Financial reporting: mastering data for reliable figures
Case study: how an SME reduced reporting errors and clarified the true cost of financial closing.
Case study: how an SME reduced reporting errors and clarified the true cost of financial closing.
The DATALIA team · Published August 7, 2026 · Updated August 7, 2026
Direct answer — Context, action, result (50 words). An SME with 120 employees centralizes its entries across three systems. We consolidated the flows, automated data ingestion and defined a full cost model. Result: a 55% reduction in consolidation time and an estimated annual saving on the "re-entries" line.
Context
The subject of this case is a 120-person company, mid-sized annual revenue, with an accounting team of six and a centralized management control function. Monthly reporting required manual exports from the ERP, the banking platform and a third‑party billing tool. Closings extended over 12 business days, and the CFO wanted to make the figures reliable without increasing headcount.
Problem and objectives
Main problem: dispersed reporting data, multiple manual re-entries and undocumented consolidation rules. This produced unpredictable variances in the provisional income statement and time-consuming reconciliations.
Measurable objectives set by the CFO:
- Reduce closing time from 12 to 6 business days.
- Halve the volume of errors detected during post-close reviews.
- Produce a complete cost table to compare integrator quotes.
- Obtain a model to calculate the cost of manual re-entries for the executive committee.
Solution implemented
Approach: audit, prioritization, targeted automation and business acceptance testing. We followed a four-step method, documented and measurable.
1. Flow audit and source mapping
Objective: identify the sources of truth and the re-entry points.
Method:
- Inventory of exports and imports used for monthly reporting.
- Field observation: recording the time spent on each operation (entry, verification).
- Creation of a matrix [source field → destination → frequency → owner].
Deliverable 1 — Flow matrix (standalone):
Objective: map the data inputs for reporting.
To gather: ERP exports, billing file, bank statements, business owners.
Method:
- List each table/export used for reporting.
- Identify the transformation logic (e.g. analytical code → cost center).
- Note the frequency and the owner.
Output: matrix usable by the CIO/Deputy CIO for integration.
Note: useful to size data migration; not useful if exports change every month.
2. Prioritization and minimal viable scope
Objective: automate first the processes that deliver value fastest.
We applied a simple rule: prioritize repeated, manual flows consuming more than 8 cumulative hours per month. The initial scope covered sales, payroll entries and bank reconciliations.
3. Data uptake and transformation rules
Objective: replace manual re-entry with verified transfers.
Method:
- Build ingest scripts testable on an N‑2 month dataset.
- Validate business rules with the management controller (20 documented test cases).
- Implement transformation logs and an error dashboard.
Deliverable 2 — Model to calculate the cost of manual re-entries:
Objective: quantify the savings from avoiding re-entries.
To gather: average hourly rate per employee, number of manual operations/month, average time per operation.
Method:
- Calculate hours avoided = operations/month × time/operation.
- Avoided cost = hours avoided × hourly rate.
Output: annual amount avoided and expected project payback.
Note: calculates direct gain; does not include indirect gains (quality, timeliness).
4. Control and acceptance testing
Objective: verify that automated figures match supporting documents.
- Run a pilot over two partial closes.
- Validate by sampling: 10% of lines versus supporting documents.
- Quick correction plan and gradual production rollout.
Table: comparison of implementation options
| Option | Estimated initial cost | Timeline | Hidden risk |
|---|---|---|---|
| Script + targeted integration | 15 000–25 000 € | 6–8 weeks | Undocumented script maintenance |
| Commercial ETL module | 25 000–50 000 € + licenses | 8–12 weeks | License and vendor lock-in |
| Full ERP overhaul | 100 000 €+ | 6–18 months | High total cost of ownership |
Results
Measured over the three closes following the gradual rollout (period: month 0 to month 3 after deployment).
| KPI | Before | After | Change |
|---|---|---|---|
| Average closing duration | 12 business days | 5.5 business days | -54 % |
| Monthly hours spent on re-entries | 220 h | 95 h | -57 % |
| Errors detected in post-close review | 18 anomalies / month | 9 anomalies / month | -50 % |
| Estimated avoided cost (annual) | — | ~68 000 € | — |
The avoided cost calculation combines the average hourly rate for the accounting role and the hours saved over the year. This figure is now used in the internal financing file.
Full project cost (example with figures)
The CFO wants to compare vendor offers. We provide here a table of items that quotes often forget.
| Item | Often included | Reasonable cost |
|---|---|---|
| Historical data migration | Sometimes excluded | 5 000–12 000 € |
| Licenses / subscription | Often included 1 year | 1 200–6 000 €/an |
| Evolutionary maintenance | Frequently forfaited | 10–20 % of project / year |
| Training and business acceptance | Sometimes limited | 3 000–8 000 € |
| Change management | Rarely priced | 2 000–6 000 € |
What didn't work
Classic mistake: trying to automate too many cases at once. We attempted to automate exceptional correction entries. Result: high maintenance costs and false business alerts. Fix: revert to the principle "automate the normal path, leave exceptions to humans".
Another point: an initial quote underestimated the migration of poorly populated analytical codes. We had to add a manual cleanup phase. The CFO drew two lessons: include migration in the scope and set aside a budget reserve for unforeseen corrections.
Key takeaways
- A data reliability project is won at the scoping stage: document sources and rules before sizing.
- Budget the full cost: licenses, migration, maintenance and change management. Without this, quotes are not comparable.
- Automate in waves: start with repeated, low-exception flows.
- Implement logs and quantified acceptance tests (e.g. 20 business cases).
- Measure: present an avoided-cost model to the committee to defend the investment.
Simple operational model to compare quotes
Objective: make three integration quotes comparable.
To gather: priced quotes, functional scope, lists of exclusions.
Method:
- Break down each quote by item (migration, licenses, maintenance, training).
- Record exclusions and price them separately.
- Calculate total cost over 3 years (3-year TCO).
Output: 3-year TCO comparison table, "points of attention" column.
Note: requires an "assumptions" section to make the comparison honest.
Limits of the approach
What this approach will not solve: upstream data quality (erroneous billing), nor disputed accounting choices. Automation fixes repetition and traceability, but does not replace business judgement. Internal control and manual review remain necessary for non-routine operations.
Scaling up in your finance department
For a CFO, the decision often comes down to: what is the budget breaking point and what will the full cost be? We recommend a 6–8 week pilot on a limited scope (sales and bank reconciliations) with three deliverables: flow matrix, cost model and acceptance protocol. These deliverables then serve as the basis for a comparative RFP.
DATALIA field observation: across several projects, the real payback threshold is often between 8 and 18 months depending on the number of manual operations per month. This threshold depends on the average hourly rate and the volume of lines processed.
Frequently asked questions
How much does data migration typically cost?
The range is €5,000 to €20,000 depending on volume and initial quality. Always ask the provider for the exact migration scope and a days/person estimate to enable comparison.
Can an SME amortize a reporting project in under a year?
It depends on the number of hours eliminated. If re-entries account for more than 150 hours per month, payback in 8–12 months is plausible. Use the cost model presented in this article to size your case.
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