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Preparing business data for automated regulatory reporting means controlling how each reported value is defined, sourced, transformed, checked, approved, and traced—not simply exporting a file. Start with the rule set that applies to the entity and reporting date, then build and validate a governed path from source records to the submitted output.
The workflow below is general guidance, with European Banking Authority (EBA) and European banking examples where useful. Fields, formats, deadlines, and checks are specific to the applicable jurisdiction, regulator, entity, report, and reference date.
1. Establish which reporting requirements apply
Before extracting data or building a report, establish the reporting perimeter: who is filing, what is being filed, to which authority, for which reference date, and under which framework release. A template or field that applied to another entity or a previous cycle is not automatically applicable now.
Record the legal entity and organizational scope, regulator, report type, reporting reference date, submission channel, and applicable framework. Confirm whether requirements apply to the entity and which parts are in scope. For EBA reporting, the EBA reporting frameworks page organizes requirements by framework and reference date and provides technical materials. Treat the relevant legal act as authoritative: the EBA notes that consolidated framework text on its site is an unofficial documentation aid, while legal acts are published in the Official Journal.
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Keep the reference date distinct from the date the team prepares or transmits the file. The former identifies the reporting period or point in time to which the information relates; the latter belongs in the run record. This distinction helps reviewers see whether a value is appropriate for the reporting period even if it was extracted or corrected later.
Build a perimeter record
For each reporting run, keep a concise record of the decisions that determine scope:
- Reporting entity and any organizational units included.
- Regulator, framework, report or register, and reference date.
- Applicable release of instructions and technical package.
- Submission route and any report-specific filing requirements.
- Decision owner and evidence supporting the scope determination.
If applicability is uncertain, resolve it against the current official instructions and relevant legal act before coding transformations. A technically valid file can still be the wrong report, period, or entity.
2. Obtain and lock the current specification
Collect the legal text and regulator instructions together with the technical artifacts needed to create and test the submission. Depending on the framework, these may include templates, data-point definitions, taxonomy or serialization specifications, filing rules, validation rules, and enumerated code lists. Record each artifact’s release or version and retrieval date with the reporting run.
Do not assume that a file used last cycle remains current. Regulators update technical packages, and validation rules can change independently of an organization’s source systems. The EBA reporting frameworks page notes updated validation-rule packages; check the relevant official framework page for each cycle rather than carrying forward old assumptions.
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Use the DORA register materials as a concrete example
For entities in scope of the EU Digital Operational Resilience Act (DORA), the EBA’s DORA register preparation page provides an example of a regulator’s preparation resources. It links a register data model, technical package, sample files, validation and business checks, explanations of data-quality feedback, and common issues from testing. The page states that DORA became applicable on 17 January 2025. Those materials and that date concern DORA and the entities in scope; they are not a universal reporting checklist or deadline.
Store the exact package used, not just a bookmark to a webpage that may later point to a newer version. Retain the package files or a controlled reference to them, along with the date obtained and a clear release identifier. That gives reviewers a way to understand which requirements and checks governed a particular output.
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Turn each required reporting concept into a documented mapping from the target report to the business meaning and authoritative source. The map should explain both what a value means and how the value reaches the output. A field name alone is not enough: similar labels across systems may represent different definitions, units, dates, or aggregation levels.
The EBA describes its Data Point Model (DPM) as “the data dictionary providing the structured representation of the data required for regulatory purposes by the EBA.” The model represents regulatory data, business concepts, relationships, and validation or calculation rules. See the EBA DPM data dictionary. The important practical implication is to map relationships among concepts as well as individual report fields.
Document the mapping at field level
For every target field or data point, capture at least the following information:
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- Target concept and its definition in the applicable instructions or model.
- Authoritative source system, source field, and accountable business owner.
- Extraction method and transformation logic, including joins, precedence, and aggregation.
- Units, currencies, enumerated codes, and identifier conventions.
- Relevant organizational level and effective date or time basis.
