A Guide to Data Reconciliation

In today's business world, having accurate and up-to-date data is essential for making informed decisions across all aspects of your operations.

With the right tools, technologies, and processes in place, businesses can leverage data reconciliation to drive operational efficiency, mitigate risks, and achieve their strategic objectives in a constantly evolving data-driven world.

In this article, we’ll help you fully understand what data reconciliation is and how it works, why it’s important, and how you can implement it for your organisation.

What is data reconciliation?

Data reconciliation is an ongoing process that involves comparing and aligning data from different sources or systems, ensuring everything is consistent and accurate. This can occur during data migrations or routine checks, where any discrepancies between datasets are identified and resolved.

It’s never a one-off process. In order to maintain data quality and integrity, it needs to be performed regularly. Using data quality tools is an easy way to set up your data reconciliation process and maintain it. It’s crucial for accurate reporting and decision-making across industries like finance, energy, and telecommunications.

Why is data reconciliation important?

Data reconciliation helps to make sure that data is accurate and complete across multiple sources. For instance, having clean, reliable data that clearly demonstrates customers’ preferences can help guide marketing strategies and product development more effective. Operational data is equally important, aiding in streamlining internal processes, managing resources effectively, and even enhancing employee training and sustainability efforts.

The main benefits of data reconciliation include:

Consistent and reliable record keeping

Data reconciliation ensures that stored information remains consistent and reliable by detecting and correcting discrepancies and errors before they impact business decisions.

Make informed business decisions

Data reconciliation provides decision makers with data they can trust when making key decisions that relate to the business. In competitive business landscapes, quick decisions are often needed to gain an edge so it’s important to know you can trust the data you are using, resulting in more assured strategic decision-making.

Find anomalies

Data reconciliation uncovers anomalies that may indicate fraud or accidental errors, helping data managers take prompt action to avoid any and significant losses.

Enhance internal processes

Reconciled data streamlines internal processes, reducing time spent on error detection and improving overall productivity.

Helps identify and prevent future errors

Ongoing reconciliation improves data quality over time, resulting in more accurate and meaningful analysis outcomes.

Keep up with compliance

In order to remain compliant with GDPR, accurate record-keeping is required. Data reconciliation helps with data accuracy which is a necessity for keeping everything in order and above board, helping you avoid potential regulatory penalties.

Without data reconciliation, errors can arise in your data such as:

> Incomplete records
> Missing values
> Incorrect values
> Duplicated records
> Poorly formatted values
> Broken connections between tables or systems

With the right solutions, you experience benefits like improved operational efficiency, stronger regulatory compliance, and reduced risk.

How does data reconciliation work?

There are multiple steps that need to be completed during the data reconciliation process. They can be broken down as follows:

Collecting data

To begin with, data needs to be collected from various sources, which could involve extracting it from databases, files, spreadsheets or other sources.

Validating data

The data you have collected then needs to be validated to guarantee that it is in a complete and consistent format. During this step, you’ll make sure the data isn’t missing any information, contains any formatting errors, verify the data types, identify outliers and look for any other data quality issues.

Comparing data

Once you have all your different data sources together, you need to compare them to look for any inconsistencies or differences. Matching important fields or variables between datasets is vital for this step. For large datasets, you’ll need to depend on calculating different aggregations of data and comparing results between them.

Detecting errors

Once you have compared your datasets, if you have identified any inconsistencies or differences, you’ll need to analyse why this has happened. It could be down to mistakes during data entry/capture, such as system failures or formatting issues.

Resolving errors

Any inconsistencies need to be corrected during this step. The data needs to be aligned so it’s all in one consistent format. Issues can be resolved in different ways and will depend on what the problem is and the systems being used. It could require manual changes, data transformations, system updates, or speaking with relevant stakeholders to correct things.

Verifying data

After you have corrected inconsistencies with your data, it’s vital to verify the accuracy of it. You need to recheck the data thoroughly to make sure that all errors have been rectified and that datasets are fully aligned. Performing validation checks, comparing the data again, or undertaking entirely new tests can help verify that the data has been correctly reconciled.

