Onetribe is a consulting firm specialising in management reporting, controlling, and finance function transformation for mid-market companies in Central Europe. Financial data quality is the degree to which financial data is accurate, complete, consistent, and timely. When data quality is absent, even the best report, dashboard, or KPI framework displays the wrong numbers — and leadership decides on illusion, not reality.
Most mid-market companies do not realise they have a data quality problem. They see symptoms: slow monthly closes, reports nobody trusts, numbers that change depending on who compiles them. ACCA’s Global Survey 2024 found that 62% of finance professionals spend significant time fixing data errors rather than analysing results. This article describes the five most common warning signs — and what to do about each one.
Sign 1: Two Departments, Two Truths
At the monthly leadership meeting, the sales director presents revenue of £1.2M. The finance director shows £1.15M. What follows is a thirty-minute debate about whose numbers are correct. No time remains for decisions.
This is not a people problem. It is a definitions problem. Sales pulls revenue from the CRM (booked orders). Finance pulls it from the ERP (invoiced). Both numbers are “correct” — they measure different things. Without agreed metric definitions, the company has as many truths as it has spreadsheets.
Gartner estimates that the average organisation maintains three to five “sources of truth” for the same financial data. At mid-market scale, where teams are smaller and systems less integrated, the problem is often worse.
What to do: Agree on one definition for each key metric. Document it: what exactly are we measuring, from which source, using which calculation. This is the foundation of a single source of truth (SSOT) . Not one system — one agreement.
Sign 2: Monthly Close Takes More Than Five Working Days
If the monthly close stretches beyond five working days, the bottleneck is rarely slow people. It is:
- Manual data extraction from multiple systems (ERP, CRM, payroll, bank feeds)
- Hand-built reconciliation between sources that should agree but do not
- Error correction from copy-paste mistakes
- Waiting for data from other departments
Research by insightsoftware shows that 75% of finance specialists spend five to six hours per week recreating reports they have already built — roughly 300 hours per year per person. The company pays a controller for analysis, but 80–90% of their time goes to assembly.
The Hackett Group benchmarks the gap: top-quartile finance organisations spend 30% less time on data reconciliation than their peers. The difference is not headcount or tools — it is governed data that arrives clean, consistent, and on time.
What to do: Map where the close loses time. It is almost always extraction and reconciliation, not the report itself. The fix starts with standardising data flows , not buying a new tool.
Sign 3: The Key Spreadsheet Lives in One Person’s Head
If your main reporting workbook — with its macros, cross-references, and custom formulas — can only be produced by one controller, the company is one resignation away from losing its reporting process.
BDO’s Mid-Market Report 2025 found that 62% of mid-market companies have experienced significant disruption when a key finance person left. Shadow spreadsheet solutions arise because the company has no standardised, governed path from data source to finished report. Each analyst builds their own system — and when they leave, the system leaves with them.
What to do: Document the reporting process. Not a fifty-page manual — a simple description: where data comes from, what happens to it, what gets checked, who receives the output. If nobody except “Sarah, because she knows” can describe it, the problem is governance, not the reporting tool.
Sign 4: Leadership Does Not Trust the Reports
This is the most expensive symptom. When leadership does not trust the numbers, they stop using the report. When they stop using the report, they decide by intuition, informal information, or “gut feel.”
Deloitte CFO Signals Q4 2025 found that 54% of CFOs cite data quality as a top-three barrier to decision-making. One visible error in a report is enough for leadership to distrust all numbers. Trust, once lost, takes months to rebuild.
The company then pays twice:
- For the finance team’s time compiling reports that nobody reads
- For decisions that leadership makes without data — which cost more than they save
What to do: Trust is built through consistency. Defined metrics, regular cadence, validation checks before distribution, commentary on key variances . When leadership sees that numbers are consistent and explained, trust returns — but it takes months, not weeks.
Sign 5: Every Report Requires Manual Corrections
If the controller routinely corrects numbers after generating a report — “because this row pulls wrong,” “because that cost centre should not be there,” “because the currency conversion does not work” — it is not routine maintenance. It is a signal that source data or transformation logic is broken.
Manual correction is the most expensive form of reporting. Every correction is a risk of introducing new errors. And nobody ever catches all errors — they catch only the ones they can see.
ACCA confirms: organisations where finance professionals spend the majority of their time on data correction consistently underperform on decision speed and quality compared to those with governed data processes.
What to do: Identify which corrections are recurring. A recurring manual fix equals a systemic problem in data or definitions. Fix the cause, not the symptom. If currency conversion is done manually every month, the problem is not currencies — it is that the ETL process does not exist or does not work.
