Modern analytics and AI depend on more than powerful models; they depend on modern data platforms.
As organizations race to support AI workloads, real-time analytics, and scalable self-service, legacy architectures are quickly becoming a bottleneck. Data platforms must now be faster, more flexible, and built to support analytics and AI side by side, serving as the foundation for innovation, performance, and scale.
DBTA brought together a roundtable of industry experts for recent webinar, Modern Data Platforms: Powering the Next Wave of Analytics and AI, to explore how modern data platforms are reshaping the way organizations store, manage, and activate data.
According to Evelyn Chou, senior product manager at insightsoftware, customers are demanding every product be “smart,” conversations become the new interface, and AI that is trustworthy and doesn’t hallucinate.
Teams are being asked to deliver more without giving up control. Product teams are being asked to build customer-facing analytics, AI, and reporting directly into their products. Data and AI teams are being asked to deliver governed, contextual enterprise data for trusted analytics and AI. And operations/compliance teams are being asked to produce precise, audit-ready reports and documents at scale, Chou said.
insightsoftware can help builders embed trusted intelligence into every product, she recommended. insightsoftware products are built for teams solving the hardest data problems.
A data foundation that enables AI cannot just be “good enough,” said Irem Radzik,
senior director of product marketing at Reltio. The platform must consist of:
- Unified, unique records with rich info on customers, products, and locations including their interactions and unstructured data.
- Real-life context via entity relationship views and metadata in a knowledge graph.
- Provide continuous data quality management, security, consent tracking, auditability, and data lineage for responsible AI.
- Be always up-to-date, and consumable by AI agents, systems, and humans using APIs and open protocols.
Reltio can harmonize, unify, and govern core data in real time, Radzik noted. Reltio offers an Intelligent Graph that links unified entities, relationships, and meaning. It delivers continuous governance with AI agents. Reltio AgentFlow Prebuilt Agents reduce data stewardship burden.
Simon Swan director, product marketing at Qlik, said legacy architectures are becoming a bottleneck. Pipelines aren't built for AI speed. Manual engineering can't scale. Governance fails at autonomous scale. Data quality is the #1 AI trust blocker. And data and AI teams are drifting apart.
To adapt to the agentic AI shift, Swan recommended agentic data engineering—the practice of both using and building agentic AI capabilities to deliver trusted data for AI workloads.
There are 3 principles of agentic data engineering, including:
- Intent-driven engineering: Build and deploy through natural language and coding agents
- Autonomous Operations: Self-healing pipelines, root cause analysis, and cost optimization
- AI-ready data: Real-time, data quality, trust, and governance for AI consumption
Lauren O'Connor, vice president of product marketing at Strategy, asked “Can your AI answer the same question the same way, every time, regardless of which tool, which user, or which model asks? If the answer is yes, you have context. If it’s uncertain you have a gap. And that gap will compound as AI usage scales.”
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.