Make Data SenseGovernance engine
Make Data SenseGovernance engine
Multi-academy trust boards rarely have evidence they can read across financial, educational, demographic and estate pressures at once. Make Data Sense classifies the trust’s own attested data into clear, explainable, auditable positions, so the board can see where each school stands, metric by metric, and decide with confidence.
Start a conversationFor each school, at each reporting cycle, MDS classifies every metric into one of four bands (Stable, Emerging, Elevated or Severe) and shows exactly how each band was reached. It surfaces evidence and classifies; it does not make the decision. The board reads the evidence and decides.
The same inputs always produce the same outputs. The logic is fixed and rule-based, with no hidden adjustments and no AI making the classification.
Every metric is read on its own evidence. No composite score, no weighted ranking, no league table of schools. The board reads the pattern; the engine ranks nothing.
Every band comes with its determination basis: the value, the threshold it was measured against, the source it came from, and the rule that was applied.
Values are attested by named leaders and carry provenance. Each cycle is published as an immutable snapshot the trust can reproduce and stand behind.
Named senior leaders confirm each metric value, with its source recorded against it.
The engine bands every metric against fixed thresholds; the same inputs always give the same result.
The Clerk publishes the cycle as an immutable snapshot the trust can reproduce.
The board reads the evidence, in depth or across the trust, and decides.
Every metric across every domain for one school, with movement and history, and the determination basis behind any figure.
Where pressures sit school by school, and where they co-occur across domains, which single-committee papers can’t show.
Metrics read against the trust’s own strategic objectives, so the board can see where it is on track and where it is falling short.
MDS is not a BI tool, an analytics workbench, a forecasting engine, or an AI that recommends decisions. Those blur the line between evidence and judgement, and make uncomfortable findings easy to reweight. MDS keeps the classification deterministic and the decision with the board.
Make Data Sense grew out of estates and governance work inside a multi-academy trust, where the method behind it was built and used across board reporting cycles. It is now in development as a product for trusts more widely.
Make Data Sense is in active development. We’re talking with trust leaders, governance professionals and sector specialists who want to help shape it as it develops. If that’s you, start a conversation.