8 Proactive Steps to Build Trusted Data for Analytics and AI
TechTarget, Friday, July 24th, 2026
Eight strategic steps for building high-quality, trustworthy data for analytics and AI applications.
Because AI systems amplify data quality issues, organizations need approaches that go beyond technology alone.
The eight steps are defining trusted data standards, establishing clear accountability, preventing errors at the source, continuous monitoring, addressing root causes, standardizing metadata, empowering data stewards and building an organizational culture of data responsibility.
Together these practices ensure reliable data for both human decision-making and autonomous AI agents while reducing ongoing quality management costs.