• Jeff Doolittle

Improving Strategic Foresight with Data Analytics


In today's complex and fast-paced digital marketplace, no organization is looking to stay the same year over year. The use of predictive and prescriptive analytics promises improved strategic foresight. The goal is not to predict the future but enable better decision-making and preparedness so that leaders can grow revenue amid uncertainty. Most of the investments being made by organizations today focus on descriptive analytics that provides an understanding of what happened. While useful, understanding why something happened, what will happen, or what should be done provides greater value (see Figure 1) for organizations.


Figure 1. The value of descriptive, predictive, and prescriptive analytics.

Predictive analytics reveal natural patterns to predict future outcomes, enable better decision-making, and create change resilience. Data, text, web, and media mining and forecasting are enablers to predictive analytics. Prescriptive analytics rely on mathematical algorithms to assess competing priorities and constrained resources and find the best solution from a set of feasible solutions. Optimization, simulation, and heuristic-based decision modeling are enablers to prescriptive analytics. Organizations have used predictive and prescriptive analytics since the early twentieth century (see Table 1).


Table 1. Predictive and prescriptive examples

Leaders do not have to focus on fully exploiting descriptive analytics before progressing to advanced analytics. Technology and predictive and prescriptive analytics are shifting the decision-making paradigm norm toward human decision-making enhanced by advanced analytics. The evidence-based benefits of predictive and prescriptive analytics are clear. One recent study of 1500 US retail locations found that predictive and prescriptive analytics applied to expansion decision-making resulted in substantial increases in expected sales.


What lies beyond prescriptive analytics?