Breaking the Rules with AI-Native MDM: Webinar Highlights

Modern businesses are filled with data silos that contain vast amounts of records, many of which are incorrect, inaccurate, or duplicative. And while integrating data silos delivers immense value to the organization, the process to do so is tedious and time-consuming, requiring teams of people, multiple tools, and, for many companies, writing rules upon rules. Worse yet, these processes can’t scale, making them completely ineffective as the volume and variety of data continue to grow. 

However, there is a better, more effective way to break down data silos and deliver the trustworthy golden records needed to propel the business forward. AI-native master data management (MDM) has proven to be the most effective way to master siloed data. 

In our most recent webinar, “The AI-Native MDM Advantage: Why It’s Time to Replace Your Rules-Based MDM,” learn from Tamr co-founder, Turing Award winner, and MIT professor Michael Stonebraker about why AI and machine learning are critical for scalable and efficient data mastering—as well as why other methods like rules-based MDM solutions fail. 

In this 1-minute clip below, Michael shares why rules-based MDM doesn’t scale—with teams relying on it feeling like they’re “fall[ing] into deep quicksand.”

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Here are a few key takeaways from the webinar.

The Process to Integrate Data Silos

Integrating data silos across an organization involves a significant amount of effort. The process involves:

• Moving data to a common place such as a data warehouse, a data lake, or a data lakehouse
• Transforming data so it reflects common units and meaning
• Performing schema integration to align columns that have the same meaning
• Cleaning data to ensure it is accurate, complete, and up-to-date
• Enriching data with trustworthy third-party sources
• Consolidating entities to eliminate duplicates
• Identifying golden records for key business entities
• Classifying data based on common attributes
• Conducting ongoing stewardship to preserve data quality

Historically, businesses tackled these tasks using batch processes, rules-based MDM solutions, and brute force. But these approaches have a fundamental flaw: They can’t scale. And with the demand for real-time data unification growing, organizations need to rethink their approach to entity resolution and data integration across disparate business silos. 

Case in Point: Mastering Data at Danaher with AI-Native MDM

Danaher, a leading global life sciences and diagnostics innovator, wanted to improve their cross-sell and upsell capabilities. They sell lab supplies, but they sell them through 20 different operating units, each with their own brand and system or systems. To service their customers more efficiently, Danaher needed to unify more than 17 internal and external data sources. And they knew that a traditional, rules-based approach wasn’t up to the task. Instead, Danaher employed AI-native MDM.

Using Snowflake and Tamr’s AI-native MDM solution, Danaher brought together 17 sources over the course of six months, enabling the organization to achieve the following results:

• Reduced duplication rate by 50% (from 10 million down to 5 million mastered contact entities)
• Increased completeness across ~50 attributes by 9%, from 54% complete to 63% complete
• Increased overall data quality from 65% to 85%

Tamr’s AI-Native Approach to Mastering Data at Scale and in Real Time

To meet modern demands for real-time, trustworthy, mastered data, organizations must abandon their legacy, rules-based approaches in favor of modern, AI-native solutions. 

Tamr’s AI-native MDM enables organizations to normalize, verify, match, cluster, and enrich data in real time, allowing organizations to quickly resolve entities and deliver the trustworthy golden records business need to drive three powerful use cases:

• Analytical: Improve the quality of the data used in analytics, dashboards, and reports
• Consumption: Make data accessible and consumable via 360-degree pages
• Operational: Use APIs to make data available within operational processes

By mapping to the conformed schema, AI-native MDM cleans and normalizes the data, enriches it with trustworthy third-party sources, and creates golden records that represent the best version of the data, while keeping humans in-the-loop to provide critical feedback.

Further, using interfaces such as Model Context Protocol (MCP), AI-native MDM can make the golden records available for use in large language models (LLMs), adding context to the conversation. 

Ready to learn more? Watch our webinar, “The AI-Native MDM Advantage: Why It’s Time to Replace Your Rules-Based MDM,” to discover how Tamr can help you deliver the complete, trustworthy data your business needs in real time.