Enterprise data strategy is being challenged by a counterintuitive idea: maybe cleaning and organizing data first is the wrong way to unlock AI value. In a recent article for Forbes, John Sviokla, HBS Executive Fellow and co-founder of GAI Insights, argues that many organizations are over-investing in data preparation while under-investing in finding the actual signals that drive decisions. The article opens with a sharp critique of the common enterprise mindset: “get your data ready first.” While this approach feels safe, Sviokla argues it often delays real AI value and leads companies to perfect datasets that may not even contain useful insights. Instead, the article proposes a shift toward a “signal-first” strategy. The key idea is simple: businesses should first identify which decisions matter, and then work backward to determine what data actually influences those decisions. This is where concepts like expected value of perfect information (EVPI) come in — if better information wouldn’t change a decision, then cleaning it adds little value. Sviokla also highlights that AI itself is better suited to messy, unstructured data than traditional analytics. Customer feedback, call transcripts, sensor data, and other “dirty” inputs often contain richer signals than highly structured but sanitized datasets. To illustrate the idea, it points to companies like Verisk Analytics, which built its business by aggregating real-world insurance and risk data tied directly to underwriting and claims decisions — effectively treating data acquisition as signal acquisition, not storage hygiene. The broader message is a reversal of conventional wisdom: AI shouldn’t wait for perfectly governed data. Instead, it should be used to discover which data is actually valuable in the first place. In this view, clean data without signal is just overhead — while even messy data can be a competitive advantage if it helps improve real decisions.

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Data is no longer just something executives review — it’s becoming the shared language they use to run the business. In a recent article for Forbes, financial services executive Matthew C. Meade argues that leadership is shifting from intuition-led decision-making to data-fluent strategy, where analytics plays a central role in how executives communicate, decide, and execute. The core idea is simple: modern organizations generate too much information for instinct alone to keep up. As a result, leaders are increasingly relying on data to guide everything from customer strategy to operations and financial planning. One of the biggest shifts is in customer understanding. Instead of broad market assumptions, executives now have access to granular behavioral data — tracking how customers engage, buy, and retain over time. This enables more personalized experiences and better product alignment with real user needs. Data is also improving strategic clarity. Real-time dashboards and analytics platforms allow leadership teams to continuously monitor performance, validate assumptions, and adjust direction faster than traditional reporting cycles ever allowed. Another major impact is decision speed. With real-time insights replacing delayed reporting, executives can respond faster to market changes, improving agility in areas like pricing, resource allocation, and operations. Finally, data is driving operational efficiency by exposing bottlenecks, cost overruns, and underperforming areas that might otherwise go unnoticed. The result is a more continuously optimized organization. Meade’s key takeaway is that data doesn’t replace executive judgment — it enhances it. The most effective leaders today are those who can combine experience and intuition with a fluent understanding of analytics, using data as the common language of modern business decision-making.

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AI (artificial intelligence) is forcing organizations to rethink something they’ve long treated as background infrastructure: data governance and risk management. In a recent article for Forbes, John M. Bremen – Managing Director and Chief Innovation & Acceleration Officer for WTW – explores how enterprises are re-evaluating data strategy as AI moves deeper into decision-making and operations. A central argument is that data is no longer just an IT asset — it’s a core driver of AI performance. Yet many organizations still struggle with basics like data quality, access, and ownership. Research cited in the article shows that more than half of organizations see data quality and availability as the biggest barrier to successful AI adoption. To address this, the article outlines five key practices. First, companies need to stop treating data as a commodity and instead recognize the complexity behind ownership, regulation, and security. Second, they must understand that not all data is the same — transactional, operational, and analytical data each serve different purposes and carry different risks. Third, organizations should quantify data risk in business terms, not just compliance terms, focusing on how data quality impacts real decisions. Fourth, governance needs to evolve from rigid rules to more dynamic, principle-based models that can keep up with AI systems. And finally, companies should shift from strict data control to data stewardship, focusing on how data is used and what outcomes it enables. The article also breaks down different data types — from transactional and master data to unstructured, synthetic, and real-time streams — emphasizing that each requires its own governance approach. Breman concludes as AI becomes more central to business strategy, strong data governance isn’t optional anymore. It’s the foundation that determines whether AI delivers value or creates risk.

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