Central to delivering on the AI promise is clean, reliable, scalable data pipelines, which require a robust, AI-ready data foundation consisting of the right mixture of architecture, governance, and tools. What’s clear is that while AI itself is rife with complexities, becoming ready for that AI implementation places dramatic pressure on IT and data teams.
DBTA’s webinar, Is Your Data Ready for AI? How to Build a Solid Foundation, highlighted perspectives from industry experts about proven best practices, modern architecture strategies, and new technologies that prepare organizations for true AI success.
According to Sumit Pal, strategic technology director, Graphwise, while every enterprise is eager to adopt AI, they are "encountering strong forces that are pulling them down from realizing their AI vision.” These forces—consisting of challenges such as hallucinations, data quality issues, and a lack of trust and explainability—are ultimately preventing successful AI deployment.
Graphwise brings confidence to AI, addressing the core data quality and data management issues with contextualization, semantics, and data and AI governance. Each of these components enable contextualized intelligence, which plays a critical role in ensuring AI success.
“As the future of AI evolves from being conversational to more agentic, it’s not us humans that are going to be reading websites, webpages, and documents in the future. It's going to be the AI agents,” said Pal. “And for agents to work successfully [it] requires contextualization.”
With that in mind, the time for knowledge graphs (KGs) is now, according to Pal. Serving as the foundation that enables trustworthy AI applications, implements semantic layers, and drives better data management, KGs:
- Connect the dots for knowledge, insight, and wisdom to deliver faster insights
- Contextualize data with hierarchies, semantic meaning, and classification for better AI outcomes
- Integrate data with semantic data fabrics, data virtualization, and simplified governance for massive cost savings
Echoing Pal, Jerod Johnson, senior technology evangelist, CData Software, explained that AI is only as good as its data. “Organizations can’t just rely on the model to do all the heavy lifting,” said Johnson. “You need a solid data infrastructure to make things work well.”
Additionally, stale data products, lack of consistent compliance and trust, and siloed data prevent true AI success. For scalable, responsible deployment, AI requires:
- Live read/write access to data
- Fine-grained governance
- Scalable data movement
To help meet these needs, the CData Connectivity Platform—an industry-leading connectivity framework packaged to handle every integration use case—delivers SQL access to data for AI tools. It offers:
- Multi-point live connectivity and enterprise semantic layer drive contextual live data access
- Seamless data movement with ETL/ELT and reverse ETL, as well as EDI/MFT for B2B integration
- Pre-built, client-specific connectivity spanning a wide range of connectors
Like the previous speakers, Srujan Akula, co-founder and CEO, The Modern Data Company, creator of DataOS, examined that data readiness is entirely tied to context, transparency, and explainability. True AI readiness isn’t just clean tables, noted Akula, it’s data that:
- Is trusted and governed
- Carries semantic meaning across the enterprise
- Can trigger business action, not just analytics
“AI-ready data acts more like software—complete, reliable, and ready to use,” said Akula.
However, disparate skills, slow transformation, high costs, and tool sprawl make it increasingly difficult to become AI-ready. The missing layer, according to Akula, is an operating system for data that turns data into governed, AI-ready products. DataOS delivers this layer, a platform that:
- Unifies, governs, and activates enterprise data by working with what you already have and eliminating siloed tools and fragmented architecture
- Treats data as a product, becoming logical, single sources of truth to power all use cases and accelerating time-to-market by 90%
- Establishes semantic context for AI with business meaning embedded into the platform, not bolted on, for trusted, explainable AI
- Enables AI-readiness in as little as 6 weeks without a rip-and-replace strategy
This is only a snippet of the full Is Your Data Ready for AI? How to Build a Solid Foundation webinar. For the full webinar, featuring more detailed explanations, customer success stories, a Q&A, and more, you can view an archived version here.