The Single-Most Overlooked Part of the Data Integration Process

The Single-Most Overlooked Part of the Data Integration Process

Without data integration, accurate analytics are impossible to achieve. Imagine trying to make a decision based on incomplete data, or worse, data that has not been checked for quality assurance. The less information available, the more likely a decision leads to an undesirable outcome. Now, multiply this challenge—decisions will now involve millions of dollars, hundreds of data sources, and terabytes of data. In order to steer a business correctly, data integration needs to handle a heavy burden.

Data integration is a combination of technical and business processes. It supports the analytic process by by aligning, combining, and presenting each data store to an end-user. Organizations increasingly view data integration as an enterprise imperative for data delivery and governance as well. Data integration allows organizations to better understand and retain their customers, support collaboration between departments, reduce project timelines with automated development, and maintain security and compliance.

Quality assurance testing ensures that the data integration process is optimally implemented. This means procedures and standards are deployed in a way that makes sense in accordance with the software on-hand and the intended requirements of the project. It differs from quality control in that the former is a corrective process. The goal of quality assurance is to ensure that quality control measures are never needed, basically.

There are many reasons why an organization may fail to run quality assurance at the start of a data integration project. These can range from a lack in understanding the benefits all the way to concerns surrounding investment cost for a dedicated tool. The repercussions for quality assurance avoidance are numerous, even catastrophic in some circumstances. They obviously include elongated timelines for running data analysis, but could also mean poor data quality, and ultimately, unhappy customers.

Data workers should consider their options for quality assurance testing when integrating data from a data warehouse or other source. Organizations can implement their own set of procedures to track against data movement, or they can look to deploy a dedicated ETL testing and data monitoring product for which there aren’t many, but one comes to mind. It used to be that big data was top-of-mind, and the only thing anyone cared about was compiling as much as possible. This deluge has made quality assurance testing an increasingly important aspect to a well-rounded data architecture.

Timothy King
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