Summary
- Shows how AI transforms metadata into an active business asset.
- Automates data description, tagging, and discovery.
- Enables frictionless, enterprise-wide data governance.
- Improves data discoverability for AI-ready analytics.
Chapters
The purpose of data and analytics doesn't change with AI. What changes is how we perform it with AI. For the first time, we are capable of performing data and analytics governance without friction at scale for the entire enterprise.
That is why Actian is taking steps to radically change how data and analytics governance is performed.
AI turns metadata from something we document into something we actively use to automate governance decisions across the enterprise. Unlike data engineering and data science, data and analytics governance have always suffered from lack of strategic buy-in and lack of resources. The good intentions of getting data properly managed and set up in an effective architecture disappeared in the mundane tasks of describing, maintaining, tagging, searching for, and sharing data.
And these tasks have always lacked hands on deck and proper coordination. Everyone agreed it should be done.
No one had time to really do it. At Actian, we want to change this. We see that AI can redefine how we perform data and analytics governance.
With the Actian Data Intelligence Platform, employees can scale their potential with AI and let agents complete the tasks they need to perform, but lack the time to perform. It's a perfect example of how AI should work, not replacing humans, but expanding their potential. Data is being tagged, described, and logically placed in domains by agents.
Human activity is overseeing that this is done with precision, carefully adjusting the many actions of agents and improving the agents' activity over time. You can't govern what you can't search and find. With AI, the Actian Data Intelligence Platform is enabling end users to search for data without limits and also in data.
Accessing data, observing data, and using data is also changing. Alert fatigue is eliminated as agents take over the task of fixing data engineering bugs in pipelines. Long queue to accessing data can be speeded up.
Policies for operational, analytical, and experimental data can be set, enforced, and executed.
AI is not only a strong component to succeed with data and analytics governance, AI is also a consumer of the decisions you make for your enterprise data. Data for AI is not entirely the same as data for analytics.
AI agents need data that does not make them hallucinate data with context, which is exactly what you can deliver because data and analytics governance also can be scaled in the age of AI. In Actian, we deliver AI to let you change and improve data and analytics governance. Not what data and analytics governance is, but how it is performed.
