What is Data Reconciliation? Importance & Best Practices

Financial Automation
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When you have access to seemingly infinite data, it only makes sense to put it to good use for business insights and decision making capabilities. To do so properly, data reconciliation must take place. But, how can you manage all the data in so little time?

We’re going to share some data reconciliation best practices and take a look at the best reconciliation software to streamline the heavy lifting.

Coming Up

What is Data Reconciliation?

Why is Data Reconciliation Important?

How to Implement Data Reconciliation?

What Terminology is Linked with Data Reconciliation?

What are the Types of Data Reconciliation Methods?

What are Best Practices of Using Data Reconciliation?

What are the Best Data Reconciliation Tools?

Final Thoughts

What is Data Reconciliation?

Data reconciliation is the process of verifying data during its migration phase. When you move data from a source system to its target, you need to be sure that the target data is the same as the initial source data. That means, the data hasn’t been altered incorrectly or lost during the migration process.

Data reconciliation leverages mathematical models to process the information and ensure its validity before the data is used for its output purposes.

Since businesses pull data from multiple sources, be it from customer relationship management software (CRMs), social media, emails, texts, online history, and more, data is coming into organizations from all angles. It’s easy for data to be duplicated, lost, or inconsistent.

An effective data reconciliation framework allows for data to be accurately centralized, of high quality, and consistent across business units.

Why is Data Reconciliation Important?

When making decisions within an organization, you want to remove the guesswork as much as possible. Naturally, the go-to place for verification before moving forward relies on the data - numbers and figures don’t lie! That is, if they are correct in the first place.

Data reconciliation serves as checks and balances so that you can trust your data with high confidence and remove the risk of errors.

Data can be used to implement new marketing campaigns (based on customer buying preferences), product development (based on product/market fit), and forecasting next steps (based on historical and past events).

These are just a few reasons how data can impact a business’ overall functions and operations. Thus, it is of key importance to have the right data in hand.

While the influx of data is hardly a challenge, data management and storage has increasingly caused hardship for businesses seeking to modernize their systems.

As a result, finance automation solutions have emerged to quell the burden. A tool like SolveXia connects your data systems across the board, centralizes your data, and performs data reconciliation automatically to make sure that your data is always up-to-date, clean, and ready to apply for insights to derive value.

How to Implement Data Reconciliation?

Data reconciliation can be performed with the aid of automated systems, manually, or a combination of the two. With automated data reconciliation software, the system will compare data sets for you. This saves your team time, increases accuracy, and helps to optimize productivity levels.

Manual data reconciliation is possible, but it is inevitably error-prone due to the nature of the work. It also ends up being time-consuming.

As such, it’s best for businesses to leverage automation software that can execute these tedious tasks so that you can allocate your team’s time to strategic tasks that require human thought.

What Terminology is Linked with Data Reconciliation?

Whether you choose to perform data reconciliation manually or with the help of automation software, there are some useful terms to know, including:

  • Discrepancy - The difference between two data sets that should be investigated and explained
  • Benchmark accepted value - This is a known accepted value that’s utilized to compare two data sets.
  • Observability - This kind of analysis provides information about what variables can be determined within a dataset, given a set of constraints and measurements.

What are the Types of Data Reconciliation Methods?

There are a few different data reconciliation techniques that can be deployed to fulfill the data reconciliation process. These include:

1. Master Data Reconciliation

Master data reconciliation involves cross-checking the master data between its source and target. Since master data is unlikely to change rapidly, there’s no aggregation needed.

Some examples of master data reconciliation involve checking: total number of rows, total number of items, number of active users, total customer in source and target, etc.

2. Transactional Data Reconciliation

The foundation of business intelligence reports is built upon transactional data reconciliation. This is the process of comparing transactional records.

For example, transactional data reconciliation may compare the sum of the total income calculated from a source and a target, or it could look at the sum of the entire item sold in both the source and target (quantity).

3. Automated Data Reconciliation

Automating the data reconciliation process saves time and prevents errors from occurring. With automated data reconciliation, the system performs the record matching and alerts the team of any discrepancies that need review.

With automated data reconciliation, your business gains increased visibility and stakeholders have unparalleled internal control such as audit trails (with version history).

What are Best Practices of Using Data Reconciliation?

No matter how you choose to execute the data reconciliation process, here are a few helpful tips and best practices to try and follow:

  • Prioritize accuracy with correct measurement errors
  • Set gross errors to zero so that your process is efficient
  • Review discrepancies and investigate as soon as possible
  • Double check the results
  • Set rules and standardize the process

What are the Best Data Reconciliation Tools?

When you’re ready to implement a data reconciliation tool to help improve your business’ insights and confidence in its data, consider using a reconciliation tool like SolveXia.

SolveXia is a finance automation software that enables data processing, secure database storage, no-code automation, reporting, and audit trails.

SolveXia is designed to help teams with an array of complex business processes and finance functions, such as: reconciliation, reporting, expense management, rebate management, and the like.

With SolveXia, you can manipulate files automatically into any format. The system will connect with your existing technologies, including legacy systems and centralize your data. As such, your team will be able to reduce errors by 90% and run processes 100x faster.

This way, you have an overview of how your business is performing, along with its financial data, in real-time and with utmost accuracy.

The ability to centralize and utilize your data for insights provides you with a competitive edge and optimizes your decision-making abilities. It also helps to better service customers and add more value, making it possible to work smarter and enable maximum controls over processes.

SolveXia comes with a library of supported data connectors (or you can create your own) to unify all your data. With automated data transformation and drag-and-drop features, anyone can simply use the system, without the need for dedicated IT teams or coding.

The out-of-the-box solutions allow you to aggregate, filter, sort, map, and reconcile data immediately.

Final Thoughts

Having data available in your business is just the beginning of being able to derive value from the figures. In order to transform the data into insights, data reconciliation must take place to validate your data.

As data volume grows and your workload increases, the need for data reconciliation software also grows. Get started with automation today by requesting a demo from SolveXia - we’re here to help!


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