Leveraging GenAI for Better Data Management

Data fabrics already leverage cloud-native principles, offering scalability, elasticity, and automated provisioning, allowing faster development and deployment of data products. Now these platforms are being infused with AI to further supercharge development of data products.

Foundation models, like LLMs, have a far superior and sophisticated ability to automate data discovery and classification. They can scan diverse data sources within the fabric, automatically identifying and classifying data types, entities, and relationships. This improves data understanding and facilitates faster data product development.

Identify, Rectify, and Enhance Data Quality

Gen AI can identify and rectify inconsistencies and missing values in data, improving its quality and completeness. Additionally, it can generate synthetic data to enrich existing datasets for training ML models or testing purposes.

Gen AI can automatically generate relevant features from raw data, reducing manual effort and accelerating data preparation for analytics. It can be designed to explain their reasoning and decision-making, enhancing the transparency and trust in data-driven outcomes.

Visualization of Sorting data

Unlocking Enhanced Data Governance and Security

However, AI-infused data fabric goes beyond helping with development of data products. It can also enhance data governance and security. For example, it can analyze data access patterns and usage within the fabric, detecting unusual activity or potential security threats. This enables proactive measures to ensure data security and compliance. By analyzing user profiles and data sensitivity levels, access control rules can be dynamically adjusted in real-time based on predefined policies.

Finally, AI algorithms continuously analyze fabric performance and resource utilization, suggesting optimizations and automating adjustments for improved efficiency and scalability. This intelligent and automated data fabric management can lead to significant cost savings.

To summarize, CDOs and CDAOs can demonstrate real value and ROI from their AI-inspired data fabric while building trust with the data consumers.

Next Use Case

Data Governance to Meet Regulatory Requirements