AI fails without trusted data. Discover how high-quality data fuels reliable AI—and how AI, in turn, accelerates data quality for lasting business impact.
We are entering a pivotal moment in enterprise technology. AI is no longer experimental; it’s expected. Executives are under pressure to integrate intelligent automation, predictive analytics, and generative tools into their operations—or risk being left behind.
But here’s the truth that doesn’t make the headlines: AI fails without trusted data.
The statistics are sobering. Nearly 9 out of 10 AI projects never reach production, most often because organizations underestimate the fragility of their data foundation.
Inconsistent definitions, duplicate records, and disconnected systems aren’t minor nuisances; they bias models, mislead predictions, and ultimately derail business outcomes.
AI is not just about algorithms. It’s about data quality.
Download Syniti's latest resource: The Dual Relationship of Data Quality & AI
What’s often missed in the conversation is that AI and data quality don’t exist in isolation—they form a dual, reinforcing relationship.
This is not linear. It’s cyclical. Better data makes better AI. Better AI makes better data.
Organizations that embrace this virtuous loop will not only de-risk their AI investments but also fundamentally change the way they approach transformation, governance, and decision-making.
Why do most AI projects fail?
Because organizations underestimate the importance of data quality—issues like duplicate records, inconsistent definitions, and disconnected systems bias models and lead to poor outcomes.
How does data quality impact AI?
High-quality data enables AI models to be more accurate, reliable, and scalable. Poor data introduces bias and risk.
Can AI improve data quality?
Yes. AI can automate cleansing, detect anomalies, and structure unstructured data, creating a cycle where better data improves AI and better AI improves data.
What is Syniti’s approach?
Syniti combines the Syniti Knowledge Platform with proven methodology and expertise to ensure data is governed, harmonized, and continuously improved—building a trusted foundation for AI success.
The opportunity—and the risk—has never been greater. Enterprises are investing billions into AI while simultaneously losing 15–25% of annual revenue to poor data quality. The disconnect is staggering.
The winners will be those who treat data quality as a strategic enabler, not an IT afterthought. This requires more than a toolset. It requires a framework that unites intelligent platforms with methodology and human expertise—so that data isn’t just “cleaned up” once, but governed, harmonized, and continuously improved.
At Syniti, we’ve built this philosophy into everything we do: Data First. With the Syniti Knowledge Platform, we’re proving that trusted data and AI can create a compounding cycle of business value.
Download your copy of The Dual Relationship of Data Quality & AI and see how trusted data can turn AI into a compounding cycle of business value.
Explore how Syniti's AI-driven innovations are reshaping data management for superior quality and efficiency.
Just announced! The enhanced, cloud-native Data Quality and Data Catalog modules are now live on the Syniti Knowledge Platform (SKP).
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