Reading a datapoint score in context

A datapoint score does not stand alone. It contributes to a vector score, which contributes to a pillar score, which contributes to the headline AS. The practitioner reads a datapoint score in three layers:

Layer 1: The datapoint score itself. What does this number mean? Is the datapoint at floor, low, medium, high, or near ceiling? The score’s distance from floor is the first-order diagnostic.

Layer 2: The datapoint’s relationship to its parent vector. Is this datapoint dragging the vector down (datapoint is much lower than other datapoints in the same vector), holding it up (datapoint is much higher than others), or moving with it (datapoint is near the vector average)? The relationship determines whether work on this datapoint will move the vector or whether the vector’s score is determined by other datapoints.

Layer 3: The datapoint’s relationship to OMG action selection. Which OMG actions could lift this datapoint? Are those actions appropriate for the brand’s current stage maturity? Are their prerequisites in place? Section 6 of each datapoint page provides the cross-reference.

The three-layer reading produces an action recommendation. A datapoint at floor with multiple actions available and prerequisites in place is a candidate for next-cycle work. A datapoint at floor with prerequisites unmet is a candidate for prerequisite work first. A datapoint near ceiling is a candidate for maintenance, not improvement.

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