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Master Data Management (Detailed Guide) | All You need to know about MDM


Many businesses these days use a multitude of systems, all of which include significant data on customers, vendors and products in their business, or other critical business KPIs. This results in data silos, redundant data, missing data, and, as a result, a fragmented perspective of the business. Because data is spread across several locations and languages, addressing simple business questions becomes challenging.

MDM, short for Master Data Management, is the technology, tools, and procedures that guarantee master data is synchronised across the company. MDM entails building a single master record from internal and external data sources and applications for each employee, person, location, product, customer or supplier associated with a corporation. It offers a single master data service that distributes accurate, consistent, and complete master data throughout the company in order to enhance accurate reporting, decrease data inconsistencies, eliminate redundancy, and assist workers in making better-informed business choices.



Businesses collect data that fit the following categories:

Master data: Master data includes the real, crucial business objects including but not limited to Business Partners, Products, Profit centres, cost centres, GL Accounts etc. on which day-to-day real-world transactions take place within a corporation, as well as the parameters used in data analysis.

Transactional data: It is the data that represents real-world transactions and logistics (e.g. sales orders, purchase orders, goods movements etc.) between a corporation and its business partners.

Analytical data: As the name suggests, analytical data is created by computations or analyses performed on transactional data.


All data that is crucial to the running of a corporation is referred to as master data. This information is typically shared across the company, and numerous departments and individuals rely on it to make decisions. A set of identifiers that provide some context about business data such as location, customer, product, asset, and so on is known as master data. It is the core data that is absolutely necessary for the operation of a commercial firm or unit. Otherwise, there is no other way to compare data between systems in a consistent manner. A company’s master data is one of its most valuable data assets. Some businesses are even acquired in order to gain access to their client’s master data set.



While master data contains critical components of the company, such as customer data and data linked to business operations and transactions, reference data represents a collection of permitted data to be utilised for categorization from the master data. Furthermore, changes in reference data often occur gradually over time as a direct reflection of any changes to business processes, whereas changes in master data occur inside regular business procedures. Although reference data is seldom modified, little changes over time must be handled and synced throughout the business.

Some examples of Master data are Customer master data, Employee master data, etc. Reference data on the other hand can consist of postal codes, language codes, currencies, etc.



To further comprehend why most business owners and data managers seek a Master Data Management system, consider the following scenarios in which continual changes in data and frequent manual involvement might possibly result in erroneous data being saved on corporate files:

Inconsistent data across the value chain: Organizational data in numerous versions spanning functions, locations, and systems makes ensuring a single view of truth a challenge. This is mainly due to the absence of a unified approach to information management.

Lack of cross-domain connections: Domain masters (such as suppliers, customers, products, and so on) frequently lack ties with one another. As a result, business users are unable to receive operational intelligence, making it challenging for businesses to manage interconnected business operations.

Data manipulation authenticity: Maintaining and monitoring logs for prior versions of data is frequently a burden for organisations, which can have a negative influence on the legitimacy of business operations.

Inadequate process orchestration and data governance: Because of a lack of collaborative data authorisation, organisations struggle to preserve data integrity and security. As a result, business users find it challenging to monitor and approve information in compliance with company policies and processes.


The demand for reliable, timely information is high, and as data sources grow, managing it consistently and keeping data definitions up to date so that all elements of a business utilise the same information is an ongoing problem.  Businesses look upon Master Data Management (MDM) to overcome these difficulties.



Whether you purchase an MDM technology or construct your own, there are two fundamental phases to producing good quality master data:

Data cleansing and standardisation: Cleaning data and converting it into the master data model are operations that are very similar to the Extract, Transform, and Load (ETL) processes used to create a data warehouse. If ETL tools and transformations have already been established, it may be quicker to simply alter them as needed for the master data rather than learning a new tool. Most tools will cleanse the data they can and place the remainder in an error table for manual processing. The cleansed data will be placed in a master table or a series of staging tables, depending on how the matching tool works. As each source is cleansed, you should inspect the output to check that the cleaning process is functioning properly.

Consolidating duplicates by matching data from all sources: The hardest and most critical stage in producing master data is matching master data records to remove duplicates. False matches can cause data loss, and missed matches limit the utility of keeping a common list. Hence, one of the most essential procurement factors is the matching accuracy of MDM tools. Customers may be matched based on their name, maiden name, nickname, address, phone number, credit card number, and other information, whereas items, can be matched based on their name, description, component number, specs, and price. The greater the number of attribute matches and the closer the match, the more confident the MDM software is in the match. This confidence factor is calculated for each match, and if it is beyond a certain level, the records are considered to match. Normally, the threshold is changed based on the repercussions of a false match.



