Long live MDM
Long live MDM
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January 15, 2025
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Read time
Master Data Management (MDM) has long been the cornerstone of data governance strategies, ensuring a “single source of truth” for critical business entities such as customers, products, and suppliers. MDM promised accuracy, consistency, and governance across organizations, leading to heavy investments in time and money. After several failed attempts [14] to implement one repository to “rule them all”, a growing tide of opinion now suggests that traditional MDM are on the verge of obsolescence.
In this article, we explore why MDM is considered “dead” or in decline, what is driving this seismic shift, and present the emerging techniques and technologies to replace MDM in the coming years.
MDM emerged as a solution to fragmented data silos that challenged enterprise systems back in the late 1990s and early 2000s. It aimed to achieve consistency, accuracy, unified view of core business entities, promoting data quality and governance. A 2016 study by Smith & Wang in the Journal of Data & Information Quality (JDIQ) [1] found that companies using MDM reported a 25% of cost decrease related to data quality issues. Another study [6] reported Improvements in operational efficiency and regulatory compliance.
Since then, significant changes have transformed the enterprise data landscape.
Traditional MDM systems are designed for structured data with relational databases. The modern data landscape has introduced variability on different dimensions from formts such as semi-structured and unstructured, to sources e.g., IoT devices, social media, streaming platforms, and multi-cloud environments. MDM struggles to handle such multitude of options. As a result, many enterprises qualify MDM solutions as “heavy,” slow to adapt, and costly to maintain. During the 2023 Strata Data Conference [7], multiple speakers asserted that monolithic MDM frameworks were losing ground to more agile, domain-oriented methods, prompting a shift toward new architectural patterns
A study by Gartner [14] indicates that around 90% of businesses fail in their first attempt to implement and maintain an MDM project due to inadequate data governance framework and processes. There are several reasons for this failure, but we will focus on three key points:
One of the most rising architectural shifts is Data Mesh [1], introduced by Zhamak Dehghani and embraced by companies looking to scale data-driven insights across domains. Instead of centralized data ownership, Data Mesh advocates for domain-oriented data products, each managed by autonomous teams that “publish” data contracts for others to consume.
In a 2022 paper presented at the International Conference on Data Engineering (ICDE) [3], researchers outlined how Data Mesh principles improved data discoverability and time-to-insight by up to 40% across global e-commerce platforms.
Another trend gaining traction is the Data Fabric, a concept supported by experts at Gartner [4] and Forrester [11]. A Data Fabric is an architectural approach that uses metadata and AI/ML techniques to integrate, discover, govern, and orchestrate data across hybrid and multi-cloud environments. The key benefits of Data Fabric are:
At the 2023 Data Summit [10], several speakers shared examples of successful Data Fabric projects, highlighting better data lineage, faster reporting and reduced data management costs.
Knowledge Graphs, powering Google Search, LinkedIn’s “People You May Know”, and Facebook’s social graph, are becoming more common in enterprise data strategies. These graphs store information as objects and their connections, making it easier to show complex topics in a flexible way. This helps uncover deeper insights and supports uses like:
These graphs store data as entities and relationships enabling dynamically representation of complex domains. This helps uncover deeper insights and supports uses like:
A 2024 study in the ACM Transactions on Knowledge Discovery from Data (TKDD) [4] found that companies using graph-based methods for customer data saw faster data analysis and much less time spent preparing data compared to older systems.
While MDM isn't entirely obsolete, its traditional form is giving way to more dynamic, scalable solutions leading to a rethinking of current data principles:
While it may be premature to declare MDM entirely “dead,” there is a clear consensus in research papers, conference talks, and real-world implementations that classic, monolithic MDM solutions are no longer suitable for modern data landscapes. New approaches, e.g., Data Mesh, Data Fabric, and Knowledge Graphs, offer more adaptable, scalable, and domain-oriented approaches to unify and govern enterprise data.
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