DataTerminology.comNEW
Process of comparing datasets across systems to detect, explain, and resolve discrepancies and ensure consistency.
DESCRIPTION
Data reconciliation verifies that data in different systems aligns with expectations, often between source and target after integrations or migrations. It involves rules, controls, and exception handling to identify mismatches in counts, balances, or attributes. Root cause analysis addresses systemic issues such as mapping errors or timing. It is common in finance, billing, and regulatory reporting, and relies on lineage and metadata for traceability. Automation and dashboards improve timeliness and accountability.
ALTERNATIVE DEFINITIONS
SAP Help Portal
Data reconciliation is the process of ensuring that data between systems is consistent and matches across sources and targets.
Enterprise application context with focus on source-target alignment.
Informatica Documentation
Reconciliation compares data sets to identify and resolve differences as part of data quality controls.
Positions reconciliation as a data quality control activity.
RELATED TERMS
Data Quality Entity Resolution Master Data Management Lineage Data Integration Schema Mapping Control Exception Management Single Version of the Truth Data Audit
COMPETING TERMS
KNOWLEDGE AREAS
Data Quality Data Management Data Integration & Interoperability Data Engineering
TARGET AUDIENCE
Data Engineer Data Steward Data Analyst Data Architect Data Management Consultant
EXTERNAL SOURCES
- Wikipedia - Data reconciliation
- Microsoft - Migration validation and reconciliation
- DATAVERSITY - Overview of reconciliation concepts
- Basel Committee (contextual risk data) - Risk data aggregation context
TOP QUESTIONS
Why is Data Reconciliation critical after migrations?
It validates that data moved correctly, preventing downstream reporting or billing errors. Automated checks catch discrepancies early.
Category: Relevancy
What are common Data Reconciliation techniques?
Record counts, hash totals, key-by-key comparisons, balance checks, and sampling. Thresholds and tolerances handle expected variation.
Category: Example
How does Data Lineage support Data Reconciliation?
Lineage traces data flow from source to target, helping diagnose where mismatches arise and documenting impacts for auditors.
Category: Requirements
What are typical mistakes in Data Reconciliation?
Relying on manual spot checks, ignoring timing/cutoff differences, and lacking exception workflows can mask systemic issues.
Category: Mistakes
How do controls fit into Data Reconciliation?
Preventive and detective controls define checks, approvals, and escalation paths, ensuring consistent reconciliation and accountability.
Category: Management
What tools support Data Reconciliation?
Data quality platforms, ETL frameworks, SQL diff scripts, and observability tools automate comparisons and alerting.
Category: Platform
How frequently should Data Reconciliation occur?
Frequency depends on criticality: real-time or daily for high-risk processes; periodic for low-risk. Align with SLAs and controls.
Category: Requirements
How do you reconcile slowly changing dimensions?
Account for change types and effective dates, and compare both current and historical states to ensure semantic alignment.
Category: Example
What metrics indicate effective Data Reconciliation?
Exception rates, time-to-resolution, coverage of controls, and recurrence of root causes. Trends guide process improvements.
Category: Testing