Data Quality is a challenge that needs to be owned by Data Professionals. Given the volume of growing data we deal with and complexity of data transformations we implement in our jobs, we always need options to reduce tedious effort in testing and ensuring data quality. 

• Data Validation needs to be thorough (100%) as spot checks are not sufficient. 
  
• Data Quality is more important for business critical functions
• Data Quality monitoring in production requires need to be automated as manual checks are not scalable. 

We recommend checking out following tools. Use the trial version to implement your scenarios before you recommend adoption for your organization

QuerySurge

Datagaps

If you want to list any other tool or options you have used on this page, please let us know