A 5-Part Data Model Review That Should Come before Any EPM Programme Is Priced

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Editor’s Note: This article is contributed by Alex Townend, the CEO of Constancia. Constancia is an EPM consultancy that makes multi-entity finance data AI-ready. We diagnose the data model before we quote. The price is then fixed.

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Enterprise performance management (EPM) programmes in multi-entity groups tend to overrun for the same reason. The scope and budget are agreed before anyone has examined the data model the new platform will sit on.

The data model is how a group’s entities, accounts, currencies and intercompany relationships are structured. Consolidation, planning and reporting all read from it. If it is inconsistent, a new platform reproduces the inconsistency faster.

This article sets out a 5-part review that a finance leader, or the adviser supporting them, can run before a price is agreed. Each part has a question to answer, the evidence to collect and what a warning sign looks like.

1. The Entity Structure Shows Whether the Group Can Be Consolidated Cleanly

Question: does every legal entity map to the group structure in one agreed way?

Evidence:

• a list of entities with their ERP, currency and ownership percentage
• the reporting hierarchies used for statutory and management views
• any entity that is consolidated outside the main tool

Warning sign: management and statutory views are built from different hierarchies, and nobody can say when they last reconciled.

2. The Chart of Accounts Shows How Much Translation the Close Depends On

Question: how far do local charts of accounts differ from the group chart, and how is the gap bridged?

Evidence:

• the mapping tables from each local chart to the group chart
• who maintains them, and how changes are approved
• accounts mapped to “other” or a suspense line

Warning sign: mappings live in spreadsheets maintained by 1 or 2 people, with no change log.

3. Intercompany Matching Shows Whether Eliminations Can Be Trusted

Question: are intercompany balances matched by the system, or by people at month end?

Evidence:

• the intercompany matching process and its timetable
• the value of unmatched differences at the last 3 closes
• any manual elimination journals

Warning sign: unmatched differences are written off at group level every month rather than resolved in the entities.

4. Manual Adjustments Show Where the Real Close Happens

Question: which numbers in the final reports were changed after the system produced them?

Evidence:

• top-side journals and their reasons for the last 3 closes
• reports that are finished in a spreadsheet before they go to the board
• adjustments that recur every month

Warning sign: the same adjustment appears every month. A recurring adjustment is a rule the system should hold and does not.

5. History and Definitions Show What the New Platform Can Be Trusted With

Question: how far back can the data be trusted, and do the documented definitions match what runs?

Evidence:

• the number of years of history available at the level of detail planning needs
• restatements after acquisitions or restructures
• definitions of headline measures, such as EBITDA or headcount, compared with how they are calculated in practice

Warning sign: planning needs 3 years of comparable history, and only 1 year exists on a consistent basis.

The Output Is a Written List of Risks Tied to the Work They Would Affect

The review ends with a short document. For each finding it states the risk, the part of the programme it would affect and what it would cost to fix.

That document changes how a programme is priced. Scope is set against what is actually in the model. Where the data is worse than expected, the cost is known before anyone commits, so it does not arrive later as a change request.

This is the sequence we follow in EPM consulting. We diagnose the data model before we quote. The price is then fixed.

The Review Matters Most When a Group Has a Date It Cannot Move

The groups that gain most from this are those with a complex structure and a fixed deadline. Typical deadlines are:

• an IPO or audit readiness date
• an integration after an acquisition
• a new CFO’s first plan
• an ERP replacement
• a board asking for AI that the finance data cannot yet support

Each of these asks the data model to carry more than it was built for. Each leaves little room for a programme that overruns.

3 Questions to Put to Anyone Pricing an EPM Programme

Whether the work is done internally or by a firm, these questions separate a plan from an estimate:

1. Has anyone been into the data model before producing a number?
2. What happens if the data turns out worse than expected, and who pays for that?
3. Is the AI foundation being built, or assumed?

If the answer to the first is no, the 5-part review above is the place to start.

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