Build the Foundation for AI Success With Enterprise-Grade Data Quality
AI can’t deliver without trustworthy data. According to the 2024 Gartner® AI Mandates for the Enterprise Survey about 40% of AI prototypes make it into production, and participants reported data availability and quality as a top barrier to AI adoption. . We think this Gartner report , “How to Design an Effective Data Quality Operating Model”, gives you a step-by-step framework to shift from reactive fixes to proactive governance — so your AI and analytics initiatives actually deliver.
What You’ll Learn
The 6 core components of an enterprise data quality operating model (DQOM)
How to scope data quality programs around business outcomes, not just IT hygiene
Tactics to embed GenAI, automation, and AI agents into your data quality stack
How to measure and monitor data quality with “good enough” thresholds
Steps to scale from tactical projects to enterprise-wide impact
Why It Matters Now
By 2027:
70% of organizations will adopt modern data quality solutions to better support their AI adoption and digital business initiatives.
The application of GenAI will accelerate the time to value of data and analytics governance and master data management (MDM) programs by 40%.
Our Key Takeaways from the Report
Proactive, not reactive: Most orgs fix issues ad hoc. Gartner provides a scalable model for consistent, future-proof quality.
From metrics to ROI: Learn how to benchmark, track and improve quality — and link those improvements to real business outcomes.
AI-ready infrastructure: Equip your teams with quality data at the source, supported by governance, automation, and continuous improvement.
Who Should Read This?
This report is a must-read for:
Chief Data & Analytics Officers (CDAOs) prioritizing trustworthy data for AI, decision-making, and regulatory compliance.
IT and Data Leaders seeking to implement a scalable, cost-effective data governance framework.
Data Quality, MDM, and AI Teams aiming to operationalize quality across use cases like analytics, MDM, and transactional data.
Included in the Report
Critical components: scope, metrics, governance, process, roles, and technology.
Use case mapping: from MDM to AI, analytics, and data engineering.
The Three Rings of Information Governance to prioritize your efforts.
Frameworks to align DQ strategy to your enterprise D&A roadmap.
Bonus: See How We Believe Syncari Accelerates Data Quality at Scale
Syncari helps you activate the Gartner data quality operating model with:
Continuous unification and data quality across all connected systems
Autonomous schema sync and field mapping to minimize manual effort
Real-time multi-directional sync with your source systems and destinations
Built-in observability and governance for compliance and auditing
Download the Gartner Report Now
Get immediate access to your complimentary copy of “How to Design an Effective Data Quality Operating Model.” Learn how to elevate your data quality strategy — and power every AI initiative with trusted data.
Complimentary Gartner Report – Limited Time Access
Syncari is proud to provide this Gartner research to help organizations operationalize trustworthy, AI-ready data.
Gartner, How to Design an Effective Data Quality Operating Model, By Sue Waite, Melody Chien, 15 July 2025
