At GM, self-service analytics drives business results

The automotive giant built a predictive analytics platform to fuel insights across its many business lines, including the emerging market for autonomous vehicles.

At GM, self-service analytics drives business results

General Motors has built a predictive analytics platform that generates insights into several core business strategies, including anticipating market demand for autonomous vehicles. Maxis — shorthand for maximizing insights — represents a multi-million-dollar investment in data-crunching technologies that coincides with the company's IT strategy overhaul.

Maxis, which received a CIO 100 Award in IT excellence, is making information accessible for thousands of GM employees, including anyone from business analysts and software engineers to data scientists and C-suite executives, says Les Copeland, GM’s CIO of global data strategy, artificial intelligence and analytics services, who oversees the platform. Among its key attributes is self-service analytics, which allows employees to query a Google-like search interface for information about specific business needs, including pricing, incentives and marketing optimization, sales lead management and forecasting, and problem detection.

Self-service analytics accelerates predictive insights

Using data-as-a-service platforms to democratize information is becoming table stakes. The analytics output of business users with self-service capabilities will surpass that of professional data scientists by 2019, according to Gartner. More than 3,000 CIOs Gartner surveyed ranked analytics and BI as the top differentiating technology for their organizations.

Roughly 300 GM software engineers worked on Maxis, which includes four main pillars, says Copeland. The first pillar involves ingesting data — more than 30 billion records (1.5 petabytes) per day. The data comes from internal sources such as applications and internet of things (IoT) sensors from connected cars, as well as external sources from partners and other market forces that make up GM's supply chain. A key challenge to such “hyperingestion” is ensuring data was “cleansed” and tied to its source system. "Just like any company, we had things to clean up," Copeland says.

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