Data, Insight, and Prediction: The Sense-Making Paradox

Welcome to an overview of Sense-Making in AI, exploring the central paradox that defines modern business intelligence: We are drowning in data and starving for insight.
Despite the explosion of information—with 90% of all data created in the last two years (a figure only growing with AI)—most organizations still utilize less than 5% of their total data for critical decision-making. What stands between this massive volume of data and genuine business meaning?

This segment explains the important shift from seeing AI just as a tool for automation to recognizing its true power as a sense-maker. We explain how Artificial Intelligence finds patterns and predicts results, allowing systems to learn and potentially improve their own learning. However, the pursuit of insight requires the essential element of Human Intelligence, which adds context, ethics, and creativity. Data shows you what happened, AI predicts what’s next, but humans decide what matters most. The most valuable insights are found where AI and humans meet. You will learn how to turn a simple pattern detected by AI into a practical business decision. We focus on the formula: “Insight = Pattern + Purpose.” An actionable insight must be surprising yet simple, backed by data and context, and lead to a specific action. While facts describe, insights explain and inspire agility in business.

Ultimately, this overview helps you understand the connection between Pattern, Meaning, and Decision, making sure that AI’s predictions are turned into hypotheses that lead to experimentation and business success.

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