arXiv:2606.13692v1 Announce Type: cross Abstract: Data quality assessment is a critical prerequisite for effective data analytics and data-driven decision-making, yet it remains a challenging task due to the inherently context-dependent nature of data quality. Existing approaches often rely on static rules or manual assessment strategies, limiting their adaptability to diverse usage scenarios and constraining automation at scale. Recent advances in artificial intelligence, particularly large language models, offer new opportunities for automating data quality assessment, but raise concerns rel
The accelerating capabilities of LLMs and the increasing complexity of data environments make autonomous data quality assessment a critical and newly achievable frontier.
Improving data quality dramatically enhances the reliability of AI systems and data-driven decisions, which are foundational to modern economic and technological progress.
Data quality assessment can shift from manual, static, and resource-intensive processes to dynamic, context-aware, and largely automated agentic frameworks.
- · Enterprise data platforms
- · AI-driven analytics companies
- · Data scientists
- · SaaS providers leveraging AI for internal data management
- · Traditional data quality consultancies
- · Manual data governance tools
- · Companies with low data maturity
Companies will experience reduced costs and improved accuracy in their data pipelines due to automated quality assessment.
Higher confidence in data will accelerate the adoption and deployment of more sophisticated AI applications across industries.
This could lead to a 'data quality arms race' where robust data becomes a key competitive advantage, further entrenching leading data-savvy firms.
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Read at arXiv cs.AI