What is data reconciliation?

Organizations’ data ecosystems are becoming increasingly complex: They’re integrating a growing array of enterprise systems, operational platforms and customer engagement channels while adopting hybrid cloud infrastructures and managing real-time data flows. With this complexity comes a higher likelihood of data discrepancies, missing data and mismatches in datasets. These issues can undermine the accuracy and reliability of enterprise-wide insights.

Data reconciliation focuses on identifying and resolving these discrepancies. It typically occurs after data has been collected or transferred, and either complements or follows extract, transform, load (ETL) workflows, where data is moved and transformed between systems.

The data reconciliation process can be time-consuming when performed manually, and can be further complicated by tight resources, fragmented data ownership, legacy systems and the need to maintain regulatory compliance. However, there are several software solutions and data reconciliation tools to help automate and streamline the process, improving efficiency, speed and error detection.

Modern data environments produce and collect extremely large volumes of data. Global data creation alone is projected to grow from 149 zettabytes in 2024 to more than 394 zettabytes by 2028, a 164.4% increase.¹

This data exists across a wide range of systems—such as customer relationship management (CRM) platforms, financial databases, healthcare systems and cloud applications—each with its own structure and update frequency.

To extract meaningful value from this explosive growth in data, organizations must break down silos and harness information from across the enterprise. When unified and analyzed effectively, the data can reveal patterns, predict trends and drive smarter decisions. These insights enable organizations to optimize marketing campaigns, improve patient outcomes, streamline logistics and more.

However, when organizations combine data from all these different sources without an effective data reconciliation process, they can experience a host of issues. For example, in healthcare, mismatched patient records across electronic health systems can lead to duplicated tests and incorrect diagnoses, which in turn contribute to broader data inaccuracies. And in finance, inconsistent data can result in reporting and audit errors, compliance risks and flawed financial forecasting.

Enter data reconciliation. This data management practice emerged to prevent data integrity issues before they impact decision-making, operational efficiency or stakeholder trust. Data reconciliation supports accurate forecasting, reliable performance tracking, reporting and more. It strengthens data governance by creating a clear lineage of how data is sourced, transformed and validated.

Additionally, more organizations are realizing the power of artificial intelligence (AI): 61% of CEOs say their organization is actively adopting AI agents and preparing to implement them at scale, according to the IBM Institute for Business Value 2025 CEO Study. Data reconciliation is essential for maximizing the return on AI and analytics investments by ensuring that models are trained and tested on high-quality, consistent data.

Data reconciliation is a structured process that helps ensure consistency and accuracy across datasets. Here’s a step-by-step breakdown of how the process typically unfolds:

1. Data extraction
2. Data standardization
   
3. Data comparison
4. Discrepancy identification
5. Resolution and correction
6. Validation and audit logging

Relevant datasets are pulled from a variety of internal and external data sources, such as structured repositories and cloud-based services—which may themselves host structured or unstructured data. This step makes all necessary information available for matching and comparison.

Extracted data is cleaned and formatted into a consistent structure. This step may involve converting date formats, normalizing field names or removing duplicates to prepare for accurate comparison and maintain data consistency.

At this stage, standardized datasets are reviewed to identify inconsistencies. While automated tools and algorithms are commonly used to compare values across systems, some scenarios may require manual inspection, such as when dealing with complex business rules or anomalies that require contextual judgment.

Inconsistencies are flagged and categorized based on severity or type. This step helps prioritize which issues need immediate attention and which can be resolved later, supporting overall data integrity.

Discrepancies are resolved either automatically (based on predefined rules and algorithms) or with manual effort by data stewards. Corrections may involve updating records, merging duplicates or escalating issues for further review to ensure data accuracy.

Once reconciled, the data is validated to confirm data accuracy and data consistency. The entire process is logged to create an audit trail, supporting compliance and transparency.

Data reconciliation can take various forms depending on the complexity of the systems involved and the nature of the data. Below are the most common types of data reconciliation used across industries:

• Manual reconciliation
• Automated reconciliation
  
• Transaction-level reconciliation
• Balance-level reconciliation
• System-to-system reconciliation

Manual reconciliation involves human review and comparison of datasets, often using spreadsheets or reports. While it’s flexible and easy to implement, this method is time-consuming and prone to human error, especially with large volumes of data.

Using reconciliation tools or scripts, this method automatically compares data across systems, flags discrepancies and can even apply data validation rules. Automated reconciliation improves efficiency, scalability and data quality, making it ideal for organizations with high data volumes.

This method matches individual transactions across systems, such as comparing bank statements with internal ledgers. It ensures data integrity at a granular level and is commonly used in finance and accounting.

Instead of matching individual transactions, balance-level reconciliation compares summary balances. For example, it may involve reviewing the total daily sales recorded in different systems to ensure they align. This method is faster than transaction-level reconciliation but may miss detailed errors unless combined with deeper checks.

Used when integrating data from multiple platforms such as CRM to enterprise resource planning (ERP), this method ensures that data is consistent across systems and supports data reconciliation efforts during migrations or integrations.

