Continuous Data Reconciliation for 100% Complete Data.

Data Reconciliation

What is Data Reconciliation?

The process of data integration starts with replicating data from different sources before it is merged and transformed into a format that is suitable for use on the destination database or system. But before that you need to verify that the target data is the same as in the source system. Data Reconciliation is the term given to this verification of the target data against the original source data.

Why Data Reconciliation is essential

You cannot trust your data without data verification

So you have your data in your Data Lake or Data Warehouse. But how do you know that it is complete, and that there is no missing data? Without high quality, complete data all your clever analytics and data insights simply cannot be trusted. Incorrect data will lead to flawed insights, and that isn’t what you want for your data management projects. How to Manage Data Quality (The Case for DQM)

Full extracts vs Change Data Capture

Some organisations rely on full extracts from the source to prevent data loss. These are cumbersome, take lots of time to extract and load and tax the system heavily. Because of this, full extracts of data are done infrequently, e.g. at the end of the day. Change Data Capture using transaction logs is a much better design pattern for replicating data to the target, as it can be done more frequently. It has zero impact on source and is quick to extract and load. With Change Data Capture, data reconciliation is essential to make sure all the data has landed safely in the destination. Zero-ETL, New Kid on the Block?

Comparing record counts does not always work

You need to constantly verify your data and make sure that some network issue or other infrastructure issue has not prevented the data from being extracted, transformed or loaded into the target. Some organisations rely on record counts and compare source and destination counts. This is better than doing nothing, but it still does not solve the problem completely. If updates are not properly captured or applied, the record counts may be the same but the data can be drastically different.

How to verify data completeness

Data reconciliation has to be done at the column level for the most important columns and this is a tall task for large data sources as it puts a huge load on the source systems and needs a lot of engineering work – an expensive exercise in both respects. And when data is changing and being updated constantly, if the source system does not really have a quiet time or has a very small window for data verification, this is impossible to achieve. Successful Data Ingestion (What You Need to Know)

Untrustworthy data means delays in getting to insight or worse – flawed insights

When your business loses trust in the data, they will try to navigate around the data platform that was built to solve this very requirement – to provide a scalable, trustworthy foundation for all data management projects and insights. Desperate measures equal bad and unusable data. For maximum effectiveness, data reconcilation should be done at record count levels AND at the individual column level with high performance. If there are any discrepancies, the data verification software should provide timely notifications when data discrepancies are found and easy ways to fix these. How to Manage Data Quality (The Case for DQM)

BryteFlow TruData is BryteFlow’s automated data reconciliation and validation software that checks for completeness and accuracy of your data.

Take a first hand look at our data reconciliation tool. Get in touch with us for a FREE Trial.