DRAFT Framework · Pillar 01 of 05
Back to overviewClean, connected and decision-ready data — for teams that need one trusted version of the numbers before they can model, report or act.
Book a Data discovery callWhat this pillar delivers
Data problems rarely announce themselves as data problems. They show up as slow reporting, conflicting numbers, manual reconciliations and leadership meetings where nobody fully trusts the pack.
The Data pillar creates the structured foundation beneath finance, reporting, tax and risk work. We map source systems, clean and reconcile key fields, design the data model and build dashboards or exception reports that finance teams can actually maintain.
The goal is not more dashboards. It is one reliable data layer that helps the business see what is happening and decide what to do next.
A reconciled data model connecting GL, AR, AP and operational drivers into a dashboard-ready structure, with definitions documented and ownership clear.
See a sample modelServices in this pillar
Every Data engagement closes with a working data asset, documented fields, and a handover your team can operate.
"We identify where finance and operating data lives, how it moves, and where definitions break down."
We identify where finance and operating data lives, how it moves, and where definitions break down.
"We clean core fields and reconcile outputs so reports tie back to the books."
We clean core fields and reconcile outputs so reports tie back to the books.
"We build a data model designed for dashboards, not one-off spreadsheet exports."
We build a data model designed for dashboards, not one-off spreadsheet exports.
"We create management dashboards that connect to source data and answer decision questions."
We create management dashboards that connect to source data and answer decision questions.
"We reduce manual rekeying by structuring repeatable data flows across tools."
We reduce manual rekeying by structuring repeatable data flows across tools.
"We define ownership, naming, definitions and review rhythms so the data asset stays reliable."
We define ownership, naming, definitions and review rhythms so the data asset stays reliable.
Data Engagement — Case Study
"From five conflicting spreadsheets to one reporting layer the leadership team trusted."
A growing services business had revenue, project and finance data split across three systems. Monthly reporting required manual exports and every leadership meeting opened with a debate about which number was right. We mapped the data flow, cleaned the key dimensions, reconciled the GL bridge and built a dashboard-ready model with definitions documented. The management pack moved from manual assembly to controlled refresh, with exceptions visible before the meeting.
How a Data Engagement Runs
We move from source discovery to a governed reporting asset your team can maintain.
Identify systems, owners, fields and business definitions.
Resolve duplicates, gaps and mismatches against source records.
Create reusable structures for dashboards and reports.
Document definitions, controls and ownership.
Thirty minutes. We map the gap, identify the working asset needed, and tell you exactly what we'd build.