Data Governance: The Hidden Bottleneck in Scaling Finance

In brief

Data governance is rarely on the CEO’s or investor’s agenda — until something breaks. A failed integration. A liquidity shock. A forecast miss. A commercial error. A board deck that tells three different stories.

Behind almost every performance surprise in a €50m–€1bn+ company lies the same root cause: no single data model, no defined owners and no operating discipline around the numbers.

Finance becomes the “translator” between systems instead of the operator of a single truth.
Commercial teams run shadow models because the central one isn’t trusted.
Forecasts degrade because the inputs and definitions are inconsistent.
Integrations stall not on strategy, but on data, mapping and master data ownership.

Data governance is not an IT problem. It is a leadership problem. And the CFO owns it — by design or by absence.

If data debt is blocking planning and credibility, this is often part of a broader finance build-out (see scaling the finance function).

1. Data governance fails in scaling companies for predictable reasons

The pattern repeats across industries and ownership models. When companies move from €20m to €200m to €1bn+, their data problem does not scale; it compounds.

The five structural failure modes:

No single owner. IT thinks it’s a finance problem. Finance thinks it’s a systems problem. Commercial thinks it’s blocking growth.
Definitions drift. Gross margin, net revenue, “active customer”, “booking”, “churn” — each team has its own version.
Master data is nobody’s job. SKUs, items, parks, channels, suppliers, cost centres, price lists — inconsistent, duplicated, outdated.
Integrations collapse under data debt. Carve-outs or acquisitions suffer not from strategy misalignment but from missing data mapping and ownership.
Forecasting becomes narrative-driven. When data is unstable, forecasts become opinion polls, not operating tools.

If you don’t deliberately design data governance by the time you hit €100m+, the organisation will create its own versions — and you will spend two years cleaning it up.

2. What “good” data governance looks like in a PE-backed or high-growth business

Good data governance is not bureaucracy. It is a set of simple, explicit choices about how the organisation defines, owns and uses data for performance. In fast-moving businesses, it must be light, pragmatic and operator-led.

The core elements:

A single data model across finance, commercial, operations and product.
Clear data ownership — one person owns master data quality, not a committee.
Shared definitions for revenue, margin, customers, units, stock, bookings, cash items.
Integrated systems with clean mapping between ERP, CRM, EPM, data warehouse and BI.
Documented transformations — the pipeline from raw data to KPI must be visible.
Change control — updates to definitions or structures cannot be done ad hoc.
Performance rhythm anchored on this single truth.

The biggest misconception is that data governance is a “project”. It is a continuous operating discipline — and must be treated as such.

3. Why CFOs must own the data model — not IT

In high-growth or PE-backed environments, data governance only works when the CFO (not IT) owns the data model.

Three reasons:

The CFO owns the numbers that go to the board and investors. If the CFO cannot stand behind the data, the CEO loses credibility.
Data reflects the operating model. Finance is best placed to translate business model mechanics into data definitions.
Systems and data flow from decisions, not from technology. IT implements; finance decides what must be true.

IT’s job is to ensure stability, security, infrastructure and scalability. The CFO’s job is to define how performance is measured and what “one truth” means.

The CFO does not need to become a data engineer

But the CFO must set the rules of the game, own the definitions, decide the KPI tree, structure the data model and act as the final escalation point. Without this, the organisation reverts to department-level definitions.

4. The root cause behind slow forecasting, weak visibility and integration pain

Forecasts do not fail because FP&A is weak. Forecasts fail because the underlying data landscape is unaligned.

Symptoms CFOs and CEOs recognise:

Every month, numbers change late in the process.
Finance spends more time reconciling than analysing.
Commercial reviews use different numbers than finance.
Margin waterfalls differ depending on who presents them.
Cash flow forecasts fluctuate because source data is inconsistent.
Integration plans stall because nobody knows which system is “real”.

These are not FP&A problems. They are data governance problems.

Once companies accept this, the roadmap becomes much clearer — and forecasting accelerates within weeks, not quarters.

5. A practical, operator-led approach to data governance

You do not need a 200-page data strategy or a governance council that meets monthly and produces nothing. You need a simple, operational framework everyone uses.

The five-step model

Define the KPI tree. Start with how the CEO, CFO and board want to run the business. Link every KPI to its formula, owner and data source.
Build the core data model. Define entities (customers, items, parks, SKUs, channels), attributes, hierarchies and how they map across systems.
Assign clear ownership. One owner for master data. One owner for definitions. One owner for transformations. No committees.
Document the transformation pipeline. Raw → mapped → transformed → validated → reported. If this is not explicit, forecasting fails.
Embed change control. No definition changes without CFO approval. No new KPIs without aligning the data model first.

This model took global organisations (Danone, Mars) and complex integrations (Landal/Roompot) from chaos to structure — and it works in €20m companies just as well as in €1bn companies.

6. How to fix data governance during integration

Integration is where data governance proves its value — or its absence.

Three principles:

Stabilise before optimising. Close control over cash, margins and reporting comes before unifying systems.
Enforce one truth immediately. Even if systems are separate, definitions must be unified from day one.
Run a joint data room. Map entities, hierarchies, cost centres, groups, customers and products across both businesses.

Integrations don’t slow down because of strategy disagreements; they slow down because data structures don't align.

7. The CEO and board-level view: what data governance unlocks

For CEOs and investors, data governance is not a technical concept. It is a lever for speed, risk management and value creation.

What it unlocks:

Speed: faster decisions, shorter forecasting cycles, fewer surprises.
Visibility: clarity on margins, liquidity, pricing, utilisation and performance drivers.
Control: governance, audit, compliance, reliability in external reporting.
Scalability: systems and processes that do not break at €200m, €500m or €1bn.
Integration readiness: ability to absorb acquisitions without data chaos.
Exit readiness: investor-grade numbers, consistent definitions, credible reporting.

Strong data governance is not an overhead. It is a strategic asset that determines how fast a business can scale.

8. Closing thought

Most data problems do not require new systems. They require decisions.

Once a CEO or CFO defines the single version of the truth and enforces ownership, the organisation aligns quickly — and finance shifts from reconciliation to leadership.