Analytics Advice from Top Bank Data Officers

The sea of data at banks can be tough to navigate, so good captains are crucial.

American Banker recently sought the insights of several executives who try to put tremendous amounts of customer data to innovative uses in their banks. Many of their answers were compiled in a slideshow, but the Q&A below provides more detail from select respondents. It has been edited for clarity.

How did you end up in "big data"?HOFFMAN: I began my career as an intelligence officer at the Central Intelligence Agency. Then 9/11 happened. I, like many, felt a call to do something. On Sept. 12, 2001, I joined the U.S. Navy Reserve as an intelligence officer. In the nearly 17 years I spent in intelligence, I quickly learned that the fundamental role of professionals in data analytics is to empower good decision-making.

I had a front row seat to both the amazing power of good decisions and the destructive power of bad ones. I developed a visceral dislike for bad decisions, which has powered my professional passion: to enable good decision-making. At the end of the day that's really the fundamental mission for anyone in a data analytics role.

What industry or specific company do you think does a fantastic job with data analytics and why? From my lens, no organization does a better job with data analytics than the U.S. military's special forces units and the intelligence units supporting them. Their ability to translate data analytics into actionable outcomes at both the strategic and tactical level is unprecedented. For them it's literally life and death. It's not life and death for us in financial services; however, the decisions we inform can be life-changing for our customers. That inspires me, and it should inspire all of us.

What advice would you give to a Bank just starting down the road of data analytics? Don't forget that it's not only about "big data"; actionable little data is also extremely valuable. Keep the customer at the center in whatever you do. Focus on creating value at the intersections. Quite often an organization's data analytics "power plants" are up, but the lines are down.