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Tamr Insights
Tamr Insights
AI-native MDM
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Updated
January 21, 2025
| Published

What’s In and What’s Out for MDM in 2025: Tamr’s Perspective

Tamr Insights
Tamr Insights
AI-native MDM
What’s In and What’s Out for MDM in 2025: Tamr’s Perspective

Data moves fast. What’s insightful today could be irrelevant tomorrow. To stay ahead, you need data you can trust. And that means staying on top of what’s shaping the future — and what’s becoming obsolete. That’s why we hopped on the social media trend bandwagon and created our What’s In and What’s Out in 2025, MDM Edition. From the groundbreaking use of AI to once-popular tools now fading into the sunset, this list will keep you in the know and ahead of the curve. 

In

Out

AI-native AI features
Following the MDM journey Operationalizing data before improving it
Golden records Data consolidation
Real-time APIs Batch process data mastering
Event-based integration ETL/ELT
Source system corrections Right click and ignore
Organizing data by entity Organizing data by source
User feedback on the data Data team spot-checking the data
360-degree view of the data System-specific views of the data

AI-Native vs. AI Features

AI-native solutions are built with AI at their core from the beginning, enabling enormous efficiency and scalability benefits.

AI features are bolt-on capabilities like a chatbot that sit on top of traditional technology solutions. 

Following the MDM Journey vs. Operationalizing Data Before Improving It

Following the MDM Journey means assessing the data, improving it, reviewing it with users, and then operationalizing it through connections with essential business systems. 

Operationalizing data before improving it is a mistake. Starting with technology and immediately integrating it doesn’t solve the business problems and exacerbates data quality issues.

Golden Records vs. Data Consolidation

Golden records give users a single, authoritative, accurate version of a business entity (company, supplier, consumer, etc.) across multiple data sources and datasets. 

Data consolidation is about combining data from different sources into a single system (like a data warehouse) but not being concerned about data mastering, resulting in multiple, often inconsistent versions of a business entity.

Real-time APIs vs. Batch Process Data Mastering

Real-time APIs enable continuous, immediate communication between systems to prevent duplicate data from entering your systems while it's still in motion.

Batch process data mastering is fine when time is not of the essence, but doesn’t work when business users need up-to-the-moment data accuracy and completeness.

Event-based integration vs. ETL/ELT

Event-based integration operates in real time, enabling systems and applications to immediately process changes in the data.

ETL/ELT operates in batch mode, processing changes in the data at scheduled intervals. 

Source System Corrections vs. Right Click and Ignore

Source system corrections is about enabling users to engage in the data mastering process, identify issues with data quality and accuracy, and resolve them in real time across systems. 

Right click and ignore are the steps users often take when reviewing golden records, letting the underlying data remain inaccurate or incomplete. 

Organizing Data by Entity vs. Organizing Data by Source

Organizing data by entity leads to the ultimate goal: a golden record

Organizing data by source keeps data trapped in disparate systems across the organization, making it impossible to gain a holistic view of business entities.

User Feedback on the Data vs. Data Team Spot-Checking the Data

User feedback on the data builds trust by ensuring that data aligns with with real-world needs. 

Data team spot-checking the data is the typical path organizations take, resulting in missed opportunities and underutilized insights because of lack of business knowledge. 

360-degree View of the Data vs. System-specific Views of the Data

360-degree view of the data seamlessly integrates data from multiple sources to provide a clear, holistic view of key entities.

System-specific views of the data exacerbate data silos, preventing the business from obtaining a single source of truth.

For more than a decade, Tamr has focused on using AI/ML to tackle the hard problem of performing accurate, real-time data mastering and creating golden records at scale. Our unique blend of advanced AI/ML and human feedback makes improving the quality of your data easier than ever before. With Tamr by your side, you can tackle your messy data in a manageable way, progressing through each phase of the MDM journey at the pace that is right for your business. 

It’s time to abandon traditional MDM and embrace AI-native MDM. Start your MDM journey today.

Get a free, no-obligation 30-minute demo of Tamr.

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