Data Reconciliation

You cannot trust your data without data reconciliation
If you wish to derive accurate insight from your data, then there can be no room for inaccuracy. Can your data truly be trusted without data revocation? Questions that should be asked are: Is my data that is stored in the data lake or the data warehouse complete? Is it free of missing data? Gaps in data cannot be trusted. This will lead to flawed insights, thus having a direct negative impact on data management projects.

Full Extracts vs. Change Data Capture
Eliminate the strenuous process of conducting a full extraction from the source. Most often, this is done to prevent data loss. But, is this process effective? No, it is inconvenient, time-consuming, and drains the system. This elongated process causes a lapse in the frequency of conducting comprehensive data extractions. Eliminate the hassle with change data capture and data reconciliation to ensure a safe transfer to the destination.

Comparing record counts does not always work
Verification of data should be conducted more frequently to make sure the data is free of errors such as prevention of data retrieval issues, altered data or loaded into the target that could be caused by network or infrastructural issues. This is a solution to eliminate issues temporarily, but it comes with loopholes.

How to verify data completeness
When it comes to large data sources, performing data reconciliation at the column level for the most important columns comes with high demands on the source system that require engineering work, which ends up being expensive. Thus, when data is changing and needs to be updated with a narrow window for data verification, it becomes a tedious task.

Untrustworthy data means delays in getting the insight or worse, flawed insights
Untrustworthy data can cost you more than you think. When your business has lost trust in data, it will begin to work around the data. The very reason why solutions have been built will be lost, thus resulting in inaccurate decisions being made using inaccurate insight derived from data. Data reconciliation should be done at the record count level and at the individual column level. Notifications will be sent with solutions on how to correct these discrepancies. Every Businesses needs the Best Data Quality service for their Business.