Using Reinforcement Learning to Make Better Decisions

Most organizations begin their analytics journey with dashboards (descriptive) and gradually move to understanding why things happened (diagnostic). As maturity builds, they start predicting what will happen next (predictive). But the real game-changer? Knowing what to do—prescriptive analytics. This session dives into how businesses can unlock massive efficiency gains by making better decisions at scale. Despite its potential, very few companies truly leverage advanced prescriptive techniques. Why? Because it's complex, risky, and easy to get wrong. Join us to explore how to make smarter, scalable decisions that actually drive results.

Key Takeaways:

• Analytical organizations evolve from descriptive to prescriptive analytics as they mature in data capabilities.
• Prescriptive analytics focuses on identifying the best decisions based on past and predicted future outcomes.
• Scalable, automated decision-making unlocks significant business efficiency but is difficult to implement correctly.
• Despite advances, most business decisions still rely on human judgment due to the complexity and risk of automation.

A data scientist with over 5 years of industry experience, Tamojit holds a Master’s in Operations Research from the Indian Statistical Institute, Kolkata. A math enthusiast and passionate mentor, they regularly guide students and startups in AI/ML and speak at conferences and academic events. Beyond work, they enjoy running, stargazing, gaming, and reading novels—living life as a minimalist with a curious mind (and maybe, one day, a PhD). You can reach him on LinkedIn.