Prerequisite: None
This session will include a moderated Q&A featuring questions from the live audience.
Organizations today face an overwhelming array of data quality metrics, but which ones truly drive business value? Donald Farmer, TDWI research fellow and principal of TreeHive Strategy, will explore how to build effective data quality scorecards that cut through the noise and focus on the measurements that matter most to your organization.
Drawing from real-world implementations across industries, Farmer will demonstrate how to move beyond generic data quality dashboards to create scorecards that align with specific business objectives and AI readiness requirements. He will address the challenge of translating technical data quality metrics into business language that executives can understand and act upon.
This session will cover how to select the right combination of completeness, accuracy, consistency, and timeliness metrics for your unique context, while avoiding the trap of measuring everything but understanding nothing. Farmer will share frameworks for weighting different quality dimensions based on downstream impact and show how to establish meaningful thresholds that trigger appropriate responses.
Attendees will learn to:
- Design scorecards that connect data quality metrics to business outcomes
- Choose the most relevant quality dimensions for AI and analytics workloads
- Establish meaningful benchmarks and thresholds for different data domains
- Create actionable reports that drive organizational improvement rather than just documentation
- Build consensus around quality priorities across technical and business teams
Whether you're starting your data quality journey or refining existing measurement practices, this session will provide practical tools for creating scorecards that genuinely guide decision-making and drive sustained improvement.