Data Quality at the Speed of Work

In this fast-paced data-driven world, the fallout from a single data quality issue can cost thousands of dollars in a matter of hours. To catch these issues quickly, system monitoring for data quality requires a different set of strategies from other continuous regression efforts. Like a race car pit crew, you need detection mechanisms that not only don’t interfere with what you are monitoring but also allow for strategic analysis off-track. You need to use every second your subject is at rest to repair and clean up problems that could affect performance. As the systems in race cars vary, the tools and resources available to the data quality professional vary from one organization to the next. You need to be able to leverage the tools at hand to implement your solutions. Shauna Ayers and Catherine Cruz Agosto show you how to develop testing strategies to detect issues with data integration timing, operational dependencies, reference data management, and data integrity—even in production systems. See how you can leverage this testing to provide proactive notification alerts and feed business intelligence dashboards to communicate the health of your organization’s data systems to both operation support and non-technical personnel.