Understanding the 4 Steps of Data Testing

Successful data conversion projects are often a result of great planning, due diligence and clear expectations. However, when it comes to data validation and testing, things can get tricky. Everyone knows that projects dealing with mission-critical data involves attention to detail by the project team, but sometimes the need for is lost on some of the other parties involved in testing, including end users.

The questions and tips below provide background on the data testing process and will help you determine both the level of testing and amount of outside staff involvement needed for an efficient and accurate data conversion.

Step 1: Subject Matter Expert Testing

During this process, experts in both the source data system and the new data system test data and identify any issues they see, especially from a support standpoint. The subject matter expert testing will validate general criteria to be tested by end users and make sure that all necessary elements are included in the formal testing plan.

Step 2: End User Acceptance Testing

End user acceptance testing occurs in three phases: small-scale, large-scale, and full-scale. This testing method uncovers data conversion issues experienced by end users. Upon successful completion, this iterative testing will determine when data conversion can start production data loads.

Step 3: Testing Preparation

Efficient and accurate testing requires a solid testing plan that includes enough detail that users understand what is expected of them. Leaning on super-users for testing is one way to make testing more efficient, but sometimes that isn’t possible. Some other considerations when preparing for testing are:

Step 4: Testing

Testing can be a long and grueling process that requires a lot of time and attention from a lot of individuals in your organization. Luckily, there are ways that the burden of testing can be diminished, resulting in more efficient testing and a decreased resource drain. Some easy ways to reduce the demands of testing are:

Data conversions are never a walk in the park, but by having a solid plan and clear expectations, you can help mitigate issues before they arise and create a more efficient process. Testing is also simplified when data validation goes smoothly.

Have questions about an upcoming data extraction or conversion, or need support? Let us know and we can help.