Mathematical optimization offers its own prescriptive decision-making role to help teams make informed decisions on complex problems.
Contemporary breakthroughs in artificial intelligence (AI) and machine learning (ML) have placed a bright spotlight on the business role of data science. From financial services institutions to retail groups and manufacturers, virtually every organization wants the ability to leverage cutting-edge tools to assess data, generate accurate insights, and make critical business decisions.
Even so, there is no standard solution for implementing these new tools. In addition, the 2024 Gartner Hype Cycle for Artificial Intelligence found that many are confronting a “trough of disillusionment” as the initial AI/ML hype wears off and they determine whether to invest in fast-evolving technologies that have yet to deliver significant ROI.
The evolving nature of these tools leaves businesses with a key question: How exactly should we optimize our decision-making capabilities?
Current Decision-Making Processes
First, it’s important to understand exactly why decision-making needs optimization. With existing team structures and processes, it may seem like overkill to introduce new analytics tools into the mix. However, considering the number of decisions the average person is required to make each day, experts estimate the number to be between 33,000 and 35,000. Any chance to simplify these decisions and remove guesswork offers significant benefits.
Choosing not to leverage any technology that is the right tool for complex decision making can leave both money and time on the table, losing out on efficiency gains, cost reduction, competitive advantages, and more. That’s why so many teams continue to explore the application of new analytics tools in their decision-making efforts, finding ways to improve their processes and reap the business benefits.
See also: With Automated Decision Making, What Happens to BI Professionals?
An Advanced Method of Decision-Making
AI and ML applications provide modern teams with exciting new opportunities to streamline the factors in their decision-making processes. They can ingest, examine, and derive insights from varying data types, enabling teams to effectively collect and analyze data at the speed of their business. By reviewing past data and identifying relevant patterns, AI and ML tools can predict what is most likely to happen next. These actionable insights can then be leveraged to drive business initiatives and help teams reach their goals more efficiently.
Outside of these predictive analytics tools, there is another option that, while historically daunting for some, is well-suited to address analytical challenges and provide informed solutions to complex business problems: mathematical optimization.
Mathematical Optimization and its Misconceptions
With roots in the mid-20th century, mathematical optimization (MO) is no new concept. It is a process that uses the power of mathematical equations to examine complex problems and generate prescriptive solutions.
There is one key difference between MO and AI/ML. While the latter provides teams with predictive analytics, MO is a prescriptive process. It inputs your goals, constraints, and relevant variables into algorithms and uses these factors to calculate the best possible decision recommendation(s).
One common misconception among the data science community is that mathematical optimization is too, well, mathematical. Due to the algorithmic nature of the concept, many believe that you need a deep understanding of complex mathematics to implement it effectively. For those who already have a wealth of computational knowledge and often little free time to learn complex new concepts, this can be an easy deterrent.
As with many misconceptions, this belief is not entirely based in fact. While it helps to understand basic concepts, being an expert in complex mathematical theory is not a prerequisite for using MO. All you really need is a good understanding of your problem and the capacity to translate that using a little bit of algebra.
Applying Optimization in a Business Context
While MO is not yet as widespread as AI and ML, there are many ways in which organizations can model their complex business problems and run their algorithms to produce optimal outcomes.
Let’s consider the transportation industry. We’re already seeing airlines implement AI for several helpful use cases, such as predicting flight delays based on weather patterns and streamlining ticketing and baggage operations. But when unexpected delays occur, they can have a massive ripple effect across efficiency, operational costs, and customer satisfaction.
In a scenario with many moving parts, you want to eliminate any guesswork from pursuing the best possible solution. An MO model can account for the many factors in play and return a solution that works best across the board, helping avoid unhappy customers, inflated operating costs, and scheduling nightmares in one fell swoop.
Conclusion
As organizations continue to explore the application of evolving technologies, they’re furthering their capacity to make faster, better, and more sound decisions. This includes the use of predictive AI and ML platforms, as well as the more prescriptive usage of MO. Any math-based misconceptions around MO are overstated, and data scientists already have the skills required to begin to use MO tools.
Ultimately, mathematical optimization offers its own prescriptive decision-making role to help teams make informed decisions on complex problems.
