What’s IT’s Role in Analytics Adoption?

Beth Holmes, IT analytics lead, Monsanto Co.

With a Ph.D. in plant breeding and genetics and years of information technology project management, Beth Holmes brings the discipline of a scientist to her role as IT analytics lead at Monsanto Co., the seed and crop protection chemical company.

She’s going to need it. In its essence, her job is to answer questions for the company through exploratory analytics — and to advance Monsanto’s organizational strategy of embedding analytics more deeply into all corners of the company’s operations. Using analytics, her group has scoped out high-value sales targets, done cost modeling, improved the accuracy of sales forecasting and used multiple methodologies to aid long-range planning. The “exploratory analytics” team routinely tests assumptions about such things as commodity prices and agricultural trends. “Understanding the possibilities of things that may happen [is] really critical to our ability to operate profitably,” Holmes says.

“Five years ago, people would have viewed analysis as almost synonymous with reporting,” she says. But seeing what analytics can do is starting to change what executives ask for. And because Monsanto’s analytics-adoption strategy leans heavily on newly created resources housed within the IT department, “the work changes the conversation that IT has with the business units,” says Holmes. “It changes the perspective of the value that IT brings.”

Holmes spoke with MIT Sloan Management Review editor-in-chief Michael S. Hopkins about myth busting, why the simplest solution is often the smartest and what it means to push for analytics adoption by using the IT function for leverage.

So how has Monsanto’s use of analytics and data changed?

Five years ago, people would have viewed analysis as almost synonymous with reporting. That work is critical, but being able to take the broader view and doing the analyses to support hypotheses that impact the broader view is what’s different.

In the end, you need the best information possible for a variety of decisions that range all the way from daily ops to strategic planning. And the level of sophistication that you need to generate that data differs based on the problem that you’re trying to solve. That said, the best choice is always the simplest choice. The simplest choice that works is always the best one.

That’s very interesting.