- Treatment of missing, unknown, or not-applicable values, as permitted by the instructions.
- Relationships to other reported concepts and any calculation or reconciliation rule.
When multiple systems contribute to one output value, document which source takes precedence when records disagree, how records are joined, and how totals or derived values reconcile to their inputs. Make business owners accountable for definitions and meaning, and technical owners accountable for reproducible extraction and transformation. The exact division of responsibility depends on the organization, but the handoff itself should be explicit.
Assign accountability across the data flow
Responsibility should follow the value through each handoff: source-system capture, extraction, transformation, report assembly, validation, review, and filing. The Basel Committee’s progress material identifies clear data-quality roles and responsibilities across the end-to-end flow as a critical element of effective risk data aggregation and reporting. Its discussion is specific to that context, but the operational principle is useful: an exception should have an identifiable owner rather than disappearing between teams. See the Basel Committee progress report.
4. Profile source data and verify transformations
Run data-quality checks before transformation and again on the report-ready result. Source data can be incomplete or inconsistent, while joins, mappings, aggregations, and code conversions can introduce new defects. Testing only the final file makes it harder to identify where a problem began.
Check source records before mapping
Profile incoming data for completeness, valid values, duplicate records, stale records, unit or currency mismatches, inconsistent identifiers, and unexpected changes from prior periods. Where applicable, check that the source record belongs to the right entity and is effective for the reporting reference date. Route issues to the person responsible for the source or business definition rather than automatically correcting values in the reporting layer.
Check the prepared output after mapping
After transformations, verify that codes and units match the target specification, aggregation levels are correct, required relationships are preserved, and calculated or aggregated values reconcile to their source records. Confirm that missing-value handling follows the instructions rather than relying on a generic blank, zero, or default. A file may pass a format check while still containing a semantically incorrect value.
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Automated validation can test report data against defined rules. XBRL International describes validation as “the process of carrying out automated data quality checks on XBRL reports.” Its validation guidance explains that validation capabilities are included in XBRL software and that regulators may apply additional business-rule checks. The validation results help identify issues; they do not, by themselves, establish the business cause of an issue.
5. Validate drafts against the applicable rules
Validate early enough to investigate defects before the filing deadline, and repeat validation as the report changes. Apply technical-format checks as well as the current taxonomy and regulator business rules. Generic schema validation alone cannot demonstrate that the submission meets the relevant authority’s requirements.
- Check the package. Confirm that the draft uses the prescribed format, taxonomy or serialization specification, code lists, and filing conventions for the applicable release.
- Run the current rule set. Use the regulator’s prescribed validation package and any relevant authority-specific checks, not only checks embedded in a local export process.
- Review all results. Separate errors, warnings, and other feedback; capture the affected report item, rule, input value, and run or package version.
- Investigate the cause. Determine whether the source value, field mapping, transformation, rule interpretation, or reporting scope is responsible—or whether the result is a legitimate exception under the instructions.
- Correct and rerun. Apply an approved change, rerun the affected checks and any dependent controls, and preserve both the earlier result and the resolution.
For European banking reporting, the Deutsche Bundesbank describes measurable EBA and ECB data-quality checks, including compliance with EBA validation rules and additional ECB checks. Some checks compare results over time or against peers. Which checks and thresholds apply depends on the framework and authority; do not treat those examples as universal rules. Details are available from the Bundesbank’s page on data quality in harmonised European reporting.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Triage exceptions instead of suppressing them
A failed check is a prompt to investigate, not an instruction to change a value blindly. XBRL International describes validation outcomes as OK, warning, or error. An error normally indicates a serious concern and may cause rejection; a warning should still be reviewed. A flagged issue does not always prove the underlying business data is wrong.
Use a consistent exception record
For each result that requires action, record the rule or message, severity, affected field or report item, source, likely cause, owner, due date, decision, supporting evidence, and retest result. Classify the cause so the fix addresses the right layer:
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- Source-data issue: the source record is missing, incorrect, stale, or inconsistent with its business meaning.
