How Data Integrity Ensures Smooth and Accurate Reconciliation of Data

Over the past several years, data has become one of the most important assets of companies. Now is the era of big data and with this new era comes the need for data integrity, leading to the imperative need to reconcile data.

Now more than ever, a huge amount of data is being processed and stored to help companies in decision making and analytics to run a successful business with limited risks.  To rely on data to make accurate decisions or to work on data reconciliation, understanding data integrity and keeping it safe is the first step. 

Definitions of Data Integrity and Data Reconciliation 

Data integrity is all about ensuring consistency and accuracy of data and keeping the data safe when it comes to regulations – like the General Data Protection Regulation (GDPR). The data needs to be reliable and valid during its entire lifecycle and flow. This means it is free from corruption during the processing and storing phases, with a compilation of rules, processes and robust standards that need to be designed and implemented when the data flow and cycle are being structured. 

It doesn’t matter how much time can pass, a good data integrity process will keep the data safe and reliable. 

Data reconciliation is the process or the phase of verifying the data during data migration. In this process, the target data needs to be compared with the source data to make sure that the migration transfers the data correctly.

This verification step is usually done before and after the data migration, to ensure that the target and the source are equal and have the same data. Almost all industries need to make this cross-check, as it’s an important phase of the data flow.

The Importance of Data Integrity and Data Reconciliation 

Data integrity and data reconciliation need to walk together to ensure accurate and reliable data. Data integrity can be compromised during the migration process, so data reconciliation needs to be done correctly to prevent alterations, and data integrity is what ensures the smooth reconciliation of data.

If the data integrity process is robust and works efficiently to keep the data completely safe, then the data reconciliation will happen with no risks of data being altered and corrupted during a migration phase. With data integrity, the verification that is done after the reconciliation of data will show that the target and source are equal and there were no problems when the migration architecture was transferring the data. 

Both data integrity and data reconciliation are extremely important for companies, and they should always be looked at with care. 

The number of failures and risks that data can be exposed to in every minute is enough to damage an entire business and leave serious impacts on the future, especially when it comes to analyzing the data to make strategic decisions and on the reputation of the company.

Malicious insiders, accidental human and system errors and software crashes are just a few examples of what can happen if a good data integrity process is not in place. Companies should have a validation process in place, get rid of unnecessary data, back-up the important ones, make regular auditions on the data itself and on the business roles that have access to it.

When it comes to data reconciliation, the importance is to ensure a smooth migration with no problems. On the migration phase, data can be lost, network failure can happen, the tables and systems can have relationship and communication problems, values can be missing or arrive at the target incorrect, duplicated or badly formatted. 

So, using data reconciliation is what helps to track and measure data from the target to the source, to ensure that it’s still consistent and reliable. Without the reconciliation of data, issues and errors can go unnoticed, causing major problems with data inaccuracy that will put into risk the business operations and future decisions. 

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