Darwin never claimed that evolutionary success came down to survival of the fittest. To the contrary, he stated, “it is not the strongest that survives; but…the one that is able best to adapt and adjust to the changing environment in which it finds itself.” The long list of once thriving businesses that failed because they lacked the agility to evolve as rapidly as their markets is proof that this lesson applies equally to banking and broader financial services, and an inability to quickly read and respond to market signals is a common cause of that failure. External data includes information about competitors, locations, customers and the world outside an organization such as economic and market insights. Financial services organizations that integrate external data into all their analysis, planning and performance reviews are better able to detect and respond to their environment and by doing so, to survive and thrive.
Business competition is a matter of us-versus-them, so why limit analysis, planning and performance reviews to internal comparisons such as actual-to-plan and percentage changes from a prior period? Why celebrate a 7% increase in return on assets against an expected 5% rise when the market grew by 12% and, in reality, the company experienced a market share loss? To remain competitive, a comprehensive set of external data is no longer a “nice to have” item. External data is necessary for useful and accurate business-focused planning and budgeting, and for performance benchmarking. Consider the example in retail banking of a location that serves a demographic that is more digitally engaged and that does not like to visit physical locations. Using only internal data on the visitors and transactions at a location could lead to erroneous conclusions about that location’s performance. Including external data such as demographics and customer location can provide more valuable insights for improving outcomes. This is becoming increasingly important as financial services organizations adopt artificial intelligence (AI) using machine learning (ML), because omitting external data in modeling will result in flawed models with potentially expensive negative consequences.
