How to make critical decisions
Professor Cerag Pince teaches a decision strategy course in Loyola University Chicago’s MBA program.
"Gone are the days of businesses making decisions based on instinct or preference," says Cerag Pince, associate professor at Loyola University Chicago's Quinlan School of Business and instructor in the Next Generation MBA program.
"In our complex world, business leaders are turning to structured, data-driven methods to help arrive at optimal decisions."
Below, Pince offers his tips for business leaders implementing data-driven decision making.
Invest in the right tools
"Business leaders are probably already doing a kind of risk assessment in their minds, but the right tools can create systematic models to help visualize outcomes, determine where to invest company resources, and identify risk," says Pince.
Business decision software can create decision trees that enable leaders to think critically about the decision and predict what would happen by analyzing different scenarios. Some strategic decisions have long-term consequences, such as new products or services that may be in development for 10 to 15 years or investing in new technologies such as electric vehicles. These tools can help suggest the optimal long-term path for a company and help develop a strategy for responding to future uncertainties.
Understand risk management methodology
"The next generation of business leaders will need to understand risk management methodology better than the previous generation," says Pince. Regardless of the function or industry, all businesses are becoming technologically more complex. This requires a talent for a clear-eyed risk analysis and a need to respond with the appropriate approach.
Know your data
Machine learning – which automates analytical model building – can go wrong in practice. People typically focus only on the algorithm. Equally or even more important is the data, says Pince. Business leaders need to understand the link between the data used to develop and train the algorithm and the environment in which the algorithm is used to make decisions.