Title tag: What is data reconciliation, and how does the process work?

Meta description: Data reconciliation compares destination records with their sources to catch missing or changed rows. Discover data reconciliation best practices.

How to approach data reconciliation for accurate data across systems

A revenue dashboard shows one total while the billing system shows another, right before finance needs the numbers for its quarterly review. Before reporting revenue the finance team needs the data team to identify which figure is correct. Data reconciliation helps resolve the discrepancy by comparing warehouse records with source data to pinpoint missing records, changed values, and mismatched totals.

Without a repeatable reconciliation process, data engineers have to trace every mismatch manually, and then deal with the same errors after the next sync. Here’s a breakdown of data reconciliation’s meaning and how to use it effectively.

What is data reconciliation, and what does it help with? 

Data reconciliation is a process that compares records in a target system, such as a warehouse or data lake, with the source records they came from. 

For an orders table that a pipeline copies from a production PostgreSQL database, reconciliation confirms that every order reached the warehouse with the same amount and status recorded in the source. Analysts and finance teams can trust replicated or migrated data once the order count and the order amounts in the warehouse and database match. 

Automated data reconciliation runs source-to-warehouse comparisons as scheduled SQL jobs after every sync, so engineers spot mismatches without manual reconciliation. Logged results from each run also give auditors a dated record of every discrepancy and its correction.

Data reconciliation vs. data validation, deduplication, and synchronization

Data reconciliation often runs in the same pipeline as other data quality and data movement practices, so it’s easy to mix up their functions. Data reconciliation, data validation, deduplication, and synchronization serve slightly different purposes.

Data reconciliation checks whether data in a destination and source match. This often means comparing keys and totals across both systems. 

The validation step investigates whether data meets predefined rules. For example, this can include testing a single table for null IDs or out-of-range values.

Deduplication uncovers duplicate records and eliminates redundancies. Finally, data synchronization keeps systems aligned to ensure both hold the latest version of records, often pushing changes from one system to another when necessary.

5 essential steps in the data reconciliation process

Data reconciliation follows a repeatable sequence, from defining the comparison to confirming the fix. Data engineers often run each step as an automated job on a fixed schedule.

1. Set the scope and match keys 

Choose tables to reconcile and the keys that link records across both systems, such as order ID or transaction ID. Decide on tolerance thresholds. For example, you may require an exact match for invoice totals or allow a slight difference for currency conversions.

2. Extract both sides for the same window 

Pull source and destination data with the same cutoff timestamp to avoid counting records still in transit as missing. Account for rows deleted in the source, as those rows remain in the warehouse and inflate destination counts.

3. Use summary checks then key-level comparisons

Start with row counts, move to aggregates, such as daily revenue by region, and run key-level comparisons only on the dates or regions where the totals differ. Row counts and aggregate totals scan large tables quickly, so running them before key-level comparisons keeps warehouse compute costs low.

4. Classify and investigate exceptions 

Sort each mismatch by type (missing rows, extra rows, duplicate keys, or changed values) before debugging. Records still in transit during a comparison typically match the source on the next run, so label them as timing differences and keep them separate from defects that need a fix.

5. Correct the data and revalidate 

Fix the root cause of each defect, whether that means re-syncing a table that missed records or correcting a field mapping that loaded values into the wrong column. Rerun the row count and key-level comparisons with the original cutoff timestamp to confirm the warehouse now matches the source. Record the before-and-after results so the correction is clear during the next audit.

Data reconciliation best practices

Reconciliation is harder to manage when companies have many source systems and table owners. With a few best practices, data engineering teams can keep checks accurate and auditable at scale:

• Map sources to a shared schema: Use a data integration tool to map every source to one common model so that a customer ID field in the CRM and a customer number field in the ERP match as the same field. Without that mapping, each pair of systems needs its own custom join logic before any comparison can run.
• Automate source-to-warehouse checks: Write row count and key-match checks as SQL or dbt tests that run after each sync. Automated tests compare the same keys and totals on every run and reduce the errors of spreadsheet comparisons.
• Schedule consistent runs: Run each reconciliation job right after a sync completes or at a fixed time interval. A regular schedule shows engineers when a mismatch first appeared and keeps results comparable week over week.
• Log results: Store the exception counts and corrections from every run in a log table. The log shows auditors when each reported number last matched the source and what changed after a fix.
• Assign owners to each table: Name one team responsible for resolving mismatches in each reconciled table (such as the finance data team for general ledger tables). Clear ownership keeps exceptions from going unresolved in a shared Slack channel.

Data reconciliation use cases

Any company that copies data into a warehouse can benefit from reconciliation. But regulated industries such as finance and healthcare benefit most because they depend on reconciliation for regular compliance and audit work.

Finance data teams match payment processor transactions and ERP general ledger entries against warehouse tables before month-end close. Public companies generally file internal control reporting each year, and reconciliation records give auditors evidence to support their assessments.

Healthcare data engineers reconcile patient visit records and insurance claims between electronic health record systems and the warehouse behind billing reports. Source-to-target checks help companies maintain accurate electronic patient data and support HIPAA compliance.

Common data reconciliation challenges

Companies with high data volumes and many connected systems face the biggest challenges around data reconciliation. These are some of the most common roadblocks:

• Timing mismatches: When change data capture (CDC) lags behind the source during bulk imports, payment processor records arrive after a sync runs. Reconciliation flags the late records as missing.
• Schema drift: An unplanned schema change in the source, such as a renamed or dropped column, shows up as null values in every comparison until an engineer updates the mapping.
• Inconsistent formats: Dates stored as YYYY-MM-DD in one system and MM/DD/YYYY in another fail a direct match. A similar issue occurs when one system records amounts in cents and the other in dollars, or when one system stores email addresses in uppercase and the other in lowercase.
• Unstable keys: When a primary key goes missing or gets reused, reconciliation can no longer match that record to its copy in the other system. Duplicate IDs in a related table, such as two customer records with the same customer ID, add extra rows to joins and push totals higher than the source.

Build reconciliation into your data pipeline

Reporting, analytics, and AI require trustworthy data, which reconciliation helps maintain. Sometimes, challenges emerge upstream. When a pipeline breaks in the middle of a sync, the warehouse receives only part of the source data, and the next reconciliation run flags every record that never arrived.

Fivetran reduces partial loads and sync lag with fully managed data movement across more than 750 connectors and log-based CDC for database sources. Destination tables remain in sync with the latest changes in the source, reducing the gaps that amount to reconciliation mismatches.

When a source adds a column or changes a data type, Fivetran applies the change through automated schema migration, so your checks only point to actual defects. The Fivetran Platform Connector also writes sync logs and schema change events into your destination, giving data engineers an audit trail for each reconciliation run. With fewer partial loads and unmapped columns, data engineers spend less time each week investigating reconciliation exceptions manually.

Start a free trial with Fivetran to experience consistent data and fewer exceptions in your reconciliation reports.

FAQ

How do manual and automated data reconciliation compare?

Manual reconciliation means exporting records from both systems into spreadsheets and comparing them line by line, which suits a one-time check on a small table. Automated reconciliation runs the same comparisons as scheduled SQL jobs after every sync and stores a timestamped result for each run.

What are the differences between data reconciliation, data validation, deduplication, and synchronization?

All four can help maintain data quality as part of a pipeline workflow. Data reconciliation ensures data in a source and destination match, while data validation checks whether data meets predefined rules. Deduplication finds and addresses duplicate records, and synchronization keeps systems aligned so both hold updated records.

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