Opinion
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AI has leveled the playing field of analysis. Every enterprise can now generate models, dashboards, and insights at scale. But when analytic power becomes universal, advantage shifts elsewhere—to how well organizations decide with it. In the GenAI era, success will hinge less on analytic sophistication and more on decision capacity: the ability to frame options,…
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AI doesn’t erase human judgment—it exposes its importance. As AI streamlines analysis, advantage shifts upstream to decision capacity—how well organizations make choices. Decision Scientists connect AI-powered insights to strategy by clarifying options, quantifying trade-offs, and closing the loop with evidence and feedback. When sophisticated insights are widely available, the winners are those who decide confidently…
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Data teams are no longer just service providers—they’re becoming strategic partners in decision-making. But as business fluency rises on one side, a growing data literacy gap is emerging on the other. This shift is creating tension—and opportunity. This isn’t a flaw—it’s the future.
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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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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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The Sleep Training of Data-Driven Decision-Making: How Intermittent Reinforcement Creates Bad Habits
As a new father, I saw an unexpected parallel between sleep training my baby and decision-making in marketing: intermittent reinforcement. Just like a baby learns to keep crying if it sometimes works, businesses fall into the trap of justifying bad habits based on occasional success. This post explores how Decision Sciences breaks the cycle.
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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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Generative AI (GenAI) is revolutionizing how we generate, distribute, and act on data, fundamentally transforming the metaphorical “economy of insights.” In this new paradigm, data remains the currency of decision-making, but the rules of the game have shifted. From a Decision Scientist’s perspective, this evolution creates both challenges and opportunities, reshaping how we deliver value.
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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.