The more businesses deal with data, the more they will deal with data redundancy. Data redundancy happens when the same piece of data is stored or located in more than one place. And as companies change the way they process data and move away from siloed data to using central datasets with repositories to store information, they start to discover inconsistencies and duplicated entries.
Maneuver information redundancy in the data process can be challenging, and it’s also hard to benefit from those duplicated entries, but with data validation and reconciliation, it’s possible to understand how to reduce them and track any possible new redundancies, so the business can be efficient to mitigate any inaccuracy information and avoid long-term issues.
Some companies do make data redundancy an intentional process – they leverage the occurrences of data to make tests, and be able to recover it in case of disasters – in order to protect data and ensure that it will be consistent and high-quality. To make intentional redundancy, it’s important to keep a central space to track and store the data, so the company can easily update the information and see the changes. But when data redundancy happens by accident, it can be the result of inefficient processes and complex formats.
Data Validation and Data Reconciliation Processes
To manage information redundancy in the data process inside any organization, the most critical processes are data validation and reconciliation. They are both essential parts of any data handling task – it’s the way to ensure that all the information is correct and accurate before use.
But even though they are an important step inside a company’s data process, many of them still skip data validation and reconciliation, a mistake that can be fatal to avoid redundancies. Both processes are quick and can be automated to happen inside the organization’s data flow, so instead of being an extra step, they become part of the workflow.
Data validation is the process of checking the source the accuracy and quality before importing, processing and using the data. It’s a validation and verification task completed before the reconciliation step that should give the data user accuracy, quality and all the details he may need in order to mitigate issues.
As the most essential rule in data validation is the one that ensures data integrity, redundancies can be found already in this stage, while running the quality and accuracy checks at the source. The rules to process data will be defined by each company according to their needs, so the verifications to keep the data flowing with high-quality and zero redundancies – or with the planned and intentional redundancies – will vary from one organization to the other.
Data reconciliation (DR) is a process of verifying the data during data migration. In this step, the data will be compared with the source to guarantee that the migration was correct, and the migration architecture is transferring data accordingly.
This process will catch the mistakes that occurred during the migration and map the broken transactions that could lead to corrupted, duplicated, incorrect and broken data.
Managing Data Redundancy
Information redundancy sounds like a negative issue that will corrupt datasets and make the data inaccurate with poor quality. But this is only the case when redundancies appear when not planned and without the data validation and reconciliation processes as part of the workflow. Some companies manage information redundancy as part of their strategy and benefit well from it.
- Redundancy as a data backup: one of the best ways of creating an extra layer of protection for the data is by replicating the data in an additional data store or application. If any issues happen, there’s an older version of the data to come back to as a recovery plan.
- Increase the security: cybersecurity issues can happen to any database or storage, so keeping the company’s data stored in more than one place is one of the most effective ways to recover from this problem.
But the disadvantages of information redundancy can be seen by those that planned for it or not.
- Possible inconsistencies: when the same data exists in several places, they can be existing in different formats without anything tracking its changes. That’s how unreliability happens.
Bigger databases: the size and complexity of databases will increase when there’s data redundancy – the entire data flow will also become more challenging to be monitored. A bigger database will also take longer to load, research, perform migrations and updates.