- Mapping issue: the wrong source field, code, unit, relationship, or aggregation has been used.
- Rule or scope issue: the check has been applied to the wrong release, entity, period, or context, or the instruction has been interpreted incorrectly.
- Legitimate exception: the reported value or relationship is unusual but permitted under the applicable instructions and can be supported with evidence.
Escalate unresolved interpretation questions through the organization’s compliance or reporting governance process. Do not suppress warnings or override controls without a documented reason and approval. Once a change is made, rerun the affected validation and any checks that depend on the changed data.
7. Freeze, approve, submit, and retain evidence
After validation and exception review, create a controlled reporting run that another reviewer can reconstruct. Freeze the data snapshot used to produce the approved output, and identify the framework, taxonomy, and validation-package versions. If a source value or rule changes after approval, use a controlled change process to decide which extractions, transformations, checks, and approvals must be repeated.
Retain a traceable run record
Keep the evidence needed to follow a reported value back to its source and explain why the filed result was accepted:
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- Applicable scope decision and reporting reference date.
- Legal and technical instructions, package versions, and retrieval dates.
- Source extracts or a controlled, reproducible snapshot of them.
- Field mappings and transformation logic versions.
- Validation outputs, including the version of the checks run.
- Exception decisions, supporting evidence, and approvals.
- Final approved report, submitted output, and transmission receipt or other submission evidence.
The EBA’s DORA preparation materials include samples, validation feedback, and testing observations, illustrating the practical value of retaining both the data submitted and the quality feedback associated with it. This is an operational recordkeeping recommendation, not a claim that one retention period applies to every filing; follow the applicable legal and organizational retention requirements.
8. Evaluate software against the reporting workflow
A reporting or data-management platform can support preparation, testing, lineage, and review, but software does not make a report compliant by itself. Evaluate whether the product can implement the applicable rule set and leave an auditable account of how the submission was built.
| Evaluation area | What to verify |
|---|---|
| Jurisdiction and release support | Support for the regulator, entity framework, and exact release required for the reporting cycle. |
| Data model and filing format | Coverage of relevant definitions, taxonomy or serialization format, code lists, and submission-package requirements. |
| Validation | Ability to run the current regulator rules and business checks, expose results, and rerun checks after changes. |
| Version management | Clear control of template, taxonomy, mapping, and validation-package versions used in each run. |
| Lineage and explainability | Ability to trace a report value from the output to its source and show the transformations applied. |
| Exception and approval workflow | Ownership, triage, evidence capture, reviewer decisions, approvals, and an audit trail. |
| Source connectivity and repeatability | Reliable connections to relevant systems and repeatable extraction and transformation processes. |
| Draft testing and export | Ability to test draft reports and produce the required submission package without losing the run evidence. |
These criteria follow from the technical, mapping, and validation needs of a controlled submission. The official materials cited here do not establish a comparison or endorsement of any named vendor.
A practical control checklist for each cycle
- Confirm the entity, scope, report, regulator, reference date, and current framework release.
- Collect and version the applicable instructions, data model, filing specification, value lists, and validation package.
- Map every required concept to a definition, owner, source, transformation, target, and relationship.
- Assign responsibility across source capture, transformation, validation, review, and submission.
- Profile the source data and reconcile transformed values to their inputs.
- Run the applicable technical and business-rule validations; investigate warnings as well as errors.
- Record exception decisions, evidence, approvals, and retests.
- Freeze the approved snapshot and retain the mappings, results, final output, and submission evidence.
Scope and limitations
This workflow is not legal advice or a complete specification for an individual filing. Exact data fields, controls, deadlines, formats, and permitted values depend on the jurisdiction, regulator, legal entity, report, and reference date. Verify the current official instructions and the relevant legal act before implementation. The EBA and European banking examples above illustrate how to work with a specific regulatory ecosystem; they should not be applied automatically to another sector, authority, or country.
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