Building a real-time data mesh and streaming high quality data across the organization are keys to the generative AI kingdom
While everyone is talking about the possibilities of generative AI (GenAI), often overlooked and underestimated is the critical role that real-time data plays in getting the machine responses right.
The larger the data set used for training, the more expansive and comprehensive the responses of a Large Language Model will be.
That is why, businesses building GenAI applications need to deeply link their AI strategy to their data strategy. For organizations to thrive alongside the boom of GenAI, they have to overcome challenges that legacy ‘data-at-rest’ architectures create. How?
Cracking the real-time data strategy
Business leaders should shift their data from an ‘at rest’ state into one that is transient and reflective of up-to-date information to provide accurate services and applications in real time, creating customer experiences that are faster, more relevant, and more personalized.
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The first step for any business to reap the full benefits of AI is to build a real-time data mesh across the organization, enabling data streaming and powering AI applications to find the right and latest data sets to tap into.
Unreliable data often stems from old data integration methods that are built on slow, batch-based pipelines. Cumbersome systems mean that data is delivered late, stale, and inconsistently, and worsened by poor governance and scalability.
So, to optimize AI adoption, high quality data needs to be continuously fed into critical business systems and AI solutions. This is a data streaming approach to data management which enables the integration of real-time context at an AI query execution, while allowing experimenting, scaling and innovation with greater agility.
Most importantly, data streaming must be controlled by strong data governance and ethics policies to ensure that people safely scale, organize, and share data across the business. This provides oversight and insight into who can access, audit, and modify the large volume of data streams that are being fed to AI models.
- With an additional layer of security and accountability around how data is utilized, organizations can further improve the accuracy of GenAI tools while safeguarding data from all stakeholders. This becomes especially crucial with digital government services, many of which see more than billions of transactions a day, where reliable AI can successfully handle citizen data and prevent fraud.
In Singapore, for example, Confluent works closely with the Infocomm Media Development Authority’s Tech Acceleration Lab (TAL) to bring data streaming to the entire government, helping agencies test, develop and deploy solutions within a controlled test sandbox on the Government Commercial Cloud (GCC).
Intelligent data => intelligent AI
GenAI has moved so quickly in 2024 that businesses and governments have faced serious challenges keeping up, not only with new technology but also with reaping the business benefits from it.
As GenAI continues to empower organizations in realizing significant business gains, the tips above can ensure IT leaders get their data strategy right.
With data streaming capabilities in place, both private and public sectors will be in a stronger position to make the most of the GenAI revolution.
