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 commerce, and an inability to quickly read and respond to market signals is a common cause of that failure. External data includes information about competitors, customers and the world outside an organization such as economic and market statistics. Corporations that integrate external data in 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 sales 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 of an ice cream vendor who sells out on very hot days even when charging a premium but lowers prices on cooler days trying to move the inventory. Using only internal data—volume sold and price per unit—one would erroneously conclude that the more you charge, the more you sell. Including external data such as temperature provides a more complete picture that produces more accurate forecasts and recommendations. This is becoming increasingly important as 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.
