How MDM and AI are Evolving Together

Introduction:

In this episode, we explore how Master Data Management (MDM) and AI are no longer separate conversations. From rigid “golden records” to adaptive, AI-assisted data ecosystems, modern organizations are rethinking how data governance, intelligence, and scalability intersect.

Host Jane Urban, CDAO at Improzo, sits down with Stephen Gatchell, Partner & Head of Data Strategy at Ortecha, to unpack how enterprises can move beyond static data frameworks toward dynamic, AI-enabled data philosophies built for complexity.

Key Highlights

• MDM is no longer about a single “system of truth.”
  As data volumes, sources, and use cases expand, organizations increasingly operate with multiple systems of truth, each shaped by context and purpose rather than a single golden record.
• Manual MDM processes don’t scale in an AI-driven world.
  Spreadsheet-based mastering and human-only reconciliation can’t keep up with thousands of critical data elements. Automation is no longer optional — it’s foundational.
• AI enables business context, not just technical metadata.
  Modern AI can connect technical metadata with business meaning: identifying duplicates, synonyms, translations, and missing definitions — and finally making data catalogs usable.
• Observability matters as much as accuracy.
  Knowing who uses which data, how often, and for what helps teams prioritize what truly needs to be mastered — and what can be deprioritized.
• Self-documenting systems are becoming the norm.
  AI-assisted documentation allows systems to generate and maintain context automatically, shifting humans into a validation and governance role rather than manual upkeep.
• Small Language Models outperform general-purpose LLMs for enterprise use cases.
  Domain-trained, task-specific models reduce hallucinations, lower cost, and deliver better results than broad internet-trained models when applied to data management and business workflows.
• Agentic AI works best when it’s modular.
  Instead of “uber agents” that try to do everything, organizations are seeing success with small, specialized agents orchestrated together — mirroring how real teams operate.
• Human + AI is where the real value emerges.
  The most powerful outcomes come when experienced practitioners use AI to challenge assumptions, surface blind spots, and explore alternatives — not replace judgment.
• Entry-level work is changing — fast.
  AI is already absorbing repetitive analytical tasks, forcing organizations to rethink training, career ladders, and how junior talent gains experience.
• The most future-proof skills remain human.
  Critical thinking, communication, change management, and contextual judgment are increasingly valuable — and resistant to automation.
• The best way to start with AI is simple and low-risk.
  Meeting summaries, document reviews, persona-based feedback, and communication clarity are practical entry points that deliver immediate value without heavy technical lift.
• AI is ultimately a clarity multiplier.
  Clear communication reduces follow-up meetings, rework, and confusion — freeing time for deeper thinking and higher-impact work.

Final takeaway:

AI doesn’t replace data strategy — it forces it to mature. Organizations that treat MDM and AI as evolving, context-aware systems (rather than rigid rules) will scale faster, adapt better, and make smarter decisions.