Symbiotic Duality of Learning Manifested in Merging the Holmes & Columbus Approach
Traditionally, most organizations look at analysis and data only when they have to solve a business problem.
The problem-specific way of looking at data (the “Sherlock Holmes” method) is a structured method, which involves defining the business problem, identifying factors affecting it, and generating hypotheses that get tested on data to help derive insights and recommendations. This structured analytical thinking helps hone the analytical part of the brain. It is easier to enable in an organization since data and analytical investments are in service of a business case.
However, this approach can be short-sighted.
In the book ‘Where Good Ideas Come From: The Natural History of Innovation’ by Steven Johnson, innovation is said to be facilitated in an ecosystem that encourages exploration based on hunches, enabling connections of ideas while allowing the reuse of prior knowledge and permitting the freedom to learn from the ‘not-so-right’ moves. In the light of this observation, the problem-driven approach to data and analysis by its very nature is constricted and lowers the potential to discover opportunities.
Therefore, it is required to adopt an approach that starts with agenda-less observations and exploratory data analysis which in turn, lead to hunches, pattern discovery, and generation of new ideas and opportunities. The “Christopher Columbus” approach entails looking at large amounts of information and building an internal anticipation repository that feeds intuition. However, organizations are usually reluctant to follow this path, since the return on investment is unpredictable.
What we in Mu Sigma believe is that leveraging and navigating the duality between problem-driven (Holmes) and discovery-driven (Columbus) methods will be critical to success.