AI represents a fundamental shift in how data is leveraged for competitive advantage. Yet, many enterprises are struggling with fragmented systems, siloed data, inconsistent governance and aging infrastructure.
To unlock AI's potential, organizations must reimagine how data is captured, connected and consumed across the business. That means building a foundation where data is clean, contextual and continuously available for machine learning, analytics, and automation.
DBTA recently held a roundtable webinar, From Legacy to Leading: Building an AI-Driven Data Strategy, to help data and technology leaders navigate what it takes to build an AI-driven data strategy fit for the future.
According to Sriraj Rajaram, principal product manager, Reltio, the “Age of Intelligence” is being built on data strategies and digital infrastructure that unlock autonomous decision-making by humans and machines at unprecedented scale and speed.
However, siloed data is the kryptonite of AI strategies, he said. Poor data quality erodes trust. Data debt increases with aging technology and agentic apps. And complex landscapes increase operational friction.
He introduced 3 rules to building an AI-drive data strategy. This includes:
Build data trust as the foundation for autonomous systems: Trusted, high-quality data enables AI systems to act autonomously while maintaining customer and stakeholder confidence.
Architect data like a neural network: Design your data as interconnected networks, not isolated silos, so every data point informs decisions and ops instantly across your enterprise.
Compete on data velocity and data volume: Winning depends on converting data into actionable insight faster than your competitors. Real-time data flows power smarter, faster decisions.
AI is complex and companies are overwhelmed, said Phillip Miller, product marketing director, Progress. Customers want trust, governance, and simplicity.
“We all win when we make AI practical, governed, and outcome-driven,” said Miller.
The Progress Data platform provides AI-ready data. Create on-brand, compliant content (docs, articles, proposals, training) grounded in approved, cited sources and your style guides. Turn scattered content into a governed, contextual knowledgebase. Teams get grounded answers with citations from approved sources, at scale, and within permissions. Synthesize internal and external data to uncover evidence-backed insights. RAG enforces approved sources, policies, and human handoffs when confidence is high, so findings are dependable. Offer natural-language access to trusted, brand safe answers grounded in your content and policies. Responses respect roles and data sensitivity, with clear citations and an audit trail. Developer help grounded in your code, standards, and docs, permission aware suggestions with policy guard rails, citations, and optional human review.
The platform for AI success consists of people, orchestration, and governance, according to Catalina Herrera, field CDO, Dataiku.
Dataiku offers AI that delivers accelerated growth, improved efficiency, and greater resilience.
According to Robert Stanley, senior director of special projects at Melissa Informatics, Melissa Informatics provides software and services for AI-enabled data quality, data discovery, harmonization, integration, and research.
Melissa Unison is “Melissa’s well-trained application expert.” Unison accesses and applies application functions, business rules, and reference datasets.
For the full webinar, featuring a more in-depth discussion, Q&A, and more, you can view an archived version of the webinar here.