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 democratisation.
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 (GenAI) and LLMs take it to the next level with the 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 organisations to take advantage of new capabilities quickly and without major infrastructure changes.
With generative AI and data fabric, organisations can:
Enable natural language to access data. Generative AI and LLM can help democratise 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 that’s 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 organisations 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 organisations struggle with data security and governance for enterprise data, especially when data is distributed. Generative AI and data fabric will help automate discovery, classification, categorisation, and data access based on policies in real-time.
First published on Forrester Blog
