Data quality gaps
CIOs have been in a long struggle to improve data quality by assigning data stewards, automating data cleansing procedures, and measuring data health. But, most of this work was channeled to structured data sources in ERPs, CRMs, and data warehouses. AI increased the scope of this work as RAGs and AI agents leverage unstructured data sources and document repositories to train models and provide contextually relevant responses.
“RAG gives enterprises access to organizational knowledge, but it’s not without risks, including data privacy vulnerabilities, hallucinations, and integration challenges,” says Chris Mahl, CEO of Pryon. “Implementation requires investing in data quality, establishing governance frameworks, and creating evaluation systems before scaling. The companies getting real value from RAG aren’t just accessing information faster — they’re making better decisions by finding the right balance between innovation and safeguards.”
To address data quality gaps, CIOs should consider centralizing raw data in data lakes, providing data cleansing as a shared service, and enabling access through data fabrics and customer data platforms. As there are many data quality and management tools, developing a shared service focused on data quality is an efficient way to address the greater business need for clean AI data sources and increased scope of cleansing unstructured data sources.
