What is Data Reconciliation? Definition, Process, Tools

⚡ Smart Summary

Data Reconciliation verifies information during migration by comparing target data against source data. This page defines the process, explains why mismatches occur, covers the core terminology, the three reconciliation methods, best practices, and the tools that automate the work.

🔎 Definition: Data reconciliation compares migrated data against its source to confirm the transfer was complete and correct.
⚠️ Risk Addressed: Mapping faults and run-time failures cause missing records, duplicates, and broken relationships.
📐 Core Terminology: Gross error, observability, variance, and redundancy describe how measurements are judged.
🗃️ Master Method: Master data reconciliation counts slowly changing records such as customers and items.
💰 Transactional Method: Transactional reconciliation compares totals, which protects the reliability of BI reports.
⚙️ Automated Method: Automation embeds reconciliation in the data load and keeps stakeholders informed.
🛠️ Tools: OpenRefine, TIBCO, and Winpure clean, validate, and match data across systems.

What is Data Reconciliation?

Data reconciliation (DR) is defined as a process of verification of data during data migration. In this process target data is compared with source data to ensure that the migration architecture is transferring data. Data validation and reconciliation (DVR) means a technology that uses mathematical models to process information.

Data Reconciliation vs Data Validation

The two terms appear together so often that they are frequently treated as one, but they answer different questions and run at different moments.

Data validation asks whether a single value is acceptable in its own right. It checks a record against a rule: is the date real, is the email address well formed, does the country code exist in the reference list? Validation needs only the record in front of it, so it usually runs as data is entered or loaded.

Data reconciliation asks whether two data sets agree. It compares counts, sums, and key relationships between a source system and a target system, and it can only run once both sides exist. A record can pass every validation rule and still be a reconciliation failure, because it was written twice or never arrived at all.

Point of difference	Data Validation	Data Reconciliation
Question answered	Is this value correct in form?	Do source and target agree?
Scope	A single record or field	Two complete data sets
When it runs	At entry or during load	After the load completes
Typical failure caught	Badly formatted or out-of-range value	Missing, duplicated, or unmatched records

Because they catch different faults, a reliable migration uses both rather than choosing between them.

Why is Data Reconciliation important?

In the Data migration process, it is possible for mistakes to be made in the mapping and transformation logic. Issues like run time failures like network dropouts or broken transactions can corrupt data.

These kinds of errors can lead to data being left in an invalid state. These may create a range of issues like:

Missing records
Missing values
Incorrect values
Duplicated records
Badly formatted values
Broken relationships across tables or systems

Here, are important reasons for using the Data Reconciliation process:

The use of Data Reconciliation helps you for extracting accurate and reliable information about the state of industry process from raw measurement data.
It also helps you to produce a single consistent set of data representing the most likely process operation.
Without it, undetected errors lead to inaccurate insight and issues with customer service.
Reconciliation of data is also important for enterprise-control integration.

Apart from the above there are many advantages and benefits of Data reconciliation.

Terminology associated with Data Reconciliation

Gross Error	Gross errors in measurements. It reflects only bias errors, instrument failures, or abnormal noise spikes if you are using only short time averaging period.
Observability	Observability analysis can give you details about what variables can be determined for a given set of constraints and a set of measurements.
Variance	Variance is a measure of the variability of a sensor.
Redundancy	It helps you to determine which measurements should be estimated from other variables by using the constraint equations.

History of Data Reconciliation

Here, are essential landmarks from the history of Data Reconciliation.

DVR ( Data validation and Reconciliation) started in the early 1960s. It was aimed at closing material balances in production where raw measurements were available for all variables.
In the late 1960s, all the unmeasured variables were considered in the data reconciliation process.
Quasi-steady state dynamics for filtering and parallel parameter estimation over time were introduced in 1977 by Stanley and Mah.
Dynamic DVR was developed as a nonlinear optimization model which is issued by Liebman in the year 1992

Modern practice groups that work into three distinct reconciliation methods.

Data Reconciliation Process

Types of Data Reconciliation methods are:

Master Data Reconciliation

Master data reconciliation is a technique of reconciling only the master data between source and target. Master data is mostly unchanging or slowly changing in nature, and no aggregation operation is done on the dataset.

Few common examples of master data reconciliation are:

Total number of rows
Total Customer in source and target
Total number of Items in source and target
Total count of rows based on given condition
Number of active users
Number of inactive users etc.

Transactional Data Reconciliation

Transactional data make the base of BI reports. Therefore, any mismatch in transactional data can directly impact the reliability of the report and the whole BI system in general.

Transactional data reconciliation method is used in terms of the total sum which prevents any mismatch caused by changing the granularity of qualifying dimensions.

Examples of measures used for transactional data reconciliation should be:

Sum of total income calculated from source and target
Sum of all items sold, calculated from source and target, etc.

Automated Data Reconciliation

In large Data warehouse management system, it is convenient to automate the data reconciliation process by making this as an integral part of data loading. It allows you to maintain separate loading metadata tables. Moreover, automated reconciliation will keep all the stakeholders informed about the validity of the reports.

Best practices of Using Data Reconciliation

Data reconciliation process should aim at correcting measurement errors.
Gross errors should be zero to make the data reconciliation process efficient.
The standard approach of Data Reconciliation has relied on simple record counts to keep track of whether the targeted number of records have migrated or not.
Data migration solution delivers similar reconciliation capabilities and data prototyping functionality which offers full volume data reconciliation testing.

Accuracy of Activity

Alongside the counts and totals, the underlying activity itself must hold up to inspection.

You need to make sure that transactions are valid and are correct in purpose.
Need to check if the transactions have been properly authorized.

Data Reconciliation Tools

1) OpenRefine

OpenRefine, which was earlier known as Google Refine, is a useful Database Reconciliation framework. It allows you to clean and transfer messy data.

Download link: https://openrefine.org/

2) TIBCO

This data reconciliation tool offers on-demand software services from the web in the form of Software-as-a-service. It allows users to validate the data, and cleansing data. It provides complete reconciliation testing features. Widely used in ETL process. The capability was originally sold as TIBCO Clarity and now sits within the TIBCO data management portfolio.

Download Link: https://www.tibco.com/solutions/data-management

3) Winpure

Winpure is an affordable and accurate data cleaning software. It allows you to clean a large amount of data, removing duplicates, correcting and standardizing to design the final data set.

Download Link: https://winpure.com/

FAQs

After every load for a migration or nightly ETL job, and on a scheduled cycle for ongoing feeds. Running it immediately after each load keeps the investigation window small, because the suspect batch is still identifiable.

That points to a transformation fault rather than a missing row. Check rounding, currency conversion, data type truncation, and time zone handling, since each changes a value without changing the number of records.

The principle is shared but the context differs. Accounting reconciliation matches ledger entries against statements for financial reporting. Data reconciliation matches records between IT systems and is a data quality control.