Data reconciliation, data validation and data synchronization are distinct yet complementary processes within data management, each serving a specific purpose in maintaining data quality and consistency.

Data entry often serves as the starting point for these processes, as the accuracy and completeness of entered information directly impact downstream tasks. Once data is entered into systems, data reconciliation becomes the process of comparing datasets from different sources or systems to identify and resolve discrepancies. It’s typically used after data has been migrated, transformed, or integrated, and focuses on ensuring that records match across platforms.

This process is critical, for example, when working with large datasets involving financial transactions, regulatory reporting or operational metrics. Reconciliation helps confirm that data remains accurate and complete, often by checking key identifiers and values between systems.

Data validation, on the other hand, is about verifying that data meets predefined rules or standards before it’s used or stored. Validation checks might include ensuring that fields are not empty, values fall within expected ranges or formats are correct, such as dates and email addresses. While reconciliation compares data across systems, validation ensures that individual data points are correct and usable.

Data synchronization differs from both by focusing on keeping data consistent across systems in real time or at scheduled intervals. It ensures that updates made in one system are automatically reflected in others, maintaining uniformity across platforms.

Synchronization is especially useful in distributed environments where multiple applications or devices rely on shared data. Unlike reconciliation, which is corrective, and validation, which is rule-based, synchronization is a continuous process aimed at preventing inconsistencies from arising in the first place.

Organizations depend on reconciliation practices to align large datasets across various sources, optimize workflows, ensure data integrity and support a wide range of data management needs. Here are several examples of how data reconciliation is applied across industries and operational scenarios:

Cross-system patient data alignment: Healthcare providers often manage patient data across multiple systems, including electronic health records (EHRs), billing platforms and insurance databases. To maintain consistency, they must reconcile data between these systems regularly.

Migration and application integration: During data migration or new application integration, reconciliation ensures that large datasets containing clinical, financial and administrative information remain accurate and aligned.

Regulatory compliance: The Health Insurance Portability and Accountability Act (HIPAA) requires organizations to maintain documentation of compliance efforts. Data reconciliation processes create audit trails that demonstrate how data discrepancies are resolved, supporting transparency and accountability during compliance reviews.

Legacy-to-modern system integration: Banks and investment firms reconcile data between legacy platforms and modern analytics tools to preserve the integrity of client portfolios, transaction histories and compliance documentation.

Regulatory reporting accuracy: Reconciliation helps ensure that financial reporting submitted to regulators such as the US Securities and Exchange Commission (SEC) and Financial Industry Regulatory Authority (FINRA) meets regulatory requirements and is free of discrepancies, reducing the risk of fines or reputational damage due to inaccurate reporting.

Automated trade matching: Asset managers use machine learning to reconcile trade confirmations and settlement financial data between different financial institutions, minimizing manual intervention and reducing human error.

Fraud detection and risk management: Reconciliation of internal transaction logs with external payment networks such as the Society for Worldwide Interbank Financial Telecommunication (SWIFT) and the Automated Clearing House (ACH) helps detect anomalies and unauthorized transactions.

Complex data pipelines across partners: Organizations involved in supply chain operations build intricate data pipelines to track shipments, inventory levels and supplier transactions across multiple systems. Data reconciliation is essential for maintaining accuracy and consistency across interconnected systems, helping prevent delays, miscounts and mismatched records.

Source-to-target validation for inventory and orders: Reconciliation tools compare key identifiers such as product codes, order numbers and delivery dates between source and target systems to ensure consistency in inventory records and order fulfillment.

Operational accuracy and analytics readiness: These tools help maintain accurate data for demand forecasting, supplier performance analysis and real-time logistics tracking—ensuring that downstream analytics and reporting reflect true operational conditions.

Several factors can influence the effectiveness and efficiency of data reconciliation. These strategic approaches can help optimize reconciliation efforts:

• Separation of supporting data from accounts
• Timing and frequency of reconciliation
  
• Use of queries to segment data
• Selective attribute retrieval

Supporting data, such as group configuration details, often contains information about who has access to what. Reconciling this data separately from account details can be especially helpful during setup or when updating system metadata. By reconciling supporting data first, organizations can avoid misconfigurations and access issues that might otherwise disrupt operations or compromise security.

The timing and frequency of reconciliation often depend on how frequently the underlying data changes. In some cases, running reconciliation too often may create unnecessary overhead and inefficiencies, while doing it too infrequently could result in missed updates. Finding a cadence that balances performance and accuracy can help minimize redundant processing and avoid potential bottlenecks.

Reconciliation can be resource intensive. Using queries to isolate and reconcile only the records that have changed, for example, can significantly reduce the load. This approach is especially useful when dealing with large datasets, where segmenting the data into manageable chunks and scheduling them separately can improve scalability and responsiveness.

Not all fields or attributes within each record may be necessary for reconciliation, either. Limiting the scope to a subset of relevant attributes can enhance performance and reduce processing time.