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?
A recent Harvard Business Review article declared that prescriptive analytics is the future. But is it? Experts are now suggesting that the future is not prescriptive analytics but cognitive analytics. Advances in technology are creating massive amounts of data with the potential to create a competitive advantage or overwhelm companies. Implementing integrated data management and analytics is essential to support future needs.
Cognitive analytics is a field focused on the imitation of human intelligence. Cognitive analytics uses large amounts of data and advanced analytics to identify connections that support discovery, enhance decisions, and create continuous self-learning feedback. While humans can be unpredictable with decision-making, cognitive analytics can improve quality and consistency for businesses by learning from past decisions. Some have described this as auto-tuning or self-healing to create elasticity in systems rather than reacting to system failures. Cognitive analytics implies that systems can adapt and get smarter over time by learning from interactions with people and data by a feedback loop.
Cognitive analytics involve greater complexity and greater value for organizations than advanced analytics by providing real-time responses to complex questions. Cognitive analytics benefits are mining previously untapped data sources, providing personalized services, improving consistency and quality of service, and enhancing knowledge sharing. Cognitive analytics in healthcare can provide personalized healthcare using the petabyte of medical data each of us creates over our lifetime. In the property management industry, cognitive analytics leverages data from internet-enabled devices (IoT) to learn how to improve security and reduce maintenance costs. Not all businesses will want to be on the cutting edge of analytics and pursue moving beyond prescriptive analytics. Still, for those that do, there may be a distinct competitive advantage. For those daring to move ahead with cognitive analytics, Organizations begin with the end in mind and start small to evaluate the business case.
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Huang, T., Bergman, D., & Gopal, R. (2019). Predictive and prescriptive analytics for location selection of Add‐on retail products. Production and Operations Management, 28(7), 1858-1877.
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Vikas. (2018). Going beyond prescriptive analytics – self healing and auto tuning. Eurostar Huddle.
About the Author:
Jeff's knowledge and expertise include leadership development, coaching, and workforce strategies to achieve influence and grow organizations. Jeff Doolittle is the founder of Organizational Talent Consulting in Grand Rapids, MI. He can be reached at email@example.com or by calling (616) 803-9020. Visit https://www.organizationaltalent.com/executive-coaching to learn more about executive coaching services provided.