Ford’s 10-year experience with data and analytics has been nothing less than transformative.

When Gahl Berkooz defended his applied math doctoral thesis at Cornell, the possibility that he’d wind up in corporate leadership for a Big Three automaker simply wasn’t on his radar. But in the 20-plus years since then, a lot has changed in the business world — and using data analytics to understand the complexities of modern business has become not only common, but essential. Berkooz joined Ford Motor Co. in 2004, and was head of data and governance and a member of the company’s global data insights and analytics skill team at the time this interview was conducted. (He is currently chief of analytics, Global Connected Consumer Experience, with General Motors.) Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. He spoke about his experiences at Ford with MIT SMR’s Michael Fitzgerald.

You’ve built the information management and analytics function at Ford for over 10 years now. So I’m curious, what has Ford learned in a decade of data and analytics?

I think that one of the key lessons that we learned is that you really can’t separate data from analytics — that your analytics, at the end of the day, are going to be only as good as the data that goes into it. That determines the quality of the model results, but also required for your analytics productivity.

Data scientists or analytics experts spend most of their time collecting, normalizing, and assembling the data, and part of what we’ve learned is that you really want to — “curate” is the term we use — the data ahead of time so that the analytics process is efficient.

Another key learning we had is to have appropriate scoping of the business problem ahead of time and a structured process that involves illuminating both the data and analytics requirements. You may have an idea about some clever algorithm or clever method, but once you try to apply it in reality, you find that to make an impact, it invariably requires massive amounts of data.