Webinar Recap: Tackling Data Quality Myths

Tackling Data Quality Myths Webinar Image

Tackling Data Quality Myths

Curious about data quality myths and best practices? Check out our latest webinar featuring Gorkem Sevinc and Prashanth H. Southekal, recording embedded at the top of this post! Here’s a quick summary of what you’ll learn by watching the webinar.

The Importance of Data Quality

Data management is a significant expense, accounting for 91% of costs at the average financial institution. Poor data quality can cost companies up to 12% of their revenue, impacting all sectors from manufacturing to energy.

Defining Data Quality

Data quality is essential for smooth business operations, compliance, and performance management/analytics. It’s not just about technical accuracy—timeliness and integrity are equally crucial. Embracing fit-for-purpose data quality and continuous improvement are key to ensuring reliable data.

Debunking Data Quality Myths

During the webinar, we debunk several pervasive myths:

  • Myth #1: 100% data quality is needed for effective analytics. Often, 75% suffices.
  • Myth #2: The value of data is constant. It varies based on business context, data type, and lifecycle stage.
  • Myth #3: Data quality is solely a technical issue. It requires collaboration between business and IT departments.
  • And many more…

Continuous Improvement Approach

Data quality degrades by 2-7% monthly without continuous improvement. Treating it as a one-time project is ineffective. Technology can enable scalable, sustainable data quality programs.

Strategies for Improvement

Enhance your data quality with the DARTS framework:

  • Define: Establish clear data quality criteria.
  • Assess: Regularly evaluate data quality.
  • Realize: Implement improvements.
  • Sustain: Maintain ongoing quality standards.

Focus on root cause analysis, leverage data integration capabilities, and enforce proactive data governance to maintain high-quality data.

Watch the full webinar to dive deeper into these insights and learn how you can improve your data quality. Understanding its importance, dispelling common myths, and adopting a continuous improvement approach will help safeguard your data and drive better business outcomes.

Share:

Related Posts

Data warehouse governance is a framework that includes policies and practices to manage data quality, security, and availability within a data warehouse. Here are 7 best practices for effective data warehouse governance.

Search

Automated data quality that supports your company at scale