Analytics that changes a decision, not just a dashboard.

Most organisations do not have a reporting problem. They have a problem where the report exists, nobody trusts the number, and the decision gets made on instinct anyway.

01 / The problem

Where this usually goes wrong.

The usual symptom is three teams quoting three different revenue figures in the same meeting, all of them technically correct, all derived from different definitions nobody wrote down. The second symptom is a dashboard that took a quarter to build and gets opened twice a month.

Both are the same underlying failure: the metric layer was never agreed, so every consumer of the data quietly built their own. Fixing the visualisation does not touch this. Fixing the definitions does.

02 / What we build

The parts of the system.

Ingestion and modelling: Scheduled extraction from your ERP, CRM, billing and operational systems into a warehouse, modelled into tables that mean something to the business rather than mirroring whatever schema the source system happened to use.
A metric layer with owners: One definition of revenue, one of active customer, one of on-time delivery — written down, version-controlled, and attached to a named person who arbitrates when the definition needs to change.
Quality checks that fail loudly: Row counts, null rates, referential integrity and distribution drift, tested on every load. A pipeline that silently loads yesterday's file twice is worse than one that stops.
Reporting people actually open: A small number of views tied to decisions someone makes on a schedule, rather than a wall of charts covering every column in the warehouse.

03 / How it is measured

What we agree to be judged on.

Set before the build starts, against a measured baseline, and reported honestly afterwards — including where the numbers are disappointing.

Freshness: Time from a business event to it being visible and correct in reporting, tracked as a service level rather than assumed.
Reconciliation: Warehouse figures tied back to the source system of record to an agreed tolerance, checked automatically and reported when it breaks.
Definition coverage: What proportion of metrics in active use have a written, owned definition. This starts embarrassingly low almost everywhere.
Actual use: Which views are opened, by whom, before which decisions. Views nobody opens get retired rather than maintained forever.

04 / Honest limits

What this will not do.

Analytics tells you what happened and, carefully, why. It does not tell you what will happen — that is forecasting, and it demands different evidence and a different tolerance for being wrong.

It also cannot resolve a genuine disagreement about what the business should optimise for. If two directors want different things, better numbers will not settle it; they will just make the argument more precise.

05 / When it applies

You probably need this if:

Different teams quote different numbers for the same thing
Month-end reporting depends on one person and a spreadsheet
Nobody can say how a headline figure is actually calculated
Dashboards were built and then quietly abandoned

Recognising two or more of these is a reasonable trigger for a diagnostic. Recognising none of them is a reasonable trigger for not spending money here yet.

06 / Related

Often scoped together.

These capabilities share data, infrastructure or evaluation approach with data analytics, and are frequently part of the same engagement.

Next step

Is data analytics the right instrument for your problem?

The diagnostic exists to answer exactly that, including the possibility that the answer is no. It is time-boxed, fixed-fee, and the written assessment is yours.

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