Be Specific

When discussing the importance of analytics with someone outside of the field, it’s easy to generalize and say with confidence that operations research and management science reach into nearly every aspect of human activity. You may get more specific, mentioning some of those aspects by name: healthcare, transportation, military, disaster response. 

While you may get a vague nod in agreement, non-analytics professionals are much more likely to understand both the vastness and the practical applications of our field if you provide specific use cases. Several of this issue’s articles reminded me of that fact.  

For example, it’s one thing to say that analytics is useful when determining optimal efficiencies for fighting fires; it’s another thing to explain how analytics is being leveraged to reduce firefighter exhaustion and save lives. By creating a more accurate method of calculating the real labor that goes into firefighting, officials can better gauge firefighters’ actual workloads and schedule shifts so they stay energized and perform optimally. (See "Not All Time Is Created Equal: In Firefighting, Time-Based Metrics Can Miss the Real Work.") 

Likewise, while explanations of maritime law can quickly make someone’s eyes glaze over, the topic gains increased relevance when applied to the operations of vessels responding to significant domestic emergencies. The movement of vessels, railroads, trucks, and barges must all be brought to bear on mobilizing aid to some areas when disaster strikes. Creating a network optimization model can help emergency officials play out feasible rescue plans before a disaster occurs. (See “Beyond the Waiver: Using Operations Research to Strengthen Emergency Maritime Logistics.”) 

Similarly, saying that analytics is useful in public health is not nearly as effective as explaining how scientists are connecting unhealthy lead levels in developing countries to improper electric vehicle (EV) battery recycling. Although the use of EV batteries in small two- and three-wheeled vehicles seems to make environmental sense, researchers have connected the improper recycling of those batteries to a rise in unhealthy lead levels in the bloodstreams of local children and adults.  

In “Beyond the Charge: What Batteries Can Leave Behind,” a roundtable discussion among academics with mathematics, policy, product reusability, and operations management expertise talk about how better EV battery recycling methods can address dangerous lead levels and promote community health. 

Analytics can also improve how fertility drugs reach the right patients and providers. In "From Data to Marketing Strategies: Leveraging AI/ML for Optimizing Patient Segmentation in the Fertility Market," the author describes a machine learning framework that segments both patients and prescribers in the gonadotropin market using claims data, diagnosis codes, and treatment history – pinpointing, for example, which prescribers have eligible patients but aren't yet prescribing. 

At a time when AI may be the most popular topic of conversation there is, analytics has a huge opportunity to display its relevance in a new technology that people perceive as relevant to their lives and livelihoods. 

Surrounded by stories about the popularity and accomplishments of AI, it may surprise many outsiders to learn that the failure rates for AI projects are substantial. One study from Gartner reports that only 7% of CFOs see a high ROI from AI in finance. One preliminary MIT study found that 95% of custom enterprise AI tools yield no measurable value and never scale into production. 

These statistics provide background for “The Missing Triad: Science, Service, and Metrics in Durable Generative and Agentic AI Systems,” which explains that, rather than a one-and-done conventional IT product, GenAI is a powerful tool that works well only when it is embedded in a culture of continuous measurement. A science-enabled AI system, says the author, performs better by encoding patterns, relationships, and evolving statistical beliefs derived from data. 

This specificity matters when talking to outsiders about how what we do day-to-day enables us to contribute to advances in public health, disaster response, and medicine. It’s easy to assume our field speaks for itself – but without concrete examples, even the most compelling work can get lost on a general audience. Being specific about how our work touches real lives is how we build broader appreciation for what our field has accomplished and will accomplish in the years to come. That is the conversation we need to be having.