What is data reconciliation? Use cases, techniques, and challenges

Data reconciliation is probably one of the most technically involved processes of data engineering. It’s crucial for ensuring data accuracy, consistency, and integrity across various systems. It’s particularly important in industries such as finance, healthcare, and telecommunications, where data discrepancies can lead to significant issues.

It’s also something we really like talking about (check out Datafold founder’s podcast interview on the challenges of validating and reconciling data at scale).

This article is the first of a three-part series on data reconciliation, where we will define what success looks like, use cases in data replication and data migration, how it works, and the four main technical challenges.

In part two, we’ll look at the five biggest challenges faced during data reconciliation, including speed, efficiency, detail, data types, and collations. And part three will cover best practices for effectively managing and executing data reconciliation processes, including selecting validation metrics, efficient resource management, and automating data quality testing. 

What is data reconciliation?

There are many definitions of data reconciliation out there, but here’s one we really like because it captures both the process, purpose, and users in data engineering:

Data reconciliation is the process of comparing from multiple sources to ensure accuracy, consistency, and completeness for end users.

This is crucial for maintaining data integrity and reliability, and yet because it is technically difficult to implement, many teams often struggle and fallback to carrying out manual checks between systems or waste time creating custom solutions to validate across databases.

Data reconciliation has been a success when all data transferred replicates the original in function, meaning, and detail without loss, corruption, or unauthorized alteration.

Why data reconciliation is so important

When working with data from multiple sources, inconsistencies and errors will arise. It’s not a question of if but when: data discrepancies can stem from different data formats, entry errors, or timing issues. 

As data engineers, our role is to proactively identify and resolve these issues through data reconciliation. This ensures that our data remains accurate, consistent, and reliable for all downstream processes, enabling confident decision-making and robust analytics.

Data reconciliation also helps in maintaining compliance with regulations that require accurate and consistent data. This is particularly important in industries like:

Financial services

In the financial sector, regulations like the Sarbanes-Oxley Act (SOX) in the U.S. require accurate and reliable financial reporting. Banks and financial institutions must reconcile their transaction data to ensure that their financial statements are accurate. Any discrepancies can lead to significant legal and financial penalties. Many accounting teams are key stakeholders in data quality and reconciliation efforts. 

Healthcare

In healthcare, regulations such as the Health Insurance Portability and Accountability Act (HIPAA) mandate the accuracy and privacy of patient data. Hospitals and healthcare providers need to reconcile patient records, billing information, and medical histories to prevent errors that could affect patient care and to comply with legal requirements.

Data reconciliation use cases

Data reconciliation is a critical stage in data replication and migration, where the success of either project ultimately hinges on the accuracy and consistency of the data in different systems. 

And there are many reasons why the initial data replication or migration may have resulted in data inaccuracies:

• Data type mismatches: Different databases may handle data types like dates, timestamps, or decimal numbers differently.
• Network issues: Latency or interruptions can result in incomplete data replication or migration.
• Concurrency issues: Changes to the source database during replication or migration can lead to inconsistencies.
• Configuration errors: Incorrect settings or scripts in the replication or migration setup can cause data loss or duplication.

Here's how it fits into each of these use cases.

Data reconciliation in replication testing

Replication involves creating copies of data from one database to another on an ongoing basis, ensuring that the replicated data is identical to the source. Here, data reconciliation is essential for:

1. Validation: After data replication, reconciliation is used to compare the source and target databases to ensure that all records have been accurately copied. This involves checking for discrepancies in data values, missing records, and ensuring data integrity.

2. Consistency checks: Continuous reconciliation helps in maintaining consistency between the source and replicated databases over time. This is crucial for applications that rely on real-time data synchronization.

3. Error detection and resolution: Any differences identified during reconciliation can be addressed promptly, preventing issues from propagating through the system. This ensures the reliability of the replicated data.

Data reconciliation in migration testing

Data migration involves moving data from one system or format to another, often for the purposes of modernization. Reconciliation is critical in this process to ensure that the migrated data is accurate and complete. Here, data reconciliation is essential for:

1. Post-migration validation: After the migration, data reconciliation is used to compare the source and target systems. This ensures that all data has been accurately migrated without loss or corruption.
2. Data integrity verification: Reconciliation ensures that the relationships and constraints in the data are preserved during migration. This is particularly important for maintaining the integrity of complex datasets.
3. Compliance and reporting: In regulated industries, data reconciliation during migration helps ensure compliance with legal and regulatory requirements. Accurate reporting of the migration process and its outcomes is crucial for audits and verification.

How does data reconciliation work?

It’s a lengthy process to ensure that data from different sources is accurate, consistent, and complete. While the process looks slightly different in each use case and specific business context, it generally has the following key steps:

1. Data extraction: Involves identifying and extracting data from various sources, such as databases,. This step ensures that all relevant fields and records are captured for further processing, often using  custom data engineering work or ETL (Extract, Transform, Load) tools.

