Data Recon vs. Data Match: Main differences and advantages

Data Recon vs. Data Match: Main differences and advantages

Why should you choose a data match tool that allows you to work with your data in a broader way? How to leverage these benefits with Data Recon? Read on because here we tell you all about it.

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Although there is no specific date that can be attributed to the birth of Data Match and Data Recon, we can mark on the calendar the 1970s and 1980s as the time when the relational database model became present and, therefore, when the need to manage them began.

In recent years, with the exponential growth of data and the advance of technologies such as machine learning and artificial intelligence, Data Match and Data Recon have become more relevant as they are able to handle large volumes of data, make accurate comparisons and automate data reconciliation.

In other words, they have evolved over time and with the purpose of addressing the growing challenges of data management and analysis in organizations.

But what specifically is each and what advantages does it offer to companies that have to manage data today. Let’s take a look below.

What is Data Match and what advantages does it bring to organizations?

Data Matching, which involves comparing data to find matches and duplicates, has been used in various industries for many years.

As organizations accumulated large volumes of data across multiple sources, the need arose to identify and resolve duplicates to maintain data integrity.

Over time, more sophisticated algorithms and techniques have been developed to perform data matching more efficiently and accurately.

In the Conciliac EDM platform, the Data Match module offers companies the ability to reconcile millions of records in seconds. Through a flexible and easy-to-use user interface, unlimited automated matching scenarios can be created.

This module allows you to relate different data sets, looking for matching and reconciling information. On the platform, it teaches the tool the criteria and rules necessary to cross the data sources and carry out the corresponding reconciliations. These rules are set out only one time for each type of execution and stored for future reconciliations.

Once the data sources are selected, such as FTPs, SharePoint, databases, ERP, APIs or Data Labs, the extraction logic is specified and the transformed files are loaded.

In addition, the platform has the ability to remove duplicates directly from the transformed files. Search in other accounts if there are duplicate records and, in case claims, removes the current file you want to conciliate.

Once the reconciliation or match operation is completed, the results can be exported in various formats, such as XLS, CSV, TXT, XLM, or returned to a database or ERP. It is also possible to generate a consolidated report based on this information.

In addition, this module includes a sub-module called “Matching Processes” that facilitates account management. In this sub-module, templates can be created for different banks, inventories, credit card reconciliations, general reconciliations or any other data matching process. Interactions are logged and statuses are labeled, such as approved, pending or running.

The best part is that this is only a part of what companies can do by matching their records, once they start this management they can discover the full potential of automating reconciliations.

Six advantages of Data Match

Although the various advantages can be visualized when defining it, here we will mention some more:

  1. It identifies and eliminates duplicate records in data sets, improving data quality and ensuring data integrity.
  2. By comparing and matching data from different sources, Data Match can identify errors, inconsistencies and discrepancies in the data, giving you the option to improve data, increase data quality and reliability. Additionally, this increases the accuracy of the analysis and the decisions based on it.
  3. Thanks to its ability to integrate data from multiple sources and systems, it facilitates the unification of dispersed data and provides a more complete and global view of the information.
  4. The data matching and reconciliation process can be automated with Data Match tools, saving time and resources by eliminating the need to perform this process manually.
  5. Provides a more complete view of the information thanks to the ability to integration capacity, being able to perform deeper analysis and get a more accurate understanding of the patterns, trends and relationships present in the data.
  6. With high-quality data and a more complete view of information, Data Match enables more informed, evidence-based decisions.

What is Data Recon and what advantages does it bring to organizations?

Data Reconciliation refers to the reconciliation and correction of data to achieve consistency and quality. As companies adopt multiple systems and databases, it has become increasingly essential to reconcile data to ensure consistency and avoid discrepancies.

Manual data reconciliation, meanwhile, was a laborious and error-prone task, leading to the development of automated tools and techniques to facilitate the Data Recon process.

Within the Conciliac EDM platform, Data Recon is the module that makes it possible to manage unlimited financial reconciliation scenarios, such as banking, credit card, tax, payroll, sales, payments, contracts, claims, customers, invoices, and many more.

This module runs on the same platform as Data Match, allowing you to specify reconciliations.

Five advantages of Data Recon

We put together a few advantages for companies that make use of Data Recon, see:

  1. It ensures that they are aligned and consistent across all sources and systems. By resolving discrepancies and harmonizing data, a unified and consistent view of the information is obtained, thus facilitating the understanding and use of the information.
  2. It automates the correction and reconciliation process, thereby allowing substantial savings in time and resources compared to manual approaches. This, at the same time, allows for more efficient data management.
  3. Data Recon helps you comply with regulatory requirements and internal data quality policies. By ensuring data integrity and consistency, legal or regulatory issues are avoided.
  4. It allows the identification of unusual patterns in data sets, helping to detect fraud, errors or atypical behaviors that might go undetected in unreconciled data.
  5. With reconciled and consistent data, business decisions are based on more accurate and reliable information.

What are the differences between Data Match and Data Recon?

As you have seen in the previous paragraphs, Data Match and Data Recon are two concepts related to data management, but they refer to different aspects of the process.

While Data Match focuses on the process of comparing and searching for matches between different data sets with the objective of finding matching records between different databases or data sets to ensure the integrity and consistency of information, Data Recon focuses on the process of reconciling and correcting discrepancies and inconsistencies found during data matching, primarily within accounting and financial processes.

This may include updating records, removing duplicates or resolving data conflicts.

Both processes are therefore fundamental to effective data management and help ensure the quality and integrity of data used in different areas of the business.

If you have not yet implemented any of these solutions for data management, you need to know the Conciliac EDM proposal that integrates all solutions on the same platform. You manage the data, we help you to make this process simple.