Master Data Management
An overview of the four key MDM implementation styles
Everything from business and education to lifestyle relies on data in various forms and from various sources. As we witness the relentless growth of economies, it is no secret that data is probably the most important currency in contemporary times. Organisations rely heavily on accurate, consistent, and uniform data to drive strategic decisions and maintain operational efficiency. Clearly, handling this data has become just as crucial as procuring it. Master Data Management (MDM) emerges as a critical discipline, enabling businesses to unify and manage core data entities — such as customers, suppliers, sites, and accounts — through effective master data maintenance. By creating a single source of truth, MDM ensures seamless data governance and enhances business agility.
However, implementing an MDM system demands careful planning and the selection of an appropriate MDM implementation style tailored to the organisation’s unique needs. The choice of implementation approach depends on factors such as corporate structure, business processes, and data management strategy.
Choosing the right implementation style is not just about meeting current needs but also about paving the way for scalability and growth. This article delves into the four most popular MDM styles, guiding entrepreneurs and business leaders in selecting the optimal approach for their organisation’s success.
Registry style MDM
The registry style is a foundational MDM style employed primarily for identifying duplicate records using cleansing and matching algorithms on data derived from various source systems. This approach assigns unique global identifiers to matched records, enabling the creation of a single version of the truth. The registry style does not push data back into source systems; instead, master data updates continue within the original systems.
By focusing on cleaning and matching cross-referenced information, the registry style assumes that each source system maintains the quality of its own data strategy. Critical enterprise data, such as customer information, is stored to facilitate matching and linking across corresponding records, providing a 360-degree view of this data in real time. However, central governance is essential to ensure consistency and reliability across all data assets.
Consolidation style MDM
The consolidation style creates a “golden record” by centralising organisational data from multiple sources into a single hub. Unlike the registry style, this approach involves human verification to ensure data accuracy, enhance reliability and enable informed decision-making. It also has updates synced with original systems like ERP and CRM.
This data management strategy simplifies master data maintenance by consolidating cleansing, matching, and de-duplication processes in one location, making it especially valuable for organisations with significant analytics needs. While more expensive than registry implementations, it remains cost-effective compared to other approaches, offering a balanced solution for mid-sized businesses aiming to eliminate data silos and achieve operational efficiency.
Coexistence style MDM
The coexistence style creates a central MDM hub that works alongside existing source systems, offering a hybrid approach to managing master data. While the hub holds master data, source systems retain operational data and handle transactions. Bi-directional synchronisation keeps the hub and source systems in sync. It allows you to update master data directly in the MDM platform or within the source systems.
This approach has several benefits. Rolling it out in phases helps larger organisations avoid major disruptions and adopt the system gradually. It’s flexible, working with different data needs and existing integrations. A central hub keeps data accurate and consistent while letting other systems do their job without interference.
Centralised style MDM
The centralised style of MDM allows organisations to store and maintain master data using advanced algorithms for linking, cleansing, matching, and enrichment. The enterprise can ensure consistency by publishing the enhanced data back to respective source systems. The master data hub supports merging records and enables source systems to subscribe to updates, creating a unified and reliable data management strategy.
This architecture establishes the MDM hub as the system of origin for key data domains, such as suppliers, customers, and products. By centralising data creation, businesses can streamline operations, leveraging internal and external enrichment processes to enhance data quality. Workflows and validation capabilities ensure updated information flows seamlessly into ERP and other critical systems, improving operational efficiency and decision-making.
Choosing the right MDM implementation style
Three important factors help categorise the right MDM implementation style for an organisation.
- Pricing: Pricing and cost are the most important considerations when it comes to choosing an MDM implementation style. Each style has a different cost structure. Simply, the registry model is less complex and thus more cost-efficient. Enterprises should know that after choosing one model, they are at liberty to upgrade them as per organisational needs.
- Source: Another consideration is the data sources needed for the MDM implementation. An organisation with a large number of data sources has the advantage that comes with choosing the registry model. As such, multiple sources present challenges to the organisation. But using the data collection, cleansing and matching algorithms, a consensus can be formed.
- Data scrutiny: When reading the two factors given above, the clear winner appears to be the Registry implementation style. But organisations also need to ensure that the golden record is accurate. If more data is needed, then enterprises need to re-evaluate their needs and choose the right MDM style from there.
How can Infosys BPM enhance data accuracy with MDM implementation?
Infosys BPM’s master data maintenance service enhances data accuracy by implementing ongoing data maintenance, process standardisation, and centralisation. The continuous monitoring, correcting, and enriching of data allows businesses to ensure information remains precise, accessible, and actionable across the organisation.