What Poor Data Quality Actually Costs — The Hidden Bill
The cost of poor data quality does not appear on a P&L line, but it is real:
| Cost | Example | Estimate |
|---|---|---|
| Time on manual assembly | Controller: 90% preparation, 10% analysis | 30–50% of finance headcount wasted |
| Decision latency | Monthly numbers available at D+15, not D+3 | Unquantifiable but real |
| Lost trust | Leadership decides by intuition | Poor investment and operational decisions |
| Duplicate work | Two teams compile the same report differently | 2× cost for the same output |
| Regulatory risk | Mismatch between internal and statutory reports | Audit findings, fines |
McKinsey (2024) estimates that poor data quality costs organisations 15–25% of revenue in hidden inefficiencies. For a £10M mid-market company, that is £1.5–2.5M annually in wasted time, errors, and suboptimal decisions.
Automation can recover some of this — PwC estimates up to 40% of staff time through process automation. But automation works only on data that can be trusted. Otherwise, you automate errors faster.
The Data Trust Pyramid — Where Does Your Organisation Sit?
Use the Data Trust Pyramid to assess your current state:
| Level | Name | What It Looks Like |
|---|---|---|
| 1 | Chaos | No ownership, no definitions, manual everything. Board meetings debate numbers instead of decisions. |
| 2 | Controlled | Key metrics defined, one person owns the process. Close is under 10 days but fragile. |
| 3 | Connected | Systems feed a central data layer. Reports are consistent across departments. |
| 4 | Confident | Governed, monitored, audit-ready. Finance analyses instead of assembles. |
If you recognised three or more of the five signs above, your organisation is likely at Level 1 — Chaos. That is not a judgement. It is where BDO estimates 60–70% of mid-market companies sit. The path forward starts with the four governance pillars described in our data governance guide .
Frequently Asked Questions
How do I know if we have a financial data quality problem? If board meetings regularly discuss “whose numbers are right” instead of “what to do,” you have a problem. Other signals: monthly close longer than five days, reports requiring repeated manual fixes, different people compiling the same report and getting different results.
Will buying Power BI solve our data problems? No. Power BI or any BI tool visualises whatever data flows into it. If input data is inconsistent, BI displays the inconsistency more attractively. Fix data governance , metric definitions, and source systems first — then invest in visualisation.
How many people does data governance require at a mid-market company? You do not need a team. You need one person — a controller or finance director — who owns the metric definitions and is accountable for data quality. Plus agreed rules and a simple validation checklist. This is not an enterprise programme with fifteen-person teams.
What if we do not have a controller? Start with what you have. Agree on definitions for five key metrics. Document them in a shared file. Assign one person (even if it is the senior accountant or finance manager) to be accountable. An alternative is a fractional or external controller — which can be more cost-effective for companies without a full-time workload.
Where This Fits in Our Expertise
Financial data quality sits within the Reporting pillar at Onetribe — and it is the prerequisite for everything else. Without trustworthy data, there is no point building a reporting framework , designing KPIs , or automating reporting . Governance is the foundation on which the entire decision-support system stands.
Further Reading
- Financial Data Governance — Why It Is the Foundation of Trustworthy Reporting — the complete governance guide
- Management Reporting Framework — a framework that depends on trustworthy data
- Reporting Automation Fundamentals — automation without governance equals automated errors
- Business Intelligence Reporting — when to move from Excel to BI
- Profitability Analysis Fundamentals — analysis that requires data you can trust
- Reporting — Our Expertise — how we approach reporting
- Glossary: Data Quality | Data Governance | Data Validation | SSOT | Audit Trail
Sources
- McKinsey — “The Data-Driven Enterprise” 2024 — poor data quality costs 15–25% of revenue
- Deloitte CFO Signals Q4 2025 — 54% of CFOs cite data quality as top-3 barrier
- BDO Mid-Market Report 2025 — 68% of mid-market CFOs lack confidence in data consistency; 62% experienced disruption from key-person departure
- ACCA Global Survey 2024 — 62% of finance professionals spend significant time fixing data errors
- The Hackett Group — top-quartile finance: 30% less reconciliation time
- Gartner — average organisation has 3–5 “sources of truth” for the same financial data
- insightsoftware — 75% of finance specialists: 300+ hours/year recreating reports
- PwC — automating financial processes recovers up to 40% of staff time
- FRC Reporting Quality Review 2025 — increased scrutiny of data quality in UK reporting
Martin Duben is CEO of Onetribe — a consulting firm specialising in management reporting, controlling, and finance function transformation for mid-market companies in Central Europe. With over 15 years of experience, he helps CFOs and business owners build information systems that support decision-making. Contact: onetribe.team .