Generative AI is not just a general-purpose productivity aid that surfaces information the way a search engine does; with gen AI, organizations can combine their unique, proprietary data with foundation models that have been pre-trained on a broad base of public data.
Trained on a combination of public and proprietary data, generative AI may become the most knowledgeable entity within an organization, opening up innumerable opportunities for innovation.
As with all analytics, generative AI is only as good as its data.
To fully leverage AI, an organization needs mastery over its proprietary data.
This means a solid foundation of data operations technologies and organizational norms that facilitate responsible and effective use of data.
The ability to move and integrate data from databases, applications, and other sources in an automated, reliable, cost-effective, and secure manner.
Knowing, protecting, and accessing data through data governance.
This kind of data readiness is perennially overlooked and has historically derailed many attempts to leverage the power of big data and data science.
One metric suggests that as many as 87 percent of data science projects never make it to production, often because of siloed and ungoverned data as well as underdeveloped data infrastructure.
Generative AI Depends on a Foundation of Data Maturity Without data maturity, the prototyping, deployment, and testing of generative AI - or indeed, any kind of analytics - becomes extremely difficult.
A central, cloud-based data repository that can serve as a single source of truth.
Ability to block and hash sensitive data before it arrives in a central repository.
Good visibility into your data, as exemplified by cataloging of data assets.
Your Data Platform Architecture for Generative AI Building generative AI from scratch is a colossal undertaking, with the potential to cost hundreds of millions of dollars and the equivalent of hundreds of years.
Your organization is most likely to use a base or foundation model - a commercially available model already trained on huge volumes of public data.
In the initial stages, this architecture mirrors basic analytics use cases, requiring a data pipeline to extract, load, and transform raw data into models for supporting reports, dashboards, and other data assets.
Convert text into enumerations, store in a vector database for generative AI to integrate into long-term memory, enhancing results from initial training and unique organizational data.
Even with the help of an increasing number of off-the-shelf tools for managing data infrastructure with generative AI, it is likely that you will need to lean heavily on engineering, data science, and AI expertise to make the parts function properly with each other and build usable applications on top of the architecture.
The potential of generative AI can only be fully realized when organizations recognize the pivotal role of their proprietary data.
By prioritizing mastery over data through the implementation of advanced data operations technologies and cultivating a culture of responsible data use, organizations can unlock the true power of generative AI, ensuring its optimal performance and ethical deployment in a rapidly advancing technological landscape.
This Cyber News was published on feeds.dzone.com. Publication date: Fri, 19 Jan 2024 21:13:04 +0000