As businesses increasingly rely on a combination of data, files, content, analytics, and AI-generated output, KPMG LLP has observed chief data officers shifting their focus from structured to unstructured data management. They are grappling with oversight of vast data sets, some of which contain personally identifiable information, reflecting the challenge of managing data governance while balancing regulatory and ethical considerations. At the same time, their technology teams, already tasked with managing the increase in automation and AI requests, find themselves scrambling to address challenges with transparency, explainability, drift, bias, hallucination, and access control.
Current State
Turning from structured to unstructured data management
Financial reporting system
Relational database collects data
Old-style governance is the roadblock preventing the seamless integration of diverse and expanding data sets. It also struggles with the dynamic and iterative nature of AI development cycles, where continuous data flows and feedback loops are crucial.
Today’s forward-looking data leaders are evolving their governance models to accommodate systems that are increasingly real-time, flexible, and intelligent and pulling from diverse sources and processing information continuously using modular pipelines. They are preparing for a future where data will flow more freely through APIs and AI will be integrated with enterprise applications using Model Context Protocol.
batch processing validates data
Consolidates into a financial statement
