Summary Of Brian Moynihan's Competing On Analytics

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As a large, multi-national financial institution, BAC / ML is at the forefront of the analytical revolution. In an interview with PriceWaterhouseCoopers (PWC) , Brian Moynihan discusses how technology can improve operational efficiency. This is a point that Davenport & Harris make in “Competing on Analytics” - that in today’s market, efficient and effective execution are one of the strategies that can pay off in separating a company from its competition. Mr. Moynihan further speaks to how big data analysis is one of the tools that can provide the intelligence needed to achieve some of their strategic goals. As one of the world’s largest banks, BAC/ML certainly has the funds and resources to bring this intelligence to bear - provided that…show more content…
These can each be discussed as they relate to BAC/ML:
Senior Management Commitment. This trait has been purposefully listed first because without it, the other characteristics will not have the necessary effect throughout the organization. We have already seen that Brian Moynihan, BAC/ML’s CEO, has realized the benefit of using analytics and has described how it can be used to the bank’s advantage. In addition, in an interview with Forbes from 2013 , Cathy Bessant, the head of Global Technology and Operations at BAC/ML, made it clear that data analytics was one of three key areas for growth that BAC/ML has made the decision to invest in. And in many interviews with Ms. Bessant and other high-level executives at BAC/ML we hear the very same message. Hearing such direction from the top ensures that the broader company views analytics as
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In the case of BAC/ML, these two traits go hand-in-hand as any distinctive capabilities that are developed and successfully tested may ultimately be pushed out to a large customer base. In an article from 2013 in American Banker , Ms. Bessant discussed the bank’s use of data analytics to understand customer behavior – including how customers interact with the bank - from phone calls to the call center and online chat interactions - to more traditional visits to the branches. This analysis led to the deployment of self-service kiosks in some branches, which could handle 80% of what a traditional teller would normally process. Data analytics were further used to determine customer reactions to these trials. Another example is the bank’s partnership with an analytics and transaction card company to implement
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