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Data reconciliation compares data from different sources and works to reduce differences. Data adjudication is the decision step for a disputed value, record, or match: it determines what outcome to accept, under what rule, and who is accountable for that choice. Adjudication can resolve an exception found during reconciliation, but the terms are not interchangeable.
What is data reconciliation?
The DAMA Dictionary of Data Management defines reconciliation as “the process of adjusting data derived from two different sources to remove, or at least reduce, the impact of differences identified.” In practice, reconciliation can involve comparing records, matching corresponding items, investigating variances, and aligning data where appropriate. Its output may be an adjusted dataset, a resolved variance, or a documented difference that remains unresolved. DAMA Dictionary of Data Management, 2nd Edition
What is data adjudication?
There is no established universal formal data-management definition of “data adjudication.” A useful working description is the reasoned decision on a contested or ambiguous case: for example, choosing between conflicting values, deciding whether two records represent the same entity, or assigning an exception disposition. The decision may form one stage within a broader reconciliation, data-quality, or entity-resolution process; organizations should define the term and its decision authority in their own governance materials.
Unlike reconciliation, which addresses differences across sources or records, adjudication focuses on what to do about a particular case when comparison or automatic rules do not settle it. A decision could select a value, accept or reject a match, refer the case for review, or preserve a known difference with an explanation.
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Data adjudication vs. data reconciliation
| Aspect | Data reconciliation | Data adjudication |
|---|---|---|
| Main question | Where do sources or records differ, and how can those differences be reduced? | Given conflicting evidence or an ambiguous case, what outcome should be accepted, and who is accountable? |
| Typical input | Two or more datasets, ledgers, feeds, or representations to compare. | A discrepancy, uncertain match, conflicting value, or exception requiring judgment under rules. |
| Typical output | An adjusted or aligned dataset, a resolved variance, or a documented remaining difference. | A selected value, match/no-match decision, exception disposition, or reasoned escalation. |
| Relationship | A broader comparison-and-adjustment workflow. | A decision that can occur within reconciliation or data-quality operations. |
The reconciliation definition follows the DAMA Dictionary. The adjudication column is a practical operating description, not a universal standard definition. DAMA Dictionary of Data Management, 2nd Edition
How to adjudicate a data discrepancy
- Describe the discrepancy. Record the values or records that disagree, the systems they came from, and the relevant dates. Preserve their source context rather than immediately overwriting one value. Provenance can document how data was derived and passed through owners or custodians. ISO/DIS 8000-2, Data quality — Part 2: Vocabulary
- Check the applicable rules and authority. Identify relevant definitions, validation rules, source-of-record policies, and the accountable owner. UK guidance assigns data-quality accountability to information asset and/or data owners; Canadian guidance recommends using authoritative sources where possible and documenting standards and differences in practice. The Government Data Quality Framework Data Quality, DDTS-154 v1.00 Government of Canada: Guidance on Data Quality
- Assess evidence and impact. Consider whether the data is complete, valid, consistent, unique, timely, and fit for the intended use. The relevant qualities depend on what the data will support; there is no single quality score that settles every case. The Government Data Quality Framework NATO Data Quality Framework for the Alliance
- Decide or escalate. Apply deterministic rules when they are appropriate and authorized. Send unresolved or high-impact cases to a designated steward, owner, or subject-matter expert. For entity matching, make the error trade-off explicit: a false positive links different entities, while a false negative leaves records for the same entity unlinked. DAMA-DMBOK 2nd Edition
- Record the outcome. Capture the selected value or match, the rationale and evidence, who made the decision, when it was made, and any uncertainty that remains. If sources cannot be made equivalent, document the difference instead of concealing it. Government of Canada: Guidance on Data Quality
- Correct and prevent recurrence. Apply only authorized corrections, monitor data quality, and investigate upstream causes. ISO vocabulary describes cleansing as detecting and repairing defects; UK guidance considers quality risks across acquisition, preparation, integration, and maintenance. ISO/DIS 8000-2, Data quality — Part 2: Vocabulary The Government Data Quality Framework
What makes an adjudication defensible?
A defensible decision connects the case to an applicable rule, accountable ownership, and retained evidence. The UK Data Quality standard puts the purpose test plainly: “Data quality is ensuring data is fit for its purpose and good enough to support the outcomes it is being used for.” The quotation is from Data Quality, DDTS-154 v1.00, published 31 August 2024 and updated 20 January 2025.
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- Rule: State which policy, definition, or validation criterion governs the case.
- Owner: Name the person or role authorized to decide or escalate it.
- Evidence: Keep the source values and context needed to review the rationale.
- Risk: Consider the intended use and consequences of choosing incorrectly.
- Outcome: Preserve the decision and any remaining uncertainty so downstream users can interpret it.
When should a case be escalated?
Escalation is appropriate when authoritative sources conflict, the governing rule is unclear, the evidence is incomplete, or an incorrect choice could have substantial consequences. In entity resolution, the balance between false positives and false negatives depends on the application: linking unrelated people or organizations may be more harmful in one setting, while leaving duplicate records unlinked may matter more in another. Set review thresholds around those consequences, not around a supposedly universal match score.
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