Methods for managing a master list for Master Data Management in businesses are shown below.

Single Copy: There is just one master image of the master data in this technique. All additions and changes to the master data are made promptly. Every request that utilises master data is rebuilt to use the new data instead of the old data.

Multiple Copies: In this technique, master data is added or updated in a single master image of the data, but updates are delivered to source systems where copies are stored locally. Every request can update data that isn’t part of the master data, but they can’t change or add master data.

Continuous Merge: In this method, apps are allowed to make changes to their copy of the master data. Changes to the source data are submitted to the master and merged into the master list. The modifications to the master are subsequently distributed to the source systems, where they are referred to as local copies. This technique necessitates a few changes to the source systems.



There are several architecture models available for implementing MDM. Each of these models has its own pros and cons, and selecting the best option is not always easy. When selecting how to proceed with Master Data Management in a business, the available money, the IT ecosystem, the current organisational structure, the individuals involved, and their skillset must all be carefully evaluated. Regarding MDM architectures, there are three primary architectures that may be recognised:

Registry Architecture: In this structure, the downstream system is only provided read-only access, which means that unauthorised individuals cannot edit the master data in any manner. It provides a read-only representation of master data for downstream systems that need to comprehend but not alter the master data. The implementation structure is effective for eliminating duplications and providing a uniform method of master data management. It provides low-cost, quick data integration with the added benefit of requiring less interference in your application processes. This methodology is useful for identifying data redundancies.

Hybrid Architecture: This architecture gives the system the ability to adapt or modify the master data. This specialisation aids in achieving rapid access, and because the order has the capacity to alter the data, the quality of information also increases. This framework enables the Master Data Management system and the application system to collaborate. The disadvantage is that the expense of sustaining this sort of structure is sometimes significant since it is difficult to alter and amend the master data. Its primary goal is to consolidate master data and ensure consistency.

Repository Architecture: This architecture, also known as Enterprise, Centralized, or Transactional Architecture, stores all of a company’s master data in a single database, including all of the properties required by all of the master data’s applications. This framework ensures consistent consistency, precision, and efficiency. The application system has minimal overhead since Master Data Management controls everything, saving time.



No Data Duplication: One of the major issues with decentralised data applications is redundancy. Data duplication causes a lot of confusion, which can lead to errors not just in the master data processes but also in other business processes that rely on master data. An MDM system creates a single data source, eliminating data duplication and increasing the efficiency of business operations.

Improved Data Quality: When master data is maintained in several locations, also including diverse formats, conventional spreadsheets, and separate apps, the data’s usability suffers. Furthermore, uneven data formatting reduces the efficiency of many systems that rely on master data to complete their jobs. MDM guarantees data consistency and uniformity, making corporate operations more efficient and effective.

Improved Data Compliance: With data guidelines and policies becoming increasingly stringent, effective data storage and administration are vital for every firm working with data. Noncompliance with data guidelines can have far-reaching consequences, including penalties and damage to reputation. The use of MDM software reduces the likelihood of security breaches and regulatory noncompliance.

Better Decision Making: Incomplete and incorrect information would allow management to make ill-informed decisions that would have a negative influence on the company’s long-term success. Managers would benefit from having access to up-to-date, high-quality data in order to design effective plans. MDM provides a broader perspective and more control over an organization’s data. It also assists the leadership, senior management, and middle management in making sound judgments.

Decreased Cost and Time: Without MDM, firms may struggle to manage the rising volume of data. The complexity of master data makes manual processing extremely difficult. It also costs a large amount of time and money to handle master data correctly. The MDM programme automates the majority of the data management process, saving a significant amount of time. Because MDM requires fewer resources to maintain data, it lowers data management and processing expenses for businesses.



Data Standards: One of the most difficult aspects of MDM deployment is setting the standard. The data standard you define for your master data should be consistent across all data types in your organisation. The standard you establish must be adaptive to data from different departments within your business. As a result, if not well managed, standardisation can be a time-consuming procedure.

Model Definition and Agility: The master data model is divided into three layers: first-tier master data, second-tier master data, and meta-data. The integration of these many levels simplifies and clarifies the master data. Your preferred master data model will have a significant influence on your business operations. Furthermore, the MDM solution you choose should be agile and capable of easily adapting to changes in complex systems. If you combine an inactive and confusing master data model, you will simply aggravate your existing problems.

Data Governance: Even if you have established data standards and specified the appropriate data model for your vast master data, implementing the MDM solution might be difficult. To properly manage the complexity of master data, you must establish the appropriate data governance principles and stringent business standards. Data governance is critical for gaining a comprehensive picture of data operations.

