Data Reconciliation Best Practices & Tools

Across departments and in an ever growing manner, your business continues to collect data every single day. That’s a great asset, if you know how to use it correctly. Data reconciliation best practices can help to ensure that your data is consistent and up-to-date.

Let’s take a look at what the data reconciliation process entails, as well as how you can leverage helpful tools, like SolveXia, to streamline the process.

Coming Up

What is Data Reconciliation?

Why is Data Reconciliation Important?

What Terminology is Used in Data Reconciliation?

What are the Types of Data Reconciliation Methods?

When is Data Reconciliation Needed?

What are Common Data Reconciliation Challenges?

What are Best Practices to Improve the Reconciliation Process?

What are the Best Data Reconciliation Tools?

Wrap Up

What is Data Reconciliation?

Data reconciliation refers to tools and technologies that maintain the integrity of data, especially as it moves between systems or through its business-as-usual (BAU) scenarios.

The process of moving data from one source system to any other target system is called data migration. Data reconciliation is a checks-and-balances of sorts that helps to make sure that the data doesn’t lose its accuracy while it is on the move. In practice, data reconciliation tools compare target data with source data.

Why is Data Reconciliation Important?

Having data available is only step one. You, of course, want to make sure that it is the right data and that in its transfer of any kind, its integrity has not been compromised. Data reconciliation is the process to ensure data accuracy, especially when it has been migrated.

Data reconciliation allows organizations to:

1. Remedy Manual Mistakes

If data is being handled manually, the chance for human error is high. Data reconciliation helps to catch it.

2. Format Data Accordingly

Different systems may compress data and affect the format for storage sake and transactional speed.

3. Fix Data Inaccuracies

Data changes over time, especially in terms of customer data and product information that constantly gets updated.

What Terminology is Used in Data Reconciliation?

In the realm of data reconciliation best practices, it is useful to know the common terminology used. Here are some key terms:

• Discrepancy: Refers to the difference between two data sets that requires explanation.
• Benchmark: A benchmark value is used to compare two or more data sets.
• Transaction-level data reconciliation: Comparing transactions between two systems to ensure completeness and correctness.
• Data validation: Making sure that data is up-to-date, complete, and accurate.

What are the Types of Data Reconciliation Methods?

There are two main types of data reconciliation methods, which can be categorized as either system-level or transaction-level. Let’s see how they differ:

1. System-level Data Reconciliation

System-level data reconciliation compares two systems to identify any discrepancies in data. For example, you may find different values or records between a source system and a target system.

2. Transaction-level Data Reconciliation

Transaction-level data reconciliation is the action of comparing transactions that exist in two different systems to make sure that their information is complete.

When performing data reconciliation, there’s the option of using computational models, trend analysis, manual comparison, or statistical analysis.

Manual comparison tends to be common, but it is also the most time-consuming and error-prone. When you are utilizing data to complete processes or perform analysis, any tiny mistake can cause a ripple effect of negative consequences.

That’s why many organizations turn to automation software to handle the data collection, transformation, and automation needs. SolveXia integrates with your existing systems to pull the data required for any specific task, transform it into the right format, and then utilize it accordingly for utmost accuracy.

When is Data Reconciliation Needed?

If you take a moment to think about the mountain of data that your company collects, you can already deduce that data reconciliation is needed for many different purposes.

Data reconciliation best practices will prove that reconciliation is needed in three main scenarios, namely:

1. During Data Migrations

Data is often moved between systems, so data reconciliation ensures that the data that began in the source system is in alignment with the data that ends up in the target system. Automation software can be used to perform such checks in less time than it would take to do so manually.

2. To Maintain Consistency

During business-as-usual scenarios, data is put to work. In order to reap the proper results, the data must first be confirmed to be accurate and consistent across business divisions.

3. For Financial Services

When it comes to financial functions, accurate data is paramount and can make or break a business. Beyond comparing data between systems, business logic and algorithms are applied and data formats are set for recurring processes, such as accounting and reporting.

What are Common Data Reconciliation Challenges?

The struggle with data reconciliation has been felt across organizations. As a solution, many turn to the use of automation software to help manage data needs, from collecting into a centralized and secure repository to being able to transform data as needed to capture deep insights.

