Articles about using data, measurement, and uncertainty to make better decisions.
21 articles
Maintaining an architecture starts with distinguishing a signal worth recording, a small change with a clear owner, and a structural decision that deserves deliberate review.
Who needs to see a piece of talent data, in how much detail, and for how long? The answer begins with the authorized task, not hierarchical rank.
To decide what comes after the pilot, examine which decisions changed, which outcomes followed, and what it costs to sustain the mechanism. Participation and coverage do not yet demonstrate value.
An older assessment may still guide development yet be insufficient for assigning a critical responsibility. Its date alone does not determine whether it remains useful.
The book Sorting Things Out examines how categories and standards organize the world while distributing visibility, work, and consequences. Reading it through the lens of a skills architecture extends that inquiry to the decisions a taxonomy makes possible.
What can AI claim about a skill from digital traces? Moving from data to signal, inference, and decision involves leaps that require control.
What should happen when someone challenges an assessment? A review should examine data, rules, interpretation, and use without turning the outcome into a negotiation.
Before scaling an architecture, define which decisions need an owner, a threshold, traceability, and the ability to pause.
A correlation between skills and outcomes is useful for opening an investigation. A claim of contribution requires an explanation of the mechanism, the timing, and the alternatives.
Even when usage is high, a model may not yet have been adopted: the decisive evidence appears when it changes a real decision and that change can be reconstructed.
What makes an assessment valid for a specific decision? The answer connects the intended use with the evidence required, the interpretation that can be supported, and the limits of any conclusion.
Organizations can measure well and decide badly when their model leaves out dynamics that are already affecting coordination, trust, and capability.