Bad data has become an epidemic across all industries in the world today. Bad data affects data quality and productivity, and also creates a lack of confidence that business owners have in their reports.
This is why so many organizations are adopting automated data testing to validate the data going into their reports and improve overall data quality. In a series of blog posts, we want to uncover some of the real-world challenges of data testing. The benefits of data quality are substantial, but implementation comes at a cost.
According to one of Pragmatic Works' Principal Consultants, Brad Gall, the number one challenge he finds when developing and implementing these test packages is that it takes time. The time it takes to develop automated data testing packages needs to be added to the timeline of the engagement or implementation.
Also, some pushback from developers may occur. Most developers want to spend their time developing packages and focusing on the functionality. Once you implement automated data testing that captures bad data, the developer’s world is impacted by added work to find out where the bad data is coming from and to fix the issue.
Pragmatic Works has implemented LegiTest automated data testing with many of our clients, and almost 100% of the time, bad data issues are found in their packages or the source system. Business owners and management teams are thrilled to know their data is clean using an automated process that they can easily monitor for developers. Even better, their data is validated so they can trust their reports.
The cost of quality isn’t free—account for the time needed for developers to implement data testing in your project. To learn more about LegiTest automated data testing or how to make data testing part of your process, visit our website or contact Pragmatic Works to speak to a member of our team.
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