Concepts
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My latest post explores using a large language model (LLM) to directly control a reinforcement learning (RL) environment for optimizing Marketing spend. The LLM actively makes decisions step by step. Unlike typical RL applications, this setup allows real-time interaction and showcases the LLM’s reasoning process while determining optimal actions.
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Drawing inspiration from Rumi’s timeless insight—“When light returns to its source, it takes nothing from what it has illuminated”—this blog post explores how descriptive analytics reveal historical trends while prescriptive analytics chart future actions. Discover how these complementary approaches drive clarity and remarkably empower effective decision-making in today’s data-driven world.
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Check out my latest blog post, where I set up a simple scenario to show how a reinforcement learning (RL) agent finds the sweet spot in marketing spend—laying the groundwork for Agentic AI. Whether you’re curious about RL fundamentals or integrating LLMs, this mini use case offers valuable insight into the inner workings of RL…
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Large Language Models (LLMs) are powerful, but they don’t always surface the most relevant or up-to-date information for your needs. Retrieval-Augmented Generation (RAG) changes that by integrating real-time, domain-specific insights, ensuring AI-generated responses are always up-to-date and relevant. Instead of relying on generic training data, RAG personalizes AI with custom knowledge sources, from reports to…
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This latest post explores the distinctions between data-informed, data-driven, and decision-driven approaches to analytics in organizations. It highlights that while many claim to be data-driven, they often over-rely on intuition. Transitioning to decision-driven analytics, which emphasizes defining decisions first and aligning data accordingly, is crucial for effective strategy and impactful outcomes.
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We are producing more insights than ever—but are we making better decisions? This blog introduces the Decision Science Pre-Analysis Framework, a structured approach to overcoming the Last-Mile Problem in analytics, ensuring insights don’t just inform but actively drive decision-making and meaningful action.
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Data helps, people deliver—but what role does GenAI play in the new economy of insights? Reflecting on my last decade as a data scientist, I explore how Decision Sciences bridges the gap between data, decisions, and outcomes in a rapidly evolving landscape. Read more about the lessons I’ve learned and the road ahead.
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This latest post emphasizes the importance of shifting from topic-driven analysis, which provides descriptive insights, to decision-driven analysis, which focuses on actionable insights tied to specific decisions. This approach improves clarity in decision-making, quantifies outcomes, and helps leaders navigate complexities effectively, ultimately turning data into a powerful tool for impactful choices.
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I recently revisited Avinash Kaushik’s brilliant article, “Winning With Data: Say No to Insights, Yes to Out-of-Sights,” and felt inspired by his challenge to aim higher in our data-driven work. The concept of “Out-of-Sights”—insights that are Novel, Actionable, Credible, and Relative (N-A-C-R)—is not just an aspiration but a call to fundamentally reimagine how we approach…
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Drawing on HBR’s ‘Where Data-Driven Decision-Making Can Go Wrong,’ this post connects insights from the authors’ research to strengthen the idea that by defining clear ‘impact paths,’ avoiding ‘decision black holes,’ and resisting the temptation to ‘shoehorn data,’ organizations can make better decisions.