Ensuring that enterprise data is AI-ready is an expansive, meticulous process that requires a deep dive into current data management and architectural issues. Despite this, many employees are eager to join in on the efficiencies of large language models (LLMs)—and potentially expose proprietary information to these publicly available models.
That is why enterprises must investigate the AI-readiness of their data, regardless of their adoption journeys. In DBTA’s latest webinar, Is Your Data Ready for GenAI? Why You Must Know!, John O'Brien, principal advisor and industry analyst, Radiant Advisors, and Joshua Èrhardt, director, data and AI marketing, Informatica, examined the current state of generative AI (GenAI) as well as how organizations can effectively prepare for its inevitable implementation.
The fact that GenAI is already being used by employees—and potentially causing a litany of data risk issues—is a very real, very detrimental reality. According to a report from McKinsey, 91% of respondents use GenAI for work, where another study highlighted that 78% of employees do so without clearance from their employers.
At its heart, this unauthorized use of GenAI is a data management issue, as LLMs are “really not valuable [or] offer a competitive difference until you tie it to your own data,” said Èrhardt. For GenAI to provide tangible value, robust data management is required, as any model—regardless of the boons it promises—relies on good data.
To implement GenAI safely, enterprises must integrate these solutions directly, reducing the need for employees to upload data on their own, according to Èrhardt. Informatica’s Intelligent Data Management Cloud (IDMC), for example, enables enterprises to safely connect their proprietary data to publicly available LLMs without uploading data to an anonymous cloud.
O'Brien emphasized that, in terms of architectural readiness, he doesn’t want enterprises “to go out and build a second home for GenAI. I want you to take everything you’ve got with data engineering and data management and extend that.”
Architecture aside, how do you truly know if your data is AI ready? Within this extension of existing systems, Èrhardt explained that AI-ready data is:
- Accurate, transparent, and contextual
- Governed, secure, and democratized
- Complete, resilient, enterprise-scale, and consistent
And while data must be ready for GenAI, AI-ready data also needs GenAI to further ensure these systems’ success. GenAI-powered data management helps with scale, particularly by:
- Easing data users’ interaction with data using natural language queries
- Leveraging AI-generated data management insights for actionable recommendations
- Automating routine data management tasks via GenAI-powered data management
Informatica’s IDMC, Èrhardt explained, is the GenAI-powered platform for AI-ready data, offering a comprehensive set of data management services paired with CLAIRE, Informatica’s AI-powered data management copilot. From data cataloging to data integration and engineering, data quality and observability, natural language-powered data management, and more, IDMC enables organizations to save time and money with one platform while enabling enterprise-scale AI.
This is only a snippet of the Is Your Data Ready for GenAI? Why You Must Know! webinar. To view the full webinar, featuring detailed explanations, real-world use cases, a Q&A, and more, you can view an archived version here.