Data Term Definitions

AI-Native Master Data Management (MDM)

AI-native master data management (MDM) is a modern approach to data mastering, where the architecture, methodology, and workflows are all built around AI. This approach is fundamentally distinct from traditional, rules-based MDM solutions.

How is AI-Native MDM Different From Traditional, Rules-Based MDM?

AI-native MDM uses machine learning, agentic AI, and human feedback to unify, clean, and enrich data across silos and sources to produce golden records that are accurate, complete, and continuously maintained. In contrast, traditional, rules-based MDM approaches use coding logic and manual processes to standardize and match data across systems. Rules-based MDM requires constant rule updates, centralized control, teams of people to maintain, and significant effort to scale.

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Tamr is an example of AI-native MDM. Examples of traditional, rules-based MDM include Informatica, Boomi, IBM InfoSphere, and Reltio.

‍Resources

Master Data Management Showdown: AI-Native MDM vs. Rules-Based MDM

It’s Alive! Overcome the Perils of a Franken-Monster MDM

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What AI Models Does AI-Native MDM Use?

AI-native MDM solutions embrace a fit-for-purpose approach to AI/ML models, ensuring that the right model is used for each step or aspect of the data mastering and data curation process.

ML models take on the core tasks of entity resolution, match verification, data standardization and normalization, and schema mapping.
Deep learning models deliver exceptional performance and high-quality results for real-time semantic search.
‍GenAI models fuel agentic data curation, addressing difficult edge cases with minimal human involvement.

‍Resources

A Practical Guide to AI Models and Their Role in MDM

How Agentic Data Curation is Transforming Data Mastering: Perspectives from the Tamr Co-Founders

What Are the Key Features of an AI-Native MDM Platform?

Eight common capabilities of an AI-native MDM platform are:

Entity resolution: A data management technique that identifies and matches records across data sources.
Real-time APIs: APIs that enable real-time, bi-directional data flow to ensure every record remains accurate, consistent, continuously updated, and available for immediate use in CRMs, ERPs, CDPs, and other operational systems and points of consumption.
Enterprise knowledge graphs: A connected, contextualized view of an organization’s data across multiple domains.
Agentic data curation: An innovative data management concept that brings together LLM-based AI agents and human-in-the-loop oversight to streamline data curation.
LLM connectivity: A capability that brings together clean, complete, real-time data with large language models and AI agents to provide the trusted context needed for analysis and other uses in AI applications.
Data quality: A feature that adapts to the data by flagging issues and providing suggestions for improvement based on user feedback.
Data governance: A foundational data management capability that ensures compliance and stewardship.‍
Data enrichment: A core component that involves connecting internal data with trustworthy, third-party sources to improve the quality of key business entities.

‍Resources

8 Essential Platform Capabilities a Modern AI-Native MDM Solution Needs

What Makes Tamr AI-Native?

Frequently Asked Questions About AI-Native Master Data Management

What types of data should AI-native MDM manage?

AI-native MDM manages key business entities, including B2B and B2C customers, contacts, healthcare providers, healthcare organizations, locations, products, suppliers, and more. In addition, AI-native MDM can support multi-domain data mastering that builds a connected view across multiple business entities.

What types of industries use AI-native MDM?

AI-native MDM is currently used by organizations across industries including financial services, healthcare, technology, life sciences, manufacturing, retail, and higher education.

Why do companies use AI-native MDM?

AI-native MDM enables organizations to eliminate data silos, minimize data duplication, and deliver high-quality, unified, enriched golden records in real time so everyone across the organization can confidently use and trust the data that fuels analytics, operational systems, and AI applications. As a result, organizations can improve operational efficiency, uncover new opportunities for growth, and deliver world-class customer experiences.

Related Data Terms

Agentic Data Curation

Approach to data curation where AI agents analyze, explain, and resolve data issues.

Golden Records

Unified, shared, trusted views of business entity data across systems and sources.

Entity Resolution

Data management technique that identifies and matches records across systems.

See for yourself

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