How Data Analytics is Revolutionising Baking and Hunting

Big data is all well and good, but the true analytics pioneers are applying rigorous modeling to the raw numbers. You’ll be amazed by what they find, says Morice Mendoza.

Swiss-Thai asparagus quiche. It’s a prospect even the most determined herbivore might struggle to salivate over. And yet, according to a predictive analytical tool from IBM, it’s scientifically delicious.

The technology giant has developed a “recipe generation engine” that is attracting interest from world-leading chefs and food manufacturers. It works by breaking meals down into their component parts and running the numbers on what makes certain ingredients work well together. It’s also an instructive example of how predictive analytics is adding nuance and insight to the straightforward crunching of data which, thanks to massive increases in computational power, has flooded our world with information.

The IBM “cognitive cooking” algorithm allows for the intangible elements that make a meal work, quantified as the degree of “surprise” in each dish. It knows you need a counter-intuitive smidgen of lemongrass and curry powder to offset asparagus, or that Gruyère (not mozzarella or ricotta) is the only cheese that will work.

There are few areas of life these principles cannot transform. Predictive analytics now guides everything from hunting apps that optimize success based on weather and lunar cycles, to conservation efforts aimed at protecting endangered wildlife.

Predictive analytics—the branch of data mining that uses mathematical techniques to predict future events or behaviors—is neither new nor impenetrable. Retailers have used it for decades. Tesco’s Clubcard and Target’s ability to infer pregnancy from shopping patterns are well-known examples.

Yet despite its maturity in customer analytics, predictive modeling has been slower to transform workforce management. According to Luk Smeyers, founder of HR analytics consultancy iNostix, organizations initially focused on integrating HR data rather than extracting insight.

That focus is now shifting. If employee metrics can become as transformational as customer metrics, business itself may change fundamentally.

Google is a leader in this space, using analytics to optimize hiring, onboarding, organizational design, and productivity. Bank of America has used sociometric data to reduce call-center attrition by encouraging social interaction during breaks.

Asking the right questions remains critical. Karen O’Leonard of Bersin by Deloitte describes how analytics overturned assumptions at a pharmaceutical company that blamed compensation for turnover. The real predictor was time spent in role, enabling targeted interventions that reduced attrition to single digits.

Michael Housman, chief analytics officer at Evolv, reports similar findings from large-scale studies of call-center and sales employees. Contrary to conventional wisdom, job hopping and long-term unemployment had little impact on future tenure or performance.

Organizations including AT&T, Kelly Services, and Xerox integrated these insights into hiring systems, enabling thousands of people to gain employment who might otherwise have been screened out.

At RBS, workforce analytics revealed dramatic performance gaps between business units and linked leadership stability and coaching culture to superior outcomes.

Still, predictive analytics presents challenges. Capability gaps, data quality issues, governance, privacy, and explainability all pose risks. Complex algorithms that cannot be explained can undermine trust, especially when used for promotions, pay, or opportunity allocation.

There is also the danger of confusing correlation with causation. As several experts note, many predictive models lack sufficient methodological rigor and risk reinforcing false assumptions.

Bias remains another concern. Analysts may unconsciously seek data that confirms their hypotheses, exaggerating confidence in their models.

Looking ahead, prescriptive analytics—systems that recommend specific interventions for individuals—may represent the next frontier. But reaching that point requires far greater discipline, transparency, and statistical literacy.

Predictive analytics will never eliminate uncertainty or black swan events. Business is not fully deterministic, and not everything that counts can be counted.

Used responsibly, however, rigorous analytics can help organizations make better decisions, reduce bias, improve performance, and unlock human potential. The challenge is not the data itself, but how thoughtfully and ethically it is applied.