16 Ene 2025 Master Data Management

This blog post is part of our Data Governance series. In the first post, we presented Data Governance (DG) as the driver to achieving data excellence, and noted that a critical factor in implementing DG successfully is to focus on tangible, outcome-driven initiatives. DG shouldn’t be a merely theoretical exercise involving data, roles, and ownership; it must cover essential technical domains like the Data Catalogue, Data Quality, Master Data Management (MDM), and DevOps. In this article, we’ll explore the Master Data Management domain in depth, examining how it enhances data governance by fostering trust and ensuring the quality of core data entities across the organisation.

Over the years, organisations have encountered a range of data challenges, many of which have been driven by the growing volume, velocity, and variety of data, along with the rapid evolution of technology and business processes. One challenge that continues to grow – likely due to the increasing diversity of applications and data systems – is the lack of consolidated core entities across systems and business units. Core entities, like customers, products, suppliers, and employees, serve as the backbone of organisational data, and when these core entities are not fully standardised and integrated, inconsistencies and duplicates can negatively impact all areas of the company, from daily operations to analytical reporting.

For instance, business users may find inconsistencies in customer information between ERP and CRM systems, thus wasting valuable time resolving such issues. BI teams may face complications when multiple rows from different source systems represent the same supplier, requiring additional effort and complex logic in backend ETLs. And in reports, the same product may appear in multiple bars or lines, meaning misleading conclusions and the need for specialised knowledge to interpret results correctly.

MDM addresses these challenges by creating a single, unified view of core entities’ data, known as the golden record, ensuring that all systems and stakeholders work with consistent, accurate, and reliable data. However, implementing MDM can be a complex and challenging process, involving several factors that require careful consideration.

These factors include identifying the required features and functionalities, selecting the appropriate technology stack, fostering collaboration between business and IT teams, determining which data systems require integration, and deciding the right implementation approach:  analytical, transactional, or a combination of both.

Master Data Management – Features

When implementing MDM, it’s important to recognise that while some features are essential for every organisation, others may be optional depending on specific business and IT needs. Understanding which features are critical helps to build an MDM strategy that aligns with your objectives.

The three must-have features are:

1. Data matching and data deduplication: Data matching focuses on identifying and unifying records that belong to the same entity, even when they differ due to inconsistencies, variations or errors, whereas data deduplication eliminates duplicate records representing the same entity to establish a single, accurate version (the golden record). This process can be performed manually or automated using techniques such as deterministic or fuzzy matching.
2. Stewardship and governance: Appointed owners and stewards play a key role in defining how data matching and deduplication should be executed, establishing guidelines for manual processes or setting rules and thresholds for automated tasks. Additionally, they review, curate, and approve the resulting golden records, manage exceptions, and collaborate with data creators and manipulators as necessary.
3. Data integration and synchronisation: Master data should be consistently available, up-to-date, and aligned across the organisation’s various data systems, including data warehouses and transactional source systems. As explained in the following section, the systems which are integrated and synchronised with the MDM application will depend on the type of MDM implementation.

Another three optional (but relevant) features are:

• Data exploration: Before appointed owners and stewards can proceed with data matching and deduplication to define the golden record, it is essential to explore and understand the existing data. However, this step may not be necessary if users already have access to other data exploration tools.
• Hierarchy Management: MDM ensures consistency by standardising business terms, data practices, and attribute hierarchies. It allows users to define not only the values for each attribute, but also their hierarchical relationships, enabling a structured approach to data organisation. This feature may not be required in the MDM application if it is already handled by the organisation’s reporting or analytical platform.
• Workflow collaboration: MDM supports seamless teamwork by enabling task-sharing and coordination. For instance, users can manage approval processes when defining new golden records or new data matching rules, ensuring that appointed owners always have visibility and control of new definitions.

MDM Implementation Types

A critical decision in MDM is choosing the right implementation type. This choice not only determines the capabilities of the solution—for instance, in an Analytical MDM implementation, the outputs (golden records) are applied only to the analytical platform, whilst in Operational and Hybrid MDM implementations, the outputs are applied to the source systems and onwards—but it also determines the complexity of the solution. Some implementation types can be significantly more challenging depending on the technology stack and system integrations required.

In an Analytical MDM, the MDM hub connects solely to the analytical platform, leaving source systems unchanged and focusing exclusively on enhancing reporting and decision-making:

In contrast, in an Operational MDM, the MDM hub connects and synchronises with transactional systems. This not only enhances reporting and decision-making, but also improves the accuracy and efficiency of day-to-day business operations as well. However, this approach demands greater organisational maturity and is more complex than an Analytical MDM:

Lastly, the Hybrid-Enterprise MDM implementation combines both Analytical and Operational approaches. It connects to both transactional systems and the analytical platform, supporting daily business operations, reporting, and decision-making, whilst offering enhanced scalability and flexibility across systems and data domains. However, this approach is highly complex and demands a significant level of MDM maturity for successful execution:

MDM Technologies

When implementing MDM, organisations typically have two main technology options: leveraging their existing data platform and technology stack, or investing in a dedicated MDM-specific tool, based on the required features and their resources.

1. Using the Existing Analytics Stack: This approach is well-suited for organisations that require only basic or essential MDM features. Leveraging the existing analytics stack allows businesses to implement MDM without incurring additional licensing expenses, making it a cost-effective starting point. A common first step is to adopt an Analytical MDM implementation type using the organisation’s current analytics infrastructure. This strategy enables teams to establish foundational processes and workflows, gradually improving MDM practices whilst maintaining flexibility. However, this option often requires greater technical expertise, as the lack of a user-friendly interface can make it less accessible for non-technical users.
2. Using an MDM-Specific Tool: MDM-specific tools are designed to offer advanced features and an intuitive UI, making them ideal for organisations with more complex MDM requirements. These tools simplify the process of defining, reviewing, and approving MDM elements – i.e. rules and golden records – enabling seamless collaboration between technical and non-technical users. In addition to core functionalities like data matching, deduplication, and hierarchy management, many MDM tools provide value-added capabilities such as AI-driven recommendations, automated workflows, and built-in compliance features. These advanced options not only enhance operational efficiency but reduce the dependency on IT for routine tasks too. Although MDM-specific tools often require a higher initial investment, they provide a more comprehensive solution for organisations seeking to scale their MDM practices and to address complex data governance challenges effectively.

Conclusions

The importance of MDM as a foundation for effective Data Governance cannot be overstated. By ensuring the accuracy, consistency, and trustworthiness of core data entities, MDM addresses one of the most pressing challenges that organisations face: the lack of consolidated and standardised data across systems and business units.

Today we have explored how MDM strengthens Data Governance by creating a golden record for core entities and providing key features such as data integration, exploration, deduplication, and workflow collaboration. We’ve also seen the various MDM implementation approaches and outlined the key technology options available.

As businesses continue to generate and rely on vast amounts of data and diverse systems, investing in a robust MDM solution is no longer optional: it is essential! Organisations that prioritise MDM will not only enhance operational efficiency but also enable better decision-making, promote collaboration, and increase trust in data.

Ready to transform your DG practices? At ClearPeaks, we specialise in implementing robust MDM solutions tailored to your organisation’s unique needs. Whether you’re looking to streamline operations, improve decision-making, or promote collaboration through trusted data, our experts are here to help. Contact us today to learn how we can guide your organisation on the journey to data excellence and unlock the full potential of your data assets!