Supercharging Data Fabrics With Generative AI


Data fabric has been gaining traction in the enterprise. Data fabric is a modern architecture that automates the integration of any data in real-time or near real-time from disparate sources, on-premises or in the cloud, into coherent data services that support business transactions, analytics, predictive analytics, and other workloads and patterns. Now, with the explosion of interest in generative AI and large language models (LLMs), data fabric is poised to accelerate data democratization.

Generative AI brings fast automation to data fabric

Forrester’s data fabric architecture already had AI/ML as a critical component within the six layers of the architecture: data management, data ingest, data processing, data orchestration, data discovery, and access. Generative AI and LLMs take it to the next level with automation of processes, pipelines, workflows, code generation, integration with natural language query, and enabling data intelligence through adaptive learning. Moreover, with a data fabric architecture, its modular design enables organizations to quickly take advantage of new capabilities without major infrastructure changes.

With generative AI and data fabric, organizations can:

  • Enable natural language to access data. Generative AI and LLM can help democratize data through natural language query (NLQ), offering a ChatGPT-like interface to access any data connected to the data fabric. While we see some vendors already offering limited NLQ capabilities, these features are still very early in their maturity.
  • Automate the integration of data. With data distributed across hybrid and multiple clouds, integrating data has become a top challenge. Generative AI and LLMs will simplify real-time integration through automated code generation for integration, enabling dynamic entity resolution and supporting automated data mapping and linking across silos in the data fabric.
  • Perform similarity searches using vector databases. Generative AI and LLMs can also leverage vector databases to do similarity searches based on the context connected to the data fabric. This is a game-changer, especially with the ability to support data intelligence and the semantics of untapped data assets.
  • Improve data quality in real-time. Data quality is one of the top challenges, as per recent Forrester survey data. While most organizations struggle with data quality, generative AI and data fabric can help automate the detection of anomalies, perform data cleansing, and validate data, all in real-time.
  • Secure and govern data in real-time. Most organizations struggle with data security and governance for enterprise data, especially when data is distributed. Generative AI and data fabric will help automate real-time discovery, classification, categorization, and data access based on policies.

This space is evolving quickly, and we expect vendors to start rolling out new and innovative capabilities.

The original article is here.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/solarseven


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