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What is Data Reconciliation?

Data reconciliation is a process of comparing data from two or more sources to ensure accuracy and completeness. It can be used in many different scenarios, including accounting, financial reporting, manufacturing processes, energy management systems, and IT infrastructure.

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What Does the Data Reconciliation Process Involve?

Data reconciliation involves identifying discrepancies between data sets and attempting to explain them using external knowledge. This ensures that all datasets are in agreement and that data is accurate and up-to-date.

Why Is Data Reconciliation Important?

The process of data reconciliation can help reduce errors, improve accuracy, increase efficiency and improve decision-making. It can also provide valuable insights into sources of data and be used as a tool for detecting fraud or potential errors.

Data reconciliation is essential for any organization that relies on data to make decisions or run its operations. It helps ensure that the data being used is accurate and reliable and that it is being used in an informed and purposeful way.

How Does Data Reconciliation Work?

Data reconciliation involves comparing two or more data sets and comparing them to a benchmark or accepted value. The process usually begins by looking at the differences between the two sets of data and attempting to explain them. This can involve researching external sources, such as industry standards or guidelines, to provide an explanation for the discrepancies.

Once the differences have been identified, they must be resolved by either adjusting one of the datasets or combining the data sets into a single source. Finally, the results are checked to ensure accuracy and completeness.

Types of Data Reconciliation

Data reconciliation can be broken down into two main categories: system-level and transaction-level. System-level data reconciliation involves comparing two systems to identify data discrepancies, such as differences in values or records. Transaction-level data reconciliation involves comparing transactions between two systems to ensure accuracy and completeness.

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Data Reconciliation Methods

There are several methods used in the process of data reconciliation. These include manual comparison, statistical analysis, trend analysis, and computational models. Manual comparison is the most common and simplest data reconciliation method, but it can be time-consuming and labor-intensive. Statistical analysis uses mathematical algorithms to identify discrepancies and can be used to compare large datasets quickly. Trend analysis looks for patterns in the data that may indicate errors or omissions. Finally, computational models use mathematical formulas to create a set of calculations that can be used to identify discrepancies.

How to Implement Data Reconciliation?

Data reconciliation can be implemented in several ways, including automated systems, manual processes, and a combination of both. Automated systems use software to compare data sets and identify discrepancies, while manual processes rely on humans to review the data sets. The chosen method should depend on the complexity and size of the data sets and the resources available.

Data Reconciliation Use Cases

Data reconciliation can be used in many different industries and scenarios, including accounting, financial reporting, manufacturing processes, energy management systems, IT infrastructure, etc. It can also be used to detect fraud or potential errors and provide insights into data sources.

Data Reconciliation Best Practices

Data reconciliation involves several steps that must be followed to ensure accuracy and completeness. It is essential to establish rules and guidelines before beginning the process and to ensure that all data sets have been properly validated. Additionally, it is crucial to thoroughly review the data for discrepancies and to investigate any unexpected results. Finally, the results should be checked to ensure accuracy and completeness.

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Data Reconciliation Terminology

Data reconciliation involves a number of terms and concepts that must be understood to fully grasp the concept. These include:

  • Benchmark or accepted value: A benchmark value or accepted value is used to compare two or more data sets.
  • Discrepancy: A discrepancy is a difference between two data sets that need to be explained.
  • Data validation: Data validation is the process of ensuring that data is accurate, complete, and up-to-date.
  • Data reconciliation: Data reconciliation is the process of identifying discrepancies between data sets and attempting to explain them using external knowledge.
  • System-level data reconciliation: System-level data reconciliation involves comparing two systems to identify discrepancies in the data.
  • Transaction-level data reconciliation: Transaction-level data reconciliation involves comparing transactions between two systems to ensure accuracy and completeness.

Data Reconciliation Tools

Data reconciliation can be done manually or with the aid of software. Manual data reconciliation requires a significant amount of time and effort, while automated solutions can speed up the process. Automated tools can also provide additional features such as data validation, audit trails, and automated reconciliation of multiple sources.

Top Data Reconciliation Tools

Some of the best and most popular data reconciliation software and tools include:

  • Google Sheets
  • DataMatch Enterprise
  • Altair Monarch
  • Infor Reconciliation Manager
  • Oracle Hyperion Financial Data Quality Management Enterprise Edition
  • SAP Financial Consolidation
  • StatPro Revolution Reconciliation
  • Trintech Cadency Reconciliation
  • Workiva Reconciliations

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Conclusion

Data reconciliation plays a vital role in ensuring the accuracy and completeness of data. It can be done manually or with the help of automated tools, depending on the complexity and size of the data sets. Data reconciliation involves understanding terms, validating data sets, comparing data sets for discrepancies, and implementing best practices.

Understanding the process and having access to the right tools can help you effectively use data reconciliation for your organization.

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Hady ElHady
Hady is Content Lead at Layer.

Hady has a passion for tech, marketing, and spreadsheets. Besides his Computer Science degree, he has vast experience in developing, launching, and scaling content marketing processes at SaaS startups.

Originally published Jan 12 2023, Updated Jan 13 2023