Master Data Management Systems: Examples, Benefits, Practices, Software, and MDM Tools

Master Data Management (MDM) is the cornerstone of data quality and consistency. In this article we explore MDM examples, discover its wide-ranging benefits, learn best practices, and find out about the essential MDM systems and tools.

What is Master Data Management?

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    The goal of master data management (MDM) is to guarantee the accuracy and consistency of shared data inside an organization. For example, important commercial information about clients, goods, and suppliers. 

    Eliminating data silos, cutting down on duplication and missing data, and creating a single source of truth for all crucial data are the main objectives of MDM implementation. A large retail company using MDM to manage product information from many sources in order to assure the integrity and consistency of product data across all sales channels and touchpoints is a real-world example of MDM in action.

    MDM has been effectively deployed by many big companies (like Coca-Cola, Johnson & Johnson, and GE) to enhance their decision-making procedures. These companies have benefited from their MDM projects in terms of better data quality, greater efficiency, and eventually better decision-making.

    statistics of the growth of Master data management and its impact on enterprise data management

    Benefits of using Master Data Management

    There are many benefits of implementing MDM from improved data quality and better reporting to streamlined data sharing between different business systems to more trustworthy data for business intelligence and analytics applications. Here’s a full list:

    1. Improved data accuracy and consistency

    By creating procedures and tools for managing crucial data at scale, master data management can increase data consistency and accuracy. Reduce errors and redundancy by removing redundant and contradictory information with MDM. That's not all. By centralizing and managing mission-critical data, MDM can also increase productivity and foster trust in the data.

    2. Improved data visibility across the organization

    MDM offers a single point of reference for crucial data which can significantly increase data visibility throughout an organization. Regardless of where the data originated, MDM makes sure that everyone in the business has access to correct and current information.

    3. Improved decision-making and analysis

    A single, accurate, and consistent picture of vital business data can be created through the master data management (MDM) process. Information on customers, products, and vendors may be included in this data. 

    4. Increased efficiency of business processes

    Master Data Management (MDM) is capable of increasing the efficiency of business processes in different ways. Basically, this can be achieved by improving data quality, reducing errors, and eliminating information silos. 

    Improving master data quality will result in higher efficiency. Organizations can also save time and resources with automated and optimized data operations, hastening the introduction of new goods and services.

    5. Increased customer satisfaction

    Offering individualized interactions, delivering a consistent experience across channels, master data management (MDM) can significantly increase customer satisfaction. 

    6. Reduced data dredging and duplication

    No one likes data dredging and duplication. Repeatedly searching and collecting the same data from different sources results in redundant and conflicting information. This is where Master Data Management (MDM) comes in. It can help in reducing data duplication by establishing a centralized and complete master data source.

    7. Increased data security

    MDM offers more controlled, secure self-service access to trustworthy data. The healthcare, financial, and retail sectors are just a few industries that can take advantage of this benefit. MDM, for instance, can assist in ensuring the accuracy and security of patient data in the healthcare industry and fraud prevention and regulatory compliance in the financial industry.

    basic graphic of increased data security

    8. Improved compliance and risk management

    Through centralized and complete master data, users (companies and organizations) can reduce costs associated with compliance reporting and penalties. 

    Mandatory compliance reports and audits help mitigate the risks associated with non-compliance. 

    basic graph showing data sharing in master data management systems

    9. Improved productivity and profitability

    Most importantly of all, with Master Data Management Systems you can greatly improve productivity and profitability for businesses. Thanks to providing a centralized system for managing critical data across different departments and systems.

    Example: Procter & Gamble (P&G) has implemented MDM to address the challenges it faced with managing its vast amounts of product data, including information on ingredients, packaging, and labeling across its various brands and regions.

    basic graph showing improved productivity and profitability by using mdm systems


    Best practices for managing master data with MDM systems

    Now that you are aware of the benefits of using Master Data Management, you might want to know how to get the most out of MDMN tools. Let's dive:

    1. Identify Master Data and Populate Dataset

    Start with identifying datasets when using MDM tools. There are several phases involved in this, such as data profiling, data mapping, and data cleaning. Data mapping makes it possible to find correlations between various data items, while data profiling aids in determining the accuracy and completeness of data. To make sure that data is correct and consistent, data cleansing is crucial.

    A structural organization for identifying master data could include the following steps:

    • Defining business requirements and identifying data sources
    • Performing data profiling to identify data quality issues
    • Mapping data elements to identify relationships between data sources
    • Cleansing data to ensure accuracy and consistency
    • Involve stakeholders in the validation of master data attributes
    • Populating the master dataset
    • Establishing ongoing data governance processes to ensure data quality and consistency

    2. Perform Quality Management and Verification

    Prevent costly errors and inconsistencies with quality control and verification. Data consolidation, data governance, data quality management, and proactive monitoring are features offered by MDM tools. 

    3. Manage the Life Cycle of Master Data

    Here are the different stages of the life cycle and how MDM tools can help:

    • Creation: Identify the databases and applications that hold data to be included in the master record. Define all attributes or characteristics of the data. Match, reconcile, and merge data where necessary.
    • Maintenance: Cleanse, transform, and integrate new data as it's added to the master list. Maintain consistency and quality of records. Automate and accelerate many aspects of this process using MDM tools.
    • Retirement: Remove outdated or irrelevant data from the master list. MDM tools can help identify and retire data that is no longer needed.

    4. Create a Data Catalog and a Single View

    A data catalog should be designed to provide a consolidated view of all master data to allow for easier data discovery and understanding. For this purpose, many companies use MDM tools like Microsoft's Azure Data Catalog, Sphera's Master Data Management, and Pimcore. 

    These tools offer features for managing master data, including business semantics management, data governance, and data integration.


    Data Centralization


    5. Implement a Master Data Governance Program

    Establish a structured framework that defines roles and responsibilities, establishes policies and procedures, and implements a data quality framework. First, locate the apps and databases that house the data that will be part of the master record. The data should be matched, resolved where inconsistent, and merged where numerous records exist.

    The application should concentrate on maintaining the initial master data record after creating it by purging, converting, and incorporating fresh data. By utilizing MDM tools that take advantage of AI and machine learning, this process may be automated. 

    6. Create an Analytics Hub

    The best practice for managing master data is to construct an analytics hub, thus it's crucial to connect master data from many sources and have a centralized hub. This makes it possible for business and IT users to work together and create thanks to a trusted 360-degree view of master data across the organization. Follow these steps:

    • Identify all sources of master data in your organization
    • Determine which MDM tