Why AI-native Master Data Management Outperforms Traditional, Rules-Based Solutions

Editor’s Note: This post was originally published in March 2024. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

A customer 360-degree view is the gold standard for successful companies, offering a single source of truth that enables them to elevate customer experiences, uncover new revenue opportunities, and deliver personalized support. However, despite investing years of time and millions of dollars in traditional master data management (MDM) technology aimed at unifying their customer data, many businesses fail to achieve this full-circle view. The reason? Their data is and remains fragmented, and they’re not leveraging AI effectively.

The process of unifying data across multiple data sources and datasets and finding the best, most accurate details from that data to create a single, authoritative, accurate version of the customer, otherwise known as a golden record, is hard work. It requires organizations to embrace new technologies and new approaches to data management that make accurate, comprehensive, and durable data accessible to every decision-maker across the organization. 

Having access to these golden records is crucial for identifying new business opportunities, managing risks, and improving operational efficiency—benefits that collectively drive business value and lead to astounding results. Too often, however, businesses are relying on outdated, rules-based MDM solutions to solve the challenge of fragmented data. And to be frank, these traditional MDM solutions simply can’t keep up with today’s dynamic data. 

Download our ebook, “Golden Records 2.0: The AI-Native MDM Advantage,” to discover why leading organizations are ditching their rules-based MDM solutions in favor of an AI-native one.

The Challenges with Rules-Based MDM Solutions

Traditional MDM solutions have been around for decades, with many companies relying on them to solve their data mastering needs. Their promise is to create golden records by using rules to drive the standardization, validation, and governance of data across systems and silos within an organization.

When first introduced, MDM solutions—and their rules-based approach to data management—were considered innovative. But today, these solutions are proving to be a liability. Companies are dedicating inordinate amounts of time and large numbers of resources to writing, modifying, and maintaining rules. Because when your data changes, the rules in your MDM solution break. And fixing these rules requires significant amounts of time and manual human effort, making it difficult for companies to scale as their data evolves and changes (which it will). As a result, companies using traditional MDM solutions are unable to realize the promise of the golden record. 

Adding insult to injury, MDM implementations are costly, time-consuming, and complex, relying on teams of professionals to ensure project success. And because they have limited flexibility and scalability, MDM solutions often become silos themselves, exacerbating the very issue they are trying to solve. 

AI-native MDM: A New Paradigm for Customer 360

Data-savvy organizations know that traditional, rules-based MDM solutions will not give them the competitive edge they need to thrive in today’s dynamic business environment. That’s why they are making the pivot to modernize their data management systems by embracing AI-native MDM. 

AI-native MDM is the fastest and most effective way to give every decision-maker access to the accurate, comprehensive, and durable data they need to identify new business opportunities, manage risks, and improve operational efficiency—benefits that collectively drive business value and lead to astounding results. 

Using AI in data management, companies can reap the value and benefits that rules-based MDM solutions simply can’t deliver:

• Great results out of the box: By combining embedded similarity with human feedback, AI delivers best-in-class match rates with external data, which ensures the data is accurate and reliable. 
• Tailored to the consumer: AI ensures that customer interactions are informed and relevant by creating a personalized, single view of each customer and validating it with human insight.
• More use = increased effectiveness: Machine-generated feedback helps AI-native solutions learn and improve over time, which, in turn, ensures that the system keeps pace as business needs and data landscapes evolve.

While AI can handle a lot of the heavy lifting, it's not infallible. Algorithms might struggle with data that is exceptionally noisy, ambiguous, or complex. That’s why human-refinement is critical. 

Humans play a valuable role in ensuring the quality and trustworthiness of golden records. They can apply judgment and domain expertise that correct errors, make calls on ambiguous cases, and provide additional context the AI might not have considered. 

AI-native MDM solutions combine the best of both worlds: AI’s efficiency and scalability with human intuition and expertise to ensure the highest level of accuracy and reliability.

Creating Customer Golden Records at Toyota Motors Europe

The Challenge: Toyota Motors Europe (TME) launched an initiative to grow business by putting the customer closer to the center of their activities. But with 30 national marketing and sales companies (NMSCs) operating across 50 countries, each with their own source systems and approaches to integration, it was challenging for TME to gain a consolidated view of customers across NMSCs. In addition, customer data varied in quality and remained trapped in silos, making it difficult for TME to innovate, collaborate, and scale. 

The Solution: Using Tamr’s unique combination of AI and human intelligence, TME consolidated data across silos, making it easier to add new sources as their data grew. Tamr also enabled TME to clean, standardize, and track changes and improvements to data—eliminating errors and increasing reliability. Finally, using human refinement, TME was able to validate results and capture input, which improved overall trust and accuracy. 

The Result: TME gained a unified view of its customers, providing the scalability, flexibility, and collaboration they needed to deliver exceptional customer experiences. Tamr also enabled TME to optimize upsell opportunities and reduce duplicate customer records by 40%, which improved overall marketing and sales efficiency and effectiveness.

For over 10 years, Tamr has focused on using AI in master data management to help customers like Toyota Motors Europe, Old Mutual, and Santander tackle the hard problem of delivering trustworthy customer golden records at scale. And we’re ready to help you, too.

Leading organizations are ditching their rules-based MDM solutions in favor of an AI-native one like Tamr. Download our ebook, “Golden Records 2.0: The AI-Native MDM Advantage,” to learn more.