What if your AI stack is bleeding millions, and you don’t even know it?
According to Gartner, even organizations with modern platforms and advanced analytics lose an average of $12.9 million annually to poor data quality. Hidden gaps in data pipelines, system fragmentation, and weak governance silently erode AI performance, skew predictions, and stall automation, often without anyone realizing the root cause.
This whitepaper provides a strategic roadmap to address these issues, revealing:
- The true cost of bad data: How it impacts revenue, risk, and reputation.
- Cross-industry trends: An analysis of the root causes behind data readiness gaps.
- A five-step data assessment framework: A pragmatic approach aligned with key business performance indicators.
- A real-world success story: How one organization used this framework to slash data processing time by 75%.
- Partner selection criteria: Guidance for choosing a partner who can deliver measurable value.