Measures

Measures are the numbers you track on your elements: cost, headcount, risk, adoption, or any figure you want to record and roll up. One consistent model sits behind all of them, so cost, KPIs, and custom figures behave the same way and can be trended, aggregated, and scored together.

Core concepts

• Definition. A measure definition names what is being measured and how it behaves. Related measures are grouped into families and subtypes so they stay consistent across the portfolio.
• Value. The number recorded for a measure on an element. A value carries a reference, so the same measure can hold an actual and a planned figure side by side.
• Over time. Measures can be read as a series across periods, so a figure can be charted or trended, not just read as a single number.
• Rollup. A measure can aggregate from one element up across a whole population, so you can read a portfolio total, not just a per-record number.
• Coverage. Every rollup reports how completely it is populated, so a partial total is never mistaken for a full one.

How you work with measures

You start by defining a measure: what it captures and how it behaves. Then you record values for it on individual elements, each with a reference such as actual or planned, and over as many periods as you need. From there a measure can roll up across a population to give a portfolio total, and report how completely that total is populated.

Where you see it

• The Measures panel on a record shows its measures and their values, editable in a grid.
• Visualizations such as the Gravity Map and TIME use measures to position and size records.
• Cost, KPI, and similar figures are all measures, so they share one consistent model.

How it fits together

A measure holds a fact. Scoring turns measures into a rated judgment, and Data Governance can check that the measures that matter are populated before you rely on them.

Screenshots and guides for defining measures and entering values will follow in this section.