A variety of pioneering companies are entering the next phase of data analytics — dubbed Analytics 3.0 — where the data analysis not only produces faster or better internal decisions but can also be turned into external products and services.
Speaking at the CIO 100 Symposium and Awards event in Rancho Palos Verdes, Calif., author and professor Tom Davenport sketched out the past, present and future of data analytics. He said the path runs from Analytics 1.0 (known as decision support or business intelligence), which was largely backward-looking analysis of internal transactional data, to Analytics 2.0 (today’s “big data” buzz), with an emphasis on unstructured data, trying to find data scientists, and using Hadoop no matter what the business problem is.
[ Related: 18 Essential Hadoop Tools for Crunching Big Data ]
Analytics 3.0, Davenport said, combines phases 1 and 2 but the analytics become embedded throughout the data-driven organization and are available to every employee at the moment they need to make a decision. Machine learning and external market data are added to the mix — and sometimes the resulting innovations can become a new revenue stream.
[ Related: How CIOs Can Survive and Thrive in a Swirl of Change ]
Davenport cited companies such as Cisco, Ford, General Electric, Merck, Monsanto, NCR, Procter & Gamble and Wells Fargo as being in the vanguard. GE is pushing its vision of the “Industrial Internet” to sell predictive maintenance data about its jet engines. Ford sees its vehicles as rolling collections of sensors emitting data. And Monsanto is working to help farmers with predictive planting.
A prime example is GE Capital Americas, which provides financing for midmarket companies. In order to stand out from the competition, the business is developing complementary data-based products, such as a tool that helps companies better manage the maintenance of their vehicle fleets, according to Kelly Shen, CIO for business intelligence. (The fleet vehicle optimization tool won a CIO 100 award for innovation.) Similarly, Shen’s team developed a tool that helps franchisees pick the best location for their businesses, based on analysis of local consumer data and competition.
At the CIO 100 event, Shen said the company acts like a venture-capital investor to provide small amounts of seed money for experimental data projects, with the understanding that some projects will “fail fast” while others will blossom into products. The goal is to avoid over-investing in an experiment, she said, and to be ready to move on quickly.
In this move beyond traditional business intelligence, Shen said she has started to hire non-traditional IT professionals, such as “product strategists” whose job is to figure out how to take an analytics-based innovation to market.
Davenport made a similar point. He said companies will need new types of employees to make Analytics 3.0 work, including data “translators,” who can tell stories with data in a way that gets executives to take action, and product developers who commercialize analytics for external customers (a skill foreign to most IT departments).
With appropriate education, all employees will become “analytic amateurs” in the regular course of getting their work done through data-driven processes, Davenport said. “Analytics have to become everybody’s job.”
Mitch Betts is executive editor of CIO magazine. Follow him on Twitter at @mitchbetts.