Documenting steps

In order to keep track of your data reconciliation process, you should document the steps taken to ensure it has been done thoroughly. This will also help with any future reconciliation tasks that are undertaken and ensure compliance.

The process for data reconciliation could differ depending on context, your industry or how complex the data is, but the above steps are a general guide for performing this process effectively.

When is data reconciliation needed?

Data reconciliation plays a crucial role in identifying errors and inaccuracies, whether data is scattered across multiple systems and formats within different business divisions or during migration between source and target systems. Here are some scenarios where data reconciliation helps:

Data migration cases

Data reconciliation is instrumental in identifying and rectifying omissions or errors that can arise during data migrations between different systems. It ensures that data integrity is maintained by verifying that the information in the original source system matches or aligns with the data in the system it’s being moved to, once processing has been completed.

Various technologies and methods are employed to ensure data consistency post-migration. These approaches often involve comparing the total number of columns and rows migrated to ensure consistency in totals, such as the total sales value for a specific period. Additionally, technologies like checksums are utilised to compare small data blocks, identifying any inconsistencies between systems and ensuring accurate data transfer.

Performing these checks help give businesses confidence that all their data has been safely migrated and is accurate. Our Data Management Solutions can help ensure your data migration goes smoothly.

Data quality and consistency assessments for business-as-usual cases

The second key case for using data reconciliation is when checking data accuracy and consistency across an entire organisation.

Periodic checks are conducted to verify and maintain consistency across multiple business divisions and systems. These checks highlight any discrepancies found in records, underlining the further need for data cleansing and data validation.

This reconciliation process may involve:

> Confirming the accuracy of customer details between the CRM system and billing systems
> Ensuring product prices are aligned in product catalogues
> Ensuring product prices are aligned in e-commerce platforms

Often, data reconciliation tools utilise reference datasets to uphold information consistency across various systems and business divisions.

It's critical that routine data checks are performed regularly, as data undergoes frequent updates across multiple systems, presenting ongoing opportunities for errors or inconsistencies.

For businesses experiencing growth through mergers and acquisition, this kind of approach is invaluable. It aids in scenarios where custom data integrations connect disparate systems within the business or where offline processes are utilised to migrate data across different systems throughout the business. In such cases, data reconciliation is able to support techniques like gross error detection, which can identify potential systematic errors in measurements or data.

Complex financial services cases

Data reconciliation in financial services demands specialised functionality to accommodate fluctuations in macroeconomic factors like inflation and other variables such as currency conversions and tolerances for rounding up errors and margins.

Along with comparing data from multiple systems, financial data validation and reconciliation require extra functionality. This usually involves overlaying algorithms or business logic to fully account for all variables. This helps ensure that data is formatted so that monthly, quarterly, or annual reporting is easy to perform and analyse.

How can Sagacity help?

Our solutions enable swift and efficient analysis to help with data reconciliation by pinpointing errors in large volumes of data and finding inconsistencies across different systems. We help you to proactively detect and prevent various issues, such as formatting errors and changes in customer details, so you can take immediate action to correct them.

Our Smart Link data validation tool empowers all decision-makers as they can easily access data reconciliation checks with minimal training needed, helping to build company wide trust in data.

We offer Data Cleansing Solutions that seamlessly integrate with your existing systems and workflows, ensuring quality and consistency across all data. It’s highly scalable, and suitable for both large-scale data migration projects and routine data reconciliation tasks.

Conclusion

Data reconciliation is a key process for ensuring the integrity, accuracy, and reliability of data within any business. It serves as a vital process for identifying and rectifying errors, inconsistencies, and discrepancies that may arise during data migrations, routine checks, or across various systems and divisions.

By implementing data reconciliation processes, businesses can fully trust the accuracy of their data, enabling more informed and strategic decision-making across all levels.