Data reconciliation (DR) is a step of verifying the information during the data migration process. During reconciliation, the target data gets compared with the source data in order to ensure that the migration occurred successfully and all the information was transferred correctly with no missing or incorrect entries.
Data reconciliation and data comparison processes can often be considered as not crucial steps inside most companies’ migration plans, which can be a big mistake in ensuring that the migration architecture is transferring the right data.
The process of data validation and reconciliation (DVR), along with data comparison, will analyze all the information migrated with the use of technology with mathematical models to check if the data migrated from the source to the target is correct – some companies can’t have missing data, incorrect formats or any other issues with their transferred information, so they double-check the process by running the data reconciliation and data comparison processes before and after data migration, ensuring that the source and the target are 100% equal.
How Should You Implement a Data Reconciliation Process to Large Data Sets?
When it comes to massive data sets, companies worry about how the process will happen in order to reconcile everything right without disruptions or downtimes that can affect productivity.
The common approach to reconcile data relies on a simple record count that observers if the right number of records were migrated. This approach became traditional, as the processing power needed to perform the reconciliation used to look field-by-field, but with this method, one single mistake, format error or missing record could go completely unnoticed, and would then cause other issues on the data set.
The current and modern solutions to data migration already provide companies with reconciliation and comparative functionalities that can deal with the full volume of information on the data set, this way, any mistakes or duplicated records can be easily located.
Some companies still run their data reconciliation process on Excel, which can work depending on their volume and needs, but Excel can be time consuming and has a limit of 100,000 rows of data that can be processed at each time. But massive data sets can have millions of rows to be reconciled and to avoid crashes Excel won’t be the best solution.
Tools designed for large data sets will handle the job easily and will deal with the data according to the rules you define, respecting the data complexity. Another solution for companies that use statistics and mathematics info is to work with R, MATLAB or Python.
To implement a successful data reconciliation process, a few standard practices need to be considered:
- The process should have as an objective the measurement of the errors
- No errors are allowed. Gross issues should be zero or the data reconciliation process will be inefficient
- The standard approach to data reconciliation in which a simple record count is tracked to see if the right number of data was migrated works, but it must be adapted to fit your needs and the size of the data set.
- Data migration solutions that have simple reconciliation capabilities will deal well with the full volume of the data you’re migrating.
Types of Data Reconciliation Methods
The type of data reconciliation method also matters when it comes to large data sets. Choosing the right one will be crucial to ensure the success of the process.
1. Master Data Reconciliation
The master data reconciliation method will reconcile the master data between the source and the target. It’s commonly used to reconcile the total number of rows, total customers, the total number of items, the accuracy of activity and the number of active and inactive users.
The success of this method depends on guaranteeing that all the transactions are valid and correct and if they were properly authorized.
2. Transactional Data Reconciliation
This technique uses BI reports as a base, so any errors will directly impact the report’s reliability. The transactional data reconciliation method is normally used in terms of the total sum of information, for example, the sum of total income calculated at the source and at the target, the sum of the entire item sold, etc.
3. Automated Data Reconciliation
For the larger data sets, the best way to be successful is by automating the data reconciliation process and keeping it as part of data migration. Automation is also better and more convenient to keep all the data users informed about the reports at the end of the migration process.
The Best Reconciliation Solution for Large Datasets
The Emissary data reconciliation tool is easy to implement, but it’s a powerful platform to reduce risks and help ensure regulatory compliance, making the time spent on data more strategic and productive.
With the use of artificial intelligence and machine learning, the platform can reconcile datasets large and small. It is an enterprise solution engineered to meet all of your reconciliation needs.