to tackle these process related challenges.
This is all what makes Teradata’s Agile Analytics Factory so valuable. It allows companies to operationalize advanced analytics, including artificial intelligence, machine learning and deep learning, across the enterprise and — most importantly — can be offered as a service.
To get to the next step and repeatedly drive analytic innovation, we enable internal teams to optimize their resources and focus on getting the most from their analytics investments, and these teams rely on us to develop leading practices that will allow them to scale analytics throughout their culture. Instead of struggling to find the needle-in-a-haystack perfect data scientist, the Agile Analytics Factory works to provide data science savvy up front and works over time to embed that knowledge into an enterprise’s own team of experts over time. Then instead of merely keeping the proverbial lights on, these businesses will begin to develop their own best practices to stand up an analytics-driven culture.