Master Data Management (MDM)

Last updated Jan 19, 2025

Master Data Management (MDM) is a crucial methodology for centralizing Master Data. It serves as a pivotal bridge between business professionals, who are the primary custodians and experts of this data, and data specialists.

MDM is recognized as a preferred tool for ensuring data uniformity, accuracy, stewardship, semantic consistency, and accountability, particularly regarding enterprise-scale Data Assets.

The tool that probably revolutionized this process was Master Data Services (MDS) from Microsoft.

A list of notable MDM tools:

• Master Data Services (MDS)
• Semarchy
• Informatica MDM Cloud: One of the most comprehensive cloud MDM solutions, known for its robust matching algorithms and business process workflows.
• Ataccama
• Reltio
• Profisee
• Stibo Systems
• CluedIn
• EDITable
• SAP Master Data Governance Cloud: Particularly strong if you’re already using SAP systems, offering tight integration with SAP’s business applications.
• Profisee Platform: A modern, cloud-native MDM platform that’s particularly user-friendly and can be deployed in Azure, making it a popular choice for former MDS users.
• Reltio Cloud: Built specifically for cloud environments, offering real-time MDM capabilities and strong API integration features.
• Semarchy xDM: Known for its “Intelligent MDM” approach, combining traditional MDM with data quality and governance features.

Open-Source:

• AtroCore

Other Popular Modern MDM Solutions:

Relevant insights can be found in a Twitter post by Matt Ardern.

The history of Master Data Management (MDM) spans over a century, beginning with innovations that laid the groundwork for data management and evolving into sophisticated systems designed to streamline and ensure the consistency of core business data.

In 1890, Herman Hollerith designed a punch card system to expedite the U.S. census, a foundational moment that led to the classification of data into static and changing types. This early differentiation mirrors today’s distinction between master data and transaction data. Hollerith’s work, foundational to IBM’s origins, marked the inception of data categorization crucial for later developments in data management.

Master Files emerged as a concept in 1898 with Edwin G. Seibels’ invention of the lateral file, evolving into master files that contained critical descriptive information like customer names and addresses. By 1936, the Social Security Administration created a master file for tracking deaths, showcasing early government use of master data for administrative purposes. These master files, precursors to digital master data, were essential for businesses and governments to organize and access key information efficiently.

The transition to digital Master Data began in the 1960s, propelled by the Association of Data Processing Service Organizations (ADAPSO) and its focus on Data Management. Master data, conceptualized as an organization’s core data, became increasingly important in the 1980s with the rise of Master Data Management (MDM) programs. These efforts aimed to ensure the accuracy and consistency of essential data across an organization, recognizing the critical role of master data in business operations and analytics.

Master Data Management itself gained prominence in the 1990s, driven by the need to manage burgeoning volumes of data and the introduction of regulatory measures like the Sarbanes-Oxley Act of 2002. MDM systems provided a unified reference for essential data, aiming to eliminate data discrepancies and improve organizational efficiency. The evolution of MDM reflects an ongoing effort to address the complexities of managing core business data in an increasingly digital and data-driven world.

Throughout this history, the development of MDM has been a response to the challenges posed by growing data volumes and the need for accurate, consistent data management practices. From Hollerith’s punch cards to sophisticated MDM systems, the journey of master data management highlights the continuous effort to harness data’s power for organizational success and regulatory compliance.

Read more on A Brief History of Master Data - DATAVERSITY.

Central master data management provides several advantages for organizations managing data across multiple systems:

• Single Source of Truth: Eliminate data duplication and inconsistencies by maintaining master records for customers, products, and other entities across all systems.
• Automated Data Matching: Fuzzy matching capabilities to automatically consolidate customer records from CRM, ERP, and analytics systems.
• Change Management: Controlled processes for handling data updates with automated notifications and approval workflows.
• Workflows: Structured approval processes where technical teams can perform bulk matching while domain experts verify changes before production.
• 360-Degree Views: Create comprehensive views of customers, products, and suppliers by consolidating information from multiple source systems.
• Quality Control: Automated validation combined with domain expert verification ensures high data quality and consistency.
• Multi-Format Support: Handle diverse source data formats, including CSV, API responses, and direct database connections.
• Flexible Implementation: Scale from simple process-oriented workflows to sophisticated Excel-based or web applications based on organizational needs.
• Business-IT Collaboration: Clear role definition between technical teams and business domain experts.
• Unified Analytics: Enable consistent reporting and dashboards across the organization by using standardized master data.

mindmap
  root((Central Data
    Management
    Benefits))
    (Single Source of Truth)
      ::icon(fa fa-database)
      (Eliminate Duplicates)
      (Consolidated Entities)
      (System-wide Consistency)
    (Enhanced Analytics)
      ::icon(fa fa-chart-line)
      (360° Customer Views)
      (Unified Dashboards)
      (Cross-system Reporting)
    (Data Quality Control)
      ::icon(fa fa-check-circle)
      (Structured Approvals)
      (Expert Verification)
      (Automated Matching)
    (Process Management)
      ::icon(fa fa-cogs)
      (Clear Workflows)
      (Role Definition)
      (Change Management)
      (Automated Notifications)
    (Implementation Flexibility)
      ::icon(fa fa-puzzle-piece)
      (Scalable Complexity)
      (Multiple Tools)
      (Format Support)
      (System Integration)

• Master Data Management | dedp.online (paid content)

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Created: 2022-09-05