2. Data matching: Uses key fields, like primary or composite keys, to identify corresponding records between different data sets. This can involve exact matching or fuzzy matching algorithms to handle minor discrepancies and ensure records are correctly aligned.

3. Data validation: Involves comparing the values of matched records to identify discrepancies. This step checks for missing records, mismatched values, and any additional or erroneous entries, ensuring data consistency and accuracy.

4. Resolution: Focuses on investigating and correcting any identified discrepancies. This can include updating records, inserting missing data, or deleting erroneous entries. After correction, the data is re-validated to confirm that all issues have been resolved and no new discrepancies have been introduced.

Data reconciliation challenges

There are four main technical challenges involved in data reconciliation, which many tools and techniques are still not fully able to handle. 

Data volume adds complexity

Handling large volumes of data from multiple sources can be overwhelming. The complexity increases with the diversity of data formats and structures, making it challenging to accurately reconcile data within reasonable timeframes. 

Many current tools struggle with the sheer volume of data in modern enterprises. As data grows, traditional reconciliation tools may become slow or inefficient, leading to long processing times and potential bottlenecks. Some tools are not optimized for resource efficiency, making them impractical for large-scale data reconciliation without significant infrastructure investments.

Data quality issues

Poor data quality, such as missing values, duplicates, and inconsistencies, complicates the reconciliation process. Ensuring clean and accurate data is a prerequisite, but it often requires significant effort to address underlying quality issues.

Existing tools may not cover all aspects of data quality, such as data lineage, provenance, and context. This incomplete coverage means that some quality issues remain undetected or unaddressed, affecting the overall reliability of the data.

Also, integrating data quality tools with various data sources and systems can be challenging. Compatibility issues and lack of seamless integration capabilities can hinder the effectiveness of data quality management efforts.

Inconsistent data formats

Different systems may store data in various formats, which can lead to compatibility issues during reconciliation. Normalizing these formats to a common standard is essential but can be complex and time-consuming, and tools don’t always get it right. 

Many tools are designed to handle specific data formats or structures, making them less adaptable to the wide variety of formats encountered in real-world scenarios. For example, date formats can vary significantly (MM/DD/YYYY, DD-MM-YYYY, etc.), and while tools might handle some variations, they might miss others, leading to incorrect data interpretation.

Some data transformations require complex logic that current tools struggle to implement effectively. For example, converting between different units of measurement or handling nested data structures might not be straightforward and can lead to incorrect data if not managed properly.

Despite automation, many tools require significant manual intervention to handle inconsistent formats. This manual effort can be time-consuming and prone to errors, especially when dealing with large datasets.

Matching difficulties

Accurately matching records across different data sets is challenging, especially when unique identifiers are absent or inconsistent. Fuzzy matching techniques can help, but they may not always be precise, leading to potential errors.

Many data teams write custom validation scripts, but these are often insufficient for a few reasons. Custom scripts are typically tailored for specific use cases or data sets. As data sources and requirements change, these scripts can become outdated and inflexible, requiring constant updates and modifications to accommodate new data formats or validation rules. 

Custom scripts might perform well with small to moderate data sets but often struggle with large-scale data. They can become slow and inefficient when handling large volumes of data, leading to performance bottlenecks and delays in the reconciliation process.

Maintaining custom scripts requires ongoing effort and expertise. As data structures evolve and new validation requirements emerge, keeping scripts up-to-date becomes a resource-intensive task, diverting attention from other critical data engineering tasks.

Custom scripts are often developed by different individuals or teams, leading to inconsistent approaches and standards. This can result in varying levels of quality and reliability, making it difficult to ensure a consistent and accurate reconciliation process across the organization.

Solving data reconciliation challenges

While addressing the primary technical challenges of data reconciliation is essential, there are deeper and more complex issues that require careful consideration. ​​These include managing data governance and ensuring seamless integration across legacy systems.

In our next article, we'll explore these data reconciliation challenges in greater detail. We'll also discuss why using data diffs—a way to compare two datasets at the value-level to identify differences—is the only complete testing solution during data reconciliation, whether for data replication or data migration. 

Data diffs provide a precise and comprehensive way to detect inconsistencies, validate data integrity, and ensure that all changes are accounted for. This approach not only facilitates accurate data reconciliation but also enhances the overall quality and reliability of the data.

There are also a few ways to get started with better data reconciliation testing practices today: 

• Request a personalized demo with our team of data engineering experts. Tell us about your data replication or migration pipelines, tech stack, scale, and concerns. We’re here to help you understand if data diffing is a solution to your data reconciliation efforts.
• For those who are ready to start playing with cross-database diffing today, we have a free trial experience of Datafold Cloud, so you can start connecting your databases as soon as today.