Michael Lissack
interpretational frames, and distributed observation points that no indi- vidual or team could maintain. The most effective intelligence systems integrate these capabilities rather than treating them as separate domains. Consider how Unilever approaches consumer intelligence. Rather than relying solely on tradi- tional market research (human intelligence), algorithmic social media analysis (data intelligence), or customer community insights (network intelligence), they’ve created integrated sensing systems that synthesize across all three domains. This approach helped Unilever identify the emerging natural beauty movement years before it became mainstream. Their algorithmic systems detected early signals in social media conversations about chemical-free products. Human researchers contextualized these patterns through eth- nographic research with early adopters. And their network intelligence systems gathered insights from distributed experts including dermatolo- gists, ingredient suppliers, and beauty influencers. This multidimensional intelligence enabled Unilever to develop natural product lines that antici- pated rather than reacted to market evolution. From Information Gathering to Sense-Making Perhaps the most profound shift in organizational intelligence isn’t about gathering information—it’s about making sense of what’s gathered. In complex environments, collecting data is necessary but insufficient. Organizations must develop capabilities to transform information into insight and insight into action. This sense-making process involves several distinct but intercon- nected activities: Pattern Recognition identifies meaningful signals within noisy in- formation environments. It detects correlations, trends, anomalies, and emerging structures that might indicate significant developments. Pattern recognition combines human intuition with technological analysis to surface potentially meaningful dynamics.
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