4 Reasons to Replace Your Traditional MDM with AI-Native MDM

The clock is ticking for traditional master data management (MDM) systems. As data becomes increasingly larger and more complex, companies must confront an uncomfortable truth: traditional, rules-based MDM solutions – once the backbone of data management – are no longer doing the job. Since they’re rules-based, requiring teams of humans to configure, curate, and maintain, they simply can’t scale. 

Companies who cling to legacy tools risk being left behind in an ever-evolving landscape where speed, adaptability, and innovation are required and AI is the new, competitive currency. Which is why the question is no longer whether or not organizations should replace their traditional MDM solutions with AI-native MDM, but how soon they must do so. 

The Rise and Fall of Rules-based MDM

For years, organizations have employed rules-based master data management solutions as a solution to address the integration challenge of siloed, disparate data. With the promise of delivering golden records, legacy solutions like those from Informatica and Boomi employed rules upon rules (upon even more rules!) to resolve key entities across siloed data sets. But these solutions have a fundamental flaw: they provide a cumbersome technological approach to the problem without considering whether the data they manipulate is actually good and trustworthy!  

This approach of applying rules and operationalizing the data flow at the outset rather than taking the time to properly assess the data itself, improve it, and review it with the users who know it best is fundamentally flawed. As a result, these solutions deliver a robust data repository and system of APIs that connected to the company’s BI and data visualization tools but failed to deliver what companies actually need: data they could trust. 

AI Enters the Scene

What many companies today are coming to realize is that rules and technology alone are not enough to realize the true value of their data. This is why traditional MDM has failed. Organizations need to focus on both improving their data and  operationalizing it in order to realize its full value. 

To do this, companies must embrace the entire MDM journey. And it should come as no surprise that AI is the secret to enabling companies to progress through the MDM journey. 

AI-native MDM helps companies to improve the quality of their data so they can deliver data users can trust. And it also makes it easy to maintain data integrity, even as information evolves and changes. AI-native MDM fills a gap where traditional, rules-based MDM falls short. And using AI-native data mastering, companies can take advantage of the advanced capabilities they need to finally deliver the golden records users have been demanding.

Using AI, companies can master the entities that matter most to the business, giving everyone immediate access to the best, most trustworthy data the organization has to offer. AI-native MDM gives businesses everything they need to deliver high-quality, reliable, accessible data that people across the organization can consume to overcome business challenges and make better, more informed decisions. That’s why leading companies are making the switch. 

4 Reasons to Replace Your Rules-Based MDM with AI-Native MDM

If your organization is still relying on a traditional, rules-based MDM solution like Informatica or Boomi, it’s time for a change. This is more than just a technology upgrade; it’s a reckoning that forces businesses to rethink data management strategies, consider where they are in the MDM journey, and confront legacy systems that no longer serve them. Here are four reasons why it’s time for a change. 

1. Speed & Savings

AI-native MDM solutions like Tamr deliver faster time to value (weeks or months, not years) and lower project and ongoing operational costs by up to 40% using AI-driven automation as well as lower infrastructure, licensing, and personnel costs. In contrast, rules-based MDM takes years to reconcile and create trustworthy data (if it delivers at all!). And, it incurs higher project costs because of the extensive, manual intervention and higher governance, policy, and process-driven operational costs. 

2. Accuracy

AI-native MDM delivers clean, matched trustworthy data that’s ready for consumption. Tamr’s proven, patented referential matching is unmatched in its ability to deduplicate data, reduces data curation needs by 90%, and shortens report and dashboard creation time by 80%. Traditional MDM, on the other hand, relies on manual rules and preparation, risking inconsistencies and data errors. Its manual processes and extensive data manipulations are laborious and time-consuming, impeding efficiency and accuracy and delaying the receipt of timely insights.

3. Comprehensiveness

Tamr aligns all data sources and unique data attributes plus 1-click 3rd party enrichment capabilities to take data to the next level. Proven ML models ensure comprehensive and complete high-value data that is verified against a massive master database for accuracy. Rules-based MDM requires manual development of data quality logic - there is  no out-of-the-box third-party data verification. Further, organizations need custom-built integrations to enrich their internal sources with 3rd-party data. 

4. Durability

Real-time API integrations and AI-powered search ensure accuracy and completeness of golden records over time. Using “search before create,” organizations can keep the data in their most important source systems clean - and they can do so in real time. Tamr also allows companies to connect new data sources and use AI to reconcile new data with existing golden records in real time. Traditional MDM delivers search capabilities that struggle with the complexity of multi-system, multi-domain entity identification, requiring  manual updates, limiting adaptability to changing data.

Now we know you may be thinking, “isn’t replacing a legacy system daunting and scary?” In some cases, yes, but with Tamr, it’s not. Tamr makes it easy to get up and running quickly with AI-native MDM. Because Tamr is purpose-built for data mastering, it eliminates the need to write custom code and create bespoke integrations by delivering pre-trained machine learning models that automatically clean, deduplicate, and unify data from multiple sources. Tamr also removes the need to stitch together disparate parts of a monolithic platform. As a result, you can accelerate your time to achieve production-ready data, retire your legacy systems, and focus on what matters most—extracting value from your data. 

By now it should be clear that AI-native MDM overcomes the limits of rigid, rules-based MDM solutions. The time to take action and replace your solution is now. Download our ebook, Golden Records 2.0: The AI-native MDM Advantage, to discover how Tamr can help you embark on a successful MDM journey.