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Data reconciliation: the great data jigsaw

date: 12 September 2023
reading time: 10 min

Businesses deal with large quantities of data on a daily basis – it can be data related to their customers, processes, or bookkeeping, stored in different systems and locations. To make use of it, it needs to be organised, accurate and consistent. This is what data reconciliation is all about.

What is data reconciliation: understanding the basics

Data reconciliation is a process of comparing and aligning data – after migrating, replacing or transforming it – from different sources or systems to ensure consistency and accuracy. It involves identifying discrepancies or inconsistencies between datasets and taking steps to resolve them.

Rather than being a one-off project, data reconciliation is an ongoing process that may be performed regularly – daily, weekly, or monthly – depending on the specific needs and requirements of the organisation. It is essential for maintaining data quality and integrity, supporting accurate reporting, and enabling reliable decision-making in various industries, including finance, manufacturing, energy, and telecommunications.

How does the reconciliation of data work?

The data reconciliation process typically involves several steps to compare and align data. Here is a general overview of how the reconciliation process works:

  • Data Collection: The first step is to gather data from various source systems. This can involve extracting data from databases, files, spreadsheets, or other sources.
  • Data Validation: Once the data is collected, it undergoes a validation process to ensure its integrity, completeness, and consistency. This step involves checking for missing data, identifying outliers, formatting issues, verifying data types, or any other data quality problems. Data validation helps identify potential errors or discrepancies that may need to be addressed during reconciliation.
  • Comparison: The next step is to compare the data from different sources or stages to identify any discrepancies or differences. This can be done by matching key fields or variables that should be consistent across the datasets. If datasets are large, the process will rely on calculating various aggregations of data and comparing results between them.
  • Error Detection: After the comparison, any discrepancies or differences between the datasets are flagged as errors. This step involves analysing the reasons for the discrepancies, which can include data entry errors, system failures, data transmission issues, or other factors. Understanding the causes of the errors is essential for resolving them effectively.
  • Resolution: Once errors are detected, the reconciliation process moves to resolving the identified discrepancies. This step involves taking corrective actions to align the data and ensure consistency. The resolution methods can vary depending on the nature of the discrepancies and the systems involved. It may include manual adjustments, data transformations, system updates, or communicating with relevant stakeholders to rectify the errors.
  • Verification: After resolving the discrepancies, it is crucial to verify the accuracy and consistency of the reconciled data. This step involves rechecking and revalidating the data to ensure that the errors have been resolved and the datasets are now aligned. Verification may involve running validation checks, re-comparing the data, or performing additional tests to confirm the accuracy of the reconciled data.
  • Documentation: Throughout the reconciliation process, it is essential to document the steps taken, the identified discrepancies, and the resolutions applied. Documentation helps track the reconciliation activities, maintain an audit trail, and provide references for future analysis or compliance purposes.

The reconciliation process may vary depending on the specific context, industry, and complexity of the data.

Organisations may also deploy/use/set up automated reconciliation tools that streamline the process and provide additional features such as data matching algorithms, exception management, and reporting capabilities.

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Why is data reconciliation important: benefits of data reconciliation

Data reconciliation is important for several reasons, and it offers numerous benefits to organisations. Some of its key advantages include:

Data Accuracy

Data reconciliation ensures the accuracy and reliability of data. By comparing and aligning data from multiple sources, organisations can identify and correct discrepancies, errors, or inconsistencies. This leads to improved data quality, reducing the risk of making decisions based on incorrect or incomplete information.

Data Integrity

Reconciliation processes help maintain data integrity by ensuring data is complete, consistent, standardised and valid. It helps identify data outliers, missing values, or formatting issues, allowing organisations to take corrective actions and maintain the integrity of their data assets.


Accurate and consistent data is crucial for making informed business decisions. Data reconciliation provides decision-makers with reliable and trustworthy information, enabling them to analyse trends, identify patterns, and make confident decisions based on a comprehensive view of the data.

Compliance and Audit

Many industries have regulatory requirements that necessitate accurate and consistent data. Data reconciliation plays a vital role in meeting compliance standards, facilitating audits, and providing a clear audit trail. It helps organisations ensure that their data is compliant with industry regulations, internal policies, and external reporting standards.

Operational Efficiency

Automating data reconciliation processes can improve operational efficiency by reducing manual effort and saving time. By leveraging technology and automated tools, organisations can streamline the reconciliation process, minimise human error, and free up resources to focus on other critical tasks.

Data Consistency

Reconciliation processes enable data consistency across different systems or stages of processing. By aligning data from various sources, organisations can eliminate inconsistencies or discrepancies that may arise due to data integration, system upgrades, or data migration. Consistent data promotes better data integration and interoperability between systems.

Cost Savings

Data reconciliation helps identify and rectify data-related issues promptly, minimising the potential costs associated with inaccurate or inconsistent data. By addressing discrepancies early on, organisations can avoid costly mistakes, incorrect billing, customer dissatisfaction, or operational inefficiencies caused by inaccurate data.

Data reconciliation techniques and methods

Data reconciliation involves various techniques and methods to compare and align data from different sources or stages. The choice of technique depends on the nature of the data, the systems involved, and the specific reconciliation requirements. Here are some common data reconciliation techniques:

Master Data Reconciliation

Master data reconciliation refers to the process of comparing and aligning master data across different source systems and databases within an organisation.

Master data represents core business entities such as customers, products, vendors, or employees, and it is typically shared and used by multiple systems and departments. Some of its key aspects include data harmonisation, data consolidation, data mapping, data quality assessment, data governance, system integration.

