Master Data Management Showdown:
AI-Native MDM vs. Rules-Based MDM
Not sure which solution is right for you? Let’s look at head-to-head comparisons.
AI-native master data management (MDM) is a modern approach that uses machine learning and human feedback to unify, clean, and enrich data across silos and sources — producing golden records that are accurate, complete, and continuously maintained. Tamr provides the industry-leading AI-native MDM platform.
A rules-based approach to MDM uses coding logic and manual processes to standardize and match data across systems. It requires constant rule updates, centralized control, teams of people to maintain, and significant effort to scale. Examples: Informatica, Boomi, IBM Infosphere, Reltio.
Speed & Costs Comparison
AI-Native MDM
Rules-Based MDM
Time-to-value
Fast outcomes — Time-to-value achieved in days or weeks
Slow returns — Takes months to years to reconcile and create trustworthy data
Upfront costs
30% cheaper with AI-driven automation
High costs due to extensive manual intervention and specialized teams required
Ongoing maintenance
Pre-trained AI models reduce the need for constant updates
Rules-based systems require ongoing tuning and manual rule-writing
Operational costs
Lower infrastructure, licensing, and personnel costs
Higher governance, policy, and process-driven operational costs
AI-Native MDM in action
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Accuracy Comparison
AI-Native MDM
Rules-Based MDM
Deduplication and matching
Proven, patented referential matching delivers unmatched deduplication results and better entity resolution
Manual rules and preparation risk inconsistencies and data errors; rules-based logic struggles with ambiguous data
Automation and efficiency
AI-driven automation reduces data curation needs by 90%, boosting accuracy
High dependency on manual intervention and processes that are laborious, time-consuming, and error-prone
Trustworthy insights
Golden records reduce report and dashboard creation time by 80% or more and build stakeholder trust
Extensive manual data manipulation means less timely and less justifiable insights
Measurable progress
Move beyond basic metrics like fill rates to better understand the state of your data and track its improvement over time
Lack of visibility into how data evolves makes it impossible to reliably measure the data quality progress
AI-Native MDM in action
Comprehensiveness Comparison
AI-Native MDM
Rules-Based MDM
Data quality and completeness
Unifies data across systems and silos; proven machine learning models ensure comprehensive and complete high-value data
Requires manual development of data-quality logic; gaps and inconsistencies persist
Verified match
Your data, refined with AI and verified against a massive master database for accuracy, improves trust and outcomes
No out-of-the-box, third-party data verification
Third-party enrichment
One-click, third-party enrichment enhances data and adds context
Often requires custom development or data reformatting to use external sources
Scalability across domains
Purpose-built data products with domain-specific schema speed up data onboarding and curation
Built for static data — struggles to scale across business units or regions
AI-Native MDM in action
Durability Comparison
AI-Native MDM
Rules-Based MDM
AI-powered search
Keeps data clean and prevents duplicate records with intelligent "search before create" and entity resolution capabilities
Traditional search struggles with multi-system, multi-domain entity identification
Onboarding of new data sources
Connects and reconciles new data sources in hours using AI-driven automation
Requires manual updates — limiting adaptability to changing data
Real-time APIs
Resolves entities while the data is still in motion and instantly delivers the best data to operational systems
Monolithic platforms require complex efforts to maintain data accuracy
Data governance and stewardship
Empowers data teams with intuitive tools for ongoing data curation and governance
Heavy reliance on manual processes increases the risk of errors and delays
AI-Native MDM in action
The choice is clear: When it comes to mastering data to deliver business value, AI-native MDM wins
Overcome the limits of rigid, rules-based solutions, and gain the flexibility to adapt to the needs of your business. With decentralized governance — along with an intuitive interface and seamless integration — put the management and control of data into the hands of the people who need it to drive business growth, even as your data changes.
Get golden records fast
Learn more about the benefits of ditching legacy, rules-based MDM solutions in favor of AI-native MDM in our "Golden Records 2.0" ebook.