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05 February 2025
The Ataccama Data Trust Report 2025 identifies poor data quality as a critical obstacle to AI adoption. Despite AI’s transformative potential, its success depends on trusted, reliable data. 68% of Chief Data Officers (CDOs) cite data quality as their top challenge, with only 33% of organisations making meaningful progress in AI adoption.
The report underscores the urgency of addressing systemic issues like fragmented systems and governance gaps. Without resolution, businesses risk stalled innovation, wasted resources, and diminished returns on AI investments.
41% of organisations struggle to maintain consistent data quality, directly hindering AI outcomes. Knowledge gaps around data trust and governance slow progress; education is critical to closing these gaps. Trusted data drives AI success: High-quality data accelerates decision-making, enhances customer experiences, and delivers competitive advantages.
As the UK accelerates its AI strategy with the newly unveiled AI Opportunities Action Plan, the report highlights a foundational gap organisations must address: data trust. When data is accurate, reliable, and trustworthy, users can be confident in making informed decisions that drive improved outcomes and reduce risk.
The report emphasises the need for unified benchmarks to guide businesses in building AI-ready ecosystems. Creating a National Data Library is a core goal within the UK plan for homegrown AI and regulatory principles — safety, transparency, and fairness — could be operationalised through national data governance benchmarks. These standards would ensure clear compliance guidelines while supporting the UK’s pro-innovation regulatory goals.
Legacy systems remain a bottleneck to AI scalability, unable to handle real-time, high-volume data demands. With the commitment to sufficient, secure, and sustainable infrastructure, the UK’s investment in supercomputing and AI growth zones enables continuous data quality monitoring and governance. These advancements create scalable, efficient systems tailored to advanced AI technologies.
Embedding governance and automated validation practices into data workflows is crucial for compliance, reliability, and long-term growth. Aligning the UK’s ethical AI initiatives with data trust requirements would ensure AI systems both operate reliably and adhere to safety and transparency principles.
