EP58: Getting big data right for smaller banks

Sanjiv Anand, Chairman, Cedar Management Consulting InternationalA study of small and mid-size U.S. banks shows a widening analytics gap versus large banks. While big banks invest heavily in enterprise BI, predictive analytics, and large teams of specialists, smaller banks remain constrained by Excel-based reporting or limited BI tools with narrow adoption and high costs. Even mid-sized banks struggle to deploy analytics bank wide due to skill shortages and expense.Simply mimicking large-bank approaches with smaller budgets delivers poor results. For smaller banks, the real challenge is not just spending less, but adopting a fundamentally different model. Key cost drivers include specialist talent and long “time to intelligence,” which reduces business value.The proposed solution is to leapfrog traditional approaches through packaged BI and analytics solutions. These offer pre-built enterprise functionality, faster deployment, lower dependence on scarce analytics talent, and reduced costs. By using standardized, commodity-style analytics platforms, small and mid-size banks can achieve results comparable to large banks without matching their spend.