Data Integration: This is a critical component of Master Data Management since you must collect data from various corporate applications and sales channels and manage it in the MDM system. The time-consuming task of data integration is made easier with the assistance of modern technologies. Data integration is a time-consuming procedure but is critical, especially when establishing a central MDM system.



Here are a few best practices that should be followed while working on Master Data Management:

Integrate data governance: Data governance and data quality should be included in your MDM. Workflows and guardrails are included in a strong data governance architecture to verify for correctness and redundancy and to match new data entering the system with existing records. A contemporary MDM platform uses machine learning and AI to automate most of this labour. This enables you to get the benefits of master data management without the additional effort required to assure its quality.

Update data on a regular basis for privacy management and security: GDPR and CCPA were only the beginning of data privacy requirements. As technology generates and absorbs more data, additional legislation to safeguard consumer privacy will emerge. Apart from that, crucial data is a desirable asset to both hackers holding it for ransom and competitors who may use it to gain an edge. Legacy MDMs that are slow to update fail to adapt to customer requests fast, and they may also need hours or days of downtime for software and security upgrades.

Always use additional master data or a multi-domain approach: More master data types (or domains) on a single platform equals more comprehensive insights and better business outcomes. Many firms keep customer and product master data separate, much less even supply chain, asset, location, and personnel data. Bringing all of these data sources, whether a transaction or product data, into your MDM, helps you to discover hidden connections in your business’s seams. The fewer master data silos there are, the more connections can be made across processes to fuel real-time operations at scale.

Arrange master data for scalability and simplicity: A lot of MDM best practice advice will encourage you to start on a small scale, with one subset of data, and organise it clearly to obtain early wins. Wading in like this is a tried-and-true business strategy, but it may fail or cause issues if your MDM is not built to grow. Modern MDM is designed on a scalable architecture to accommodate a phased approach or to allow for agility in responding to changing market circumstances. With the option to add new data properties on the fly, you can grow MDM to bring in more data, which is the first best practice.



When evaluating MDM implementations, look for the following critical features:

  • MDM solution that can handle various domains on a single set of technology and infrastructure
  • MDM solution that allows flexible deployments, such as MDM on premises, in the cloud, and as a service
  • MDM platform that offers a comprehensive collection of data integration connectors out of the box
  • Integrated single-technology-stack data management platform for MDM, data quality, data integration, and business process management (BPM)
  • MDM solution that supports all architectural styles: registry, consolidation, coexistence, centralised, and hybrid.
  • Pricing and licences are predictable and scalable as your MDM solution is deployed.
  • Best-in-class MDM solution with a platform-based strategy that prevents vendor lock-in.
  • MDM solution built on a technological platform that is future-proofed
  • MDM solution with horizontal and vertical scalability


cbs Master Data Management

Many companies are implementing a One Corporation structure: They want to establish globally standardised business processes based on harmonised and consolidated data and systems for the entire company. Digitalisation brings new topics such as IoT, Industry 4.0, Cloud, Big Data and SAP S/4HANA as a digital ERP platform for SAP customers.

With cbs, We do more than simply provide technical consulting for master data management: We also help our customers define a company-wide strategy and implement it globally – as both consultant and implementer – while keeping sight of the big picture at all times.

cbs offers an extensive MDM consulting portfolio: from strategy consulting, governance structure and organisation, process and architecture consulting, tool selection, and SAP MDG implementation to data quality validation and valuation.

Since its foundation in 1995, cbs has amassed considerable experience in master data management. We have continued to work for our customers, devising suitable strategies and developing tailored solutions to optimise master data management at their companies.

SAP offers a range of tools that support the requirements of global MDM. Besides proven SAP NetWeaver-based tools such as SAP Workflow, SAP Records Management, SAP Folder Management, or SAP Interactive Forms by Adobe, a dedicated solution with various components for SAP master data management is available, namely SAP Enterprise MDM. It includes SAP Master Data Governance (SAP MDG), a more recent data management tool, which is integrated for the purpose of consolidating, maintaining, validating, approving, and distributing master data. SAP MDG can be used for master data that is to be maintained and managed centrally in the SAP Suite. The implementation experience that cbs has gained at diverse customers confirms the benefits of using SAP MDG in a global, regional, and local solution environment.


Learn more now: https://cbs-stag.de/apac/competencies/master-data-management/

Discover how cbs MDV, our comprehensive software solution, can help to improve the quality of your master data. Contact us and download our brochure for more valuable insights! 

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