That being said, many common data reconciliation challenges include:

• Lacking a single version of truth due to having the same dataset across different systems
• Having to fund expensive solutions and technologies
• Siloed data systems and sources that create inconsistencies
• Custom solutions that require IT teams to use them

By using a low-code platform to overcome manual processes, teams can free up their time and work faster with 90% fewer errors. Automation software makes it possible to unify data across systems and spreadsheets, without any need for IT teams interference.

Plus, with data manipulation and data calculation being automated, stakeholders can trust the analytics and dashboards at their fingertips to make critical decisions in real-time.

What are Best Practices to Improve the Reconciliation Process?

Data reconciliation best practices allow your team to operate on the same page with the same goal. While every organization handles their data reconciliation differently, there are a few key practices to deploy that can help anyone improve and protect data quality.

Keep in mind these data reconciliation best practices as you execute data reconciliation:

1. Set a Defined Process

First, have a defined process for data reconciliation. It should be standardized across your organization’s departments, too. The data reconciliation process involves these data reconciliation steps:

1. Select the data that needs to be reconciled
2. Compare data between the source and target systems
3. Identify discrepancies between the two systems
4. Resolve discrepancies
5. Update the systems
6. Repeat regularly

2. Use Automation Tools

No matter how big or small your datasets are, automation software exists to alleviate the burden on your team!

With automation software, your team can free up their time to allocate it towards high-value responsibilities that require human intellect and strategy. Data reconciliation tools save you time, money, and mistakes.

With automation tools, you can easily connect all your systems, including payment processors (i.e. eBay, Stripe, Amazon, etc.). The automation software empowers your finance teams to be able to continuously improve processes and leverage accurate and timely data to improve your business’ functions.

Low-code solutions make set-up, with limited need for IT! Plus, you can then utilize the tool to prepare and report data, freeing up your team’s time to focus on analysis, communication, and strategic endeavors.

3. Train Properly

Despite deploying the best automation and data reconciliation tools, human error can still rear its ugly head. For example, an individual may accidentally delete an important file.

This is why it’s so important to firstly train your team properly on whatever software solution you choose to use, but also to have a plan in place if and when human mistakes occur. With adequate training, you can minimize mistakes.

4. Involve Business Users

While it may feel or seem like data reconciliation is just a job for your IT team, it isn’t the case. Business users should be aware of how data is being used within the organization so that they can aid in keeping it together and formatted properly. When business users are in the know, they can easily identify if data is mismanaged or incorrect and work together to fix the issue.

5. Keep it Simple

Simple processes are easier to understand and to follow. Keep your data reconciliation process as straightforward as you can. And, use automation software wherever possible– by doing so, you can immediately mitigate risks and reduce errors from taking place.

What are the Best Data Reconciliation Tools?

Data reconciliation best practices are made easy when you have the right data reconciliation tools by your side. SolveXia is a multi-functional platform that automates spreadsheet driven data preparation and manual processes.

1. SolveXia

SolveXia transforms your data so you can transform how you work. The software has drag-and-drop components so anyone can build, deploy, and run repeatable processes. The tool enables any team member to aggregate, filter, sort, map, and reconcile data.

Additionally, it makes it easy to unify data from systems and spreadsheets with data connections and integrations. SolveXia supports multiple data formats, including but not limited to: Excel, CSV, Word, PDF, Access, and more.

2. OpenRefine

OpenRefine, previously known as Google Refine, is a database reconciliation framework to help clean up and transfer data. Its features include: clustering, reconciliation, faceting, and version control.

3. DataMarch Enterprise

DataMatch Enterprise integrates, links, and prepares organizational data with an out-of-the-box profiling tool. It will automate data cleansing, deduplication, and matching.

Wrap Up

Data reconciliation best practices can completely transform how your business works with its data. With optimal data integrity, accuracy, and access, stakeholders and your team alike will have the information they need to make the right decisions.

Using automation and data reconciliation tools takes the hassle out of manually cross-referencing data across systems, while ensuring that data is always up-to-date and usable!

To try SolveXia out, we invite you to request a demo to see how it will benefit your business.