Data Reconciliation – Definition
When the data migrate from source systems to a target analytics platform, it goes through several transformation steps. Many times, even though ETL jobs run successfully, due to one or other reasons data between source and target do not match, creating confusion among end-users and building a lack of trust in data. Therefore, it is essential that we reconcile data on regular basis and ensure the accuracy of the data and build confidence among users. This process of reconciling data is known as Data reconciliation. It is the systematic method of verifying and validating data collected from multiple systems to target systems to ensure that data move from system to system without any data loss or data duplication.
Data Reconciliation – Importance
When data flows from one source system to another, there is a high risk of data loss or discrepancies. Moreover, runtime errors, including network connectivity issues and broken or incomplete transactions can make data invalid, due to which organizations find themselves constantly spending many hours of manual effort to reconcile data. Most importantly, this is not it. There can be a host of problems that can further aggravate the entire process. It includes the following –
- Missing or mismatching records
- Missing or mismatching values
- Duplicate data
- Wrong values
- Ill-formatted records
- Ill-formatted values
- Lack of sync across tables and system
A data reconciliation process looks for all the above errors. So, it goes without saying that without the data reconciliation process, there are high possibilities that these problems can go unnoticed while damaging the overall accuracy of your data.
Fruitful Data Reconciliation With 4DAlert – The Components
To get sure about your data reconciliation process, you need to ensure that the following components are in place –
- Verifying data constantly
The data at the source is changing and updating continuously. Therefore, you need to reconcile it at a constant rate. Doing this will ensure the consistency of data at the origin as well as the destination. One of the best ways to do this is – hiring professional help or need to put your manual efforts. However, 4DAlert allows you to fully automate data reconciliation between multiple source systems and your analytics platform. It avoids manual efforts and helps to give complete and accurate data.
- Making sure to perform data reconciliation at a record count level
At times, record counts are not adequate for data verification. So, what to do? In such scenarios, it is crucial to accommodate data at record count levels. However, with our robust system, you will get time to time alerts when any record count mismatches or any anomaly detected.
- Easy to use data reconciliation system
With 4DAlert, the entire process of data reconciliation at your end gets automated. We make it easy for you while locating and resolving the issues. Improved reconciliation processes lead to reduced risks and costs and can translate into increased end-users’ satisfaction. Less effort spent on manual processing, research, and other forms of intervention saves a significant amount of time and money.
So, now as you know about data reconciliation and its importance to your business, get in touch with us. We will be more than happy to help you!