Two major trends, the proven business value of analytics and the positive productivity impact of AI, drive organizations to collect, manage, and use more data. At the same time, the challenges of governing that data have only increased. Companies must comply with a growing patchwork of privacy laws and increasing cybersecurity risks while also facing increasing demands for fast data access and unprecedented data volumes.
These challenges are hard to meet with traditional data governance solutions that were built for the era of human-driven business intelligence and not the AI-driven era of human-machine collaboration. Most notable is the fact that traditional solutions focus on the data governance applications focus only on the discovery of structured data. While that may be sufficient for analytics use cases, it is not sufficient for AI, which rely heavily on unstructured data sources.