Accuracy of Activity

The accuracy of activity refers to the reliability and correctness of the reconciliation process and its outcomes. It measures how effectively the reconciliation techniques and methods used align and match data from different sources or stages.

Some factors that contribute to the accuracy of activity in data reconciliation include data consistency and completeness, matching criteria and rules, exception handling and data quality assurance.

Transactional Data Reconciliation

Transactional data reconciliation is the process of comparing and aligning transactional data from different sources or systems to ensure accuracy, consistency, and integrity.

Transactional data typically includes information about individual transactions such as sales orders, purchase orders, invoices, financial transactions, or any other operational activities recorded in business systems.

Automated Data Reconciliation

Automated data reconciliation refers to the use of tools, software, and algorithms to streamline and expedite the process of comparing and aligning data from multiple sources. It involves leveraging automation to perform data matching, exception handling, error detection, and resolution, thereby reducing manual effort and improving efficiency.

Automated data reconciliation offers several advantages, including improved accuracy, increased efficiency, reduced manual effort, and enhanced data integrity.

By leveraging automation, organisations can streamline the reconciliation process, minimise errors, and focus on resolving exceptions and addressing data quality issues efficiently.

Data reconciliation tools

There are several data reconciliation tools available in the market that can help streamline and automate the data reconciliation process.

Some of the most popular include:

  1. SAP Data Services – a comprehensive data integration and data quality tool that includes data reconciliation capabilities. It offers features such as data matching, data cleansing, exception management, and data enrichment. It supports integration with various data sources and provides a graphical interface for designing reconciliation workflows.
  2. Oracle Data Relationship Management – an enterprise data management tool that includes data reconciliation features. It allows organisations to reconcile and align master data across multiple systems and databases. DRM provides data versioning, auditing, and change management capabilities to ensure accurate and consistent data reconciliation.
  3. Talend Data Integration – an open-source data integration tool that offers data reconciliation capabilities. It provides built-in data matching algorithms, data quality checks, and exception handling features. Talend’s drag-and-drop interface enables the creation of reconciliation workflows, and it supports integration with various data sources and systems.
  4. SAS Data Management – a comprehensive data integration and data quality tool that includes data reconciliation functionality. It offers advanced data matching algorithms, exception management workflows, and data quality assessment features. SAS Data Management also provides data lineage and audit trail capabilities for data reconciliation processes.

When selecting a data reconciliation tool, it is important to consider factors such as the specific requirements of your organisation, integration capabilities with existing systems, scalability, ease of use, and the level of support provided by the vendor.

It is recommended to evaluate multiple options and choose a tool that best aligns with your organisation’s needs and goals.

Data reconciliation: best practices and tips

When performing data reconciliation, it is important to follow best practices to ensure accurate and reliable results. Here is a guide on what to watch out for:

  1. Clearly Define Reconciliation Objectives: identify the specific data elements, sources, types, and systems that need to be reconciled. Understanding the goals upfront helps in designing an effective reconciliation strategy.
  2. Establish Data Governance Framework that outlines the policies, standards, and processes for data reconciliation. Assign responsibilities to data stewards or a dedicated team to oversee the reconciliation activities and ensure adherence to data quality standards.
  3. Standardise Data Formats and Definitions across different data sources . Consistent data formats and definitions facilitate easier comparison and matching during the reconciliation process.
  4. Implement Data Quality Controls: conduct regular data profiling, validation, and cleansing to ensure the accuracy and integrity of the data being reconciled. Address data quality issues proactively to minimise discrepancies during reconciliation.
  5. Use Automated Reconciliation Tools to streamline the reconciliation process. They can significantly reduce manual effort, improve efficiency, and minimise errors. Select a tool that aligns with your organisation’s specific needs and integrates well with existing systems.
  6. Perform Data Mapping and Matching: identify key fields and data attributes that need to be aligned during the reconciliation process. Use appropriate matching algorithms and rules to ensure accurate data alignment and identification of discrepancies.
  7. Monitor Data Changes: regularly compare data snapshots or track data change logs to identify any new or modified data that requires reconciliation. Real-time monitoring can help detect and address discrepancies promptly.
  8. Document the Reconciliation Process, including the reconciliation steps, methodologies used, and the rationale behind any decisions made. Documenting the process ensures transparency, allows for future audits, and provides a reference for troubleshooting or resolving issues.
  9. Perform Reconciliation on a Regular Basis to ensure ongoing data accuracy and consistency. Reconciling data at regular intervals helps identify and rectify discrepancies in a timely manner, maintaining data integrity and minimising the impact on business operations.
  10. Continuously Improve the Process using feedback, lessons learned, and evolving business requirements. Monitor the reconciliation outcomes, gather insights, and implement process enhancements to optimise the effectiveness and efficiency of the reconciliation activities.

How should your business implement a data reconciliation process?

Achieving 100% accuracy in data reconciliation may be challenging due to various factors such as data complexity, data quality issues, or limitations of the reconciliation techniques used.

Organisations nevertheless strive to maximise the accuracy by employing appropriate methods, ensuring data quality, and implementing robust validation processes.

When thinking about implementing data reconciliation process, it’s worth taking into consideration a collaboration with an external partner, experienced in delivering those kind of services to clients of various industries. Such a solution brings tangible benefits such as cost savings as well as access to a large pool of talented individuals, trained and experienced in doing exactly this kind of job.

If you are keen to speak in more detail about your plan, do get in touch with our team. With our data consulting and advisory solutions we will help you uncover the hidden potential of your data to use it to your advantage straight away.

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