Data Quality Proactive and Reactive Possibilities, Process, and Insights

The health and well-being of business intelligence assets once in production require a level of diligence, communication, and skill to mitigate adverse data events and maintain a high degree of confidence. This means creating the pathway at the consumption layer to report and react to data events quickly. 

Organizations are also recognizing the need to practically mitigate data at its ingestion to ensure accuracy through the pipeline and remediate events before they impact any assets and decisions.  

Leverage all levels of DQ to create a robust data quality strategy by:  

• Diligently bring the assets in production into good health with reactive data quality.
• Proactive DQ to find and report back to the business as close to the source as often as possible data anomalies so they can be researched and repaired at their source before causing havoc on the pipeline.
• Determining when field-level DQ is a worthwhile and effective endeavor.

Speaker: Theresa M Ancick

Theresa is a leader in the emerging data governance industry. She holds 20 years of experience guiding data-driven enterprises in building high-performance data warehouse and business intelligence models. Her experience includes acting as a consultant, strategist and coach as well as being a featured speaker on Data governance topics for major business leaders around the world.