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For record linkage—deciding whether two records describe the same person or entity—use rules for clear, repeatable cases with reliable matching data, and refer uncertain cases to a person when that person can assess relevant evidence or context. A hybrid workflow often fits best: automate straightforward matches, send ambiguous cases for review, and monitor outcomes. The right balance depends on the cost of false matches and missed matches, the quality of the evidence, and the capacity to review cases.
First, define what “data reconciliation” means
This comparison is about record linkage or entity resolution: deciding whether records refer to the same person or other entity. Financial reconciliation—such as balancing transactions, payments, or account totals—has different domain-specific requirements, and the guidance here does not establish how to handle those cases.
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In record linkage, a rules-based method applies conditions defined in advance. A probabilistic method scores matching evidence, and machine-learning methods may classify record pairs. Human adjudication means a reviewer examines a referred pair or discrepancy and decides its match status. These approaches are not interchangeable labels: rules specify conditions, while adjudication is a human decision step that can sit alongside automated methods.
When rules are the better starting point
Records have reliable identifiers and the cases repeat
Rules are a strong starting point when identifiers are trustworthy, definitions are stable, and the same kinds of cases recur at scale. Explicit criteria make decisions consistent and testable against the organization’s definition of a valid link. AWS describes configurable hierarchical matching workflows and distinguishes exact matching from advanced workflows that support exact and fuzzy matching: AWS rule-based matching workflow documentation.
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Differences are formatting issues with approved fixes
Standardization can make equivalent values comparable—for example, applying an approved format transformation before matching. The U.S. Census Bureau’s linkage standard calls for standardizing variables used in linkage. Keep the original values or otherwise preserve traceability, and encode only transformations the organization has approved: U.S. Census Bureau Standard C4.
Rule definitions can be specified and checked
Before relying on a rule, document what counts as a valid link, which fields it uses, any blocking choices, how inputs are standardized, and how exceptions are handled. UK Government guidance notes that linkage choices involve trade-offs among accuracy, analytical validity, human and computing resources, and matching-data quality: UK Government guidance on data-linking methods.
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When a person should review a case
Evidence is incomplete, conflicting, or unusual
Refer a pair when the available fields do not support a confident rule-based decision, identifiers conflict, or a case falls outside the rules’ intended scope. A reviewer may weigh context or evidence that the automated process did not use. Clerical review can help estimate match status, but it takes resources and remains limited by the evidence available to the reviewer. UK Government guidance discusses these trade-offs, while ONC patient-matching guidance addresses the role and limits of human review: ONC patient identification and matching report.
A wrong link could have serious consequences
Where a false match or missed match could materially affect people, services, or downstream analysis, use stronger validation and consider review for cases that rules cannot safely resolve. The appropriate referral threshold depends on the use and error consequences; the cited guidance does not prescribe one universal cutoff. Define criteria and verification appropriate to the application, and protect restricted information throughout the process, as required for systems within the scope of Census Standard C4.
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Review helps only if the reviewer has useful evidence
Human judgment is not a substitute for missing data. A reviewer can also make an error, and cannot improve on an automated result without adequate or supplementary matching evidence. If the information needed to decide is absent, the appropriate outcome may be unresolved or further investigation—not a forced match.
Why a hybrid workflow often works
When a large share of cases is clear and a smaller share is uncertain, a hybrid process can let rules handle the clear cases while reserving reviewer capacity for referrals. It does not make either method infallible: set referral criteria, document reviewer decisions, and assess the resulting error profile.
- Define the purpose. State why records are being linked and what counts as a valid link for that use.
- Specify the matching process. Record the fields, standardization, blocking choices, rule parameters or cutoffs, and conditions that trigger referral.
- Route uncertain cases deliberately. Set out what happens when evidence conflicts, falls below a cutoff, or does not fit an established rule, including an escalation path where needed.
- Keep an auditable decision record. For referred cases, retain the evidence shown to the reviewer, the decision, and its rationale, subject to appropriate confidentiality safeguards.
- Verify and monitor. Check that implementation follows the specification and that all components work as intended. Define quality checks against user needs and business objectives, record results over time, and investigate failed checks.
Census Standard C4 requires a linkage plan, confidentiality safeguards, and verification and testing for systems within its scope: U.S. Census Bureau Standard C4. UK Government data-quality guidance emphasizes that quality is use-dependent: data adequate for one purpose may be inadequate for another: Government Data Quality Framework.
How to choose the balance
| Situation | Starting approach | Control to apply |
|---|---|---|
| Reliable identifiers, stable definitions, and repeated cases | Rules | Define valid-link criteria and test that the implementation applies them consistently. |
| Safe, approved formatting transformations can resolve differences | Rules after standardization | Document transformations and preserve traceability to source values. |
| Incomplete or conflicting evidence, unusual exceptions, or uncertain cases | Human referral, potentially after automated triage | Specify the referral and escalation process; record evidence and rationale. |
| High consequences from false or missed links | Human review alongside stronger validation and audit | Set criteria appropriate to the use; there is no universal risk threshold in the cited guidance. |
| High volume with many obvious cases and a smaller uncertain tail | Hybrid | Automate clear cases, focus review capacity on referrals, and monitor errors rather than assuming either method is infallible. |
There is no single cutoff at which automation should stop and review should begin. Set thresholds according to the matching evidence, the purpose of the linkage, the cost of each type of error, and the amount of review capacity available. Also account for privacy, auditability, consistency, and downstream consequences.
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One implementation detail to check before choosing rules
AWS documents an operational constraint for its Entity Resolution service: “You can’t change the rule type after creating a workflow.” If you use that service, decide which documented workflow type fits your needs before creating the workflow, and check the current product documentation for its features and constraints: AWS rule-based matching workflow documentation.
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