GE’s marketing division uses data to continuously improve performance — and democratize analytics.
General Electric (GE) is a massive conglomerate that encompasses a number of separate businesses: Power & Water, Oil & Gas, Energy Management, Aviation, Transportation, Healthcare, Home & Business Solutions. Oil & Gas is the company’s fastest growing business, with revenues of $15 billion. It competes in high-growth markets and creates products, like the recently launched first subsea compressor, that utilize GE’s broad technical capabilities. Measurement & Control, a division of Oil & Gas, covers a swath of industries and applications, according to its website, including sensing, asset condition monitoring, controls and instrumentation. But Oil & Gas, along with the rest of GE, is also betting heavy on analytics. The company announced this summer the first-of-its-kind cloud platform for collecting, storing and analyzing large-scale machine data, to handle the massive data from the upcoming Industrial Internet. GE is also applying that analytic rigor to innovate internally – and drive commercial change. Philip Kim, (former) marketing operations leader for Measurement & Control,1 talks with MIT Sloan Management Review contributing editor Renee Boucher Ferguson about the process of innovation through analytics, driving commercial change, and what others can do to get there.
How are you are using analytics within GE Oil & Gas?
General Electric is a very large conglomerate. So when you use the word “analytics,” [providing] context is probably the paramount thing you can and must do, in order to make sure that people understand where you’re coming from. When we talk about analytics within the context of our business — Measurement & Control, which is a part of Oil & Gas — we segment analytics into two large categories. One is what I would call “big machine data” or “big data” — applications, for example, trying to identify from a series of data points if there’s a technical issue with a customer asset. For example, detecting when rotating machinery might fail by combining a lot of sensor data with software and analytics.