Lens
What can we reliably know, represent and preserve?
17 articles in this Lens
We’re obsessed with data, but our dashboards often make us dumber. We track everything yet decide nothing. More data, less judgment. This isn't a tooling issue, it's a thinking one. This 11-principle series shares ideas on how to build judgment in an AI era that demands it.
Read articleData-driven is not enough. Data-informed restores judgment but keeps you in the present. Knowledge-driven turns decisions into reusable assets. A simple template and review loop help teams learn faster, cut rework, and scale better choices.
Read articleI found a file in my LinkedIn data export I could not explain. Ten categories. A credibility score. A notability tier. Twenty-one dated snapshots, all identical. I published throughout that period. Nothing moved.
Read articleAn introduction and eleven principles that move from passive data worship to deliberate signals, decision systems, learning and AI-assisted diagnosis.
Explore seriesA progression from filtering signal to creating context, establishing credibility and synthesizing trusted inputs into new understanding.
Explore seriesI found a file in my LinkedIn data export I could not explain. Ten categories. A credibility score. A notability tier. Twenty-one dated snapshots, all identical. I published throughout that period. Nothing moved.
Read articleEnterprise knowledge becomes valuable when information is connected to context, credibility and decisions, not merely stored.
Read articleInformation is abundant. Insight requires context, relevance and judgment. A framework for moving from generated information to useful understanding.
Read articleData-driven is not enough. Data-informed restores judgment but keeps you in the present. Knowledge-driven turns decisions into reusable assets. A simple template and review loop help teams learn faster, cut rework, and scale better choices.
Read articleOpenAI’s ChatGPT usage report is consumer-only, so enterprise signal is skewed. Still clear: AI is a copilot. Writing dominates, most of it editing. Younger users drive volume; older pros drive work. B2B: prioritize decision support, co-creation, embedded copilots, governance.
Read articleAI will not kill search; it crowns whoever controls a fresh index. Synthesis engines give fast answers but depend on live, trusted crawling for freshness, coverage, and provenance. The future is hybrid: shelves and summaries, with winners owning both discovery and explanation. Clicks fund freshness.
Read articleMost teams have great dashboards that report the past, not systems that drive decisions. A thinking system starts with a clear question, ties metrics to actions, supports diagnosis, and treats AI as a partner for speed, not a substitute for judgment. Goal: better decisions, not prettier charts.
Read articleTeams don’t argue about numbers; they argue about definitions. Inconsistent metrics like MAU erode trust, stall decisions, and mislead AI at scale. Fix it upstream: build a Metric Dictionary with clear names, sources, formulas, and owners. One name, one definition.
Read articleBlended portfolio metrics become Franken-metrics: pretty roll-ups that mask product realities. Treat each product as its own system with distinct health and leading signals. Synthesize across products instead of averaging. AI clarifies only when signals stay separate. Or it polishes the camouflage.
Read articleOne-size dashboards are a myth. Build three layers: Outcome for execs (telescope), Driver for teams (levers), Deep Dive for analysts (microscope). AI enriches layers, it doesn’t flatten them. The aim isn’t more charts; it’s scaling clear decisions at the right altitude. Make layers reduce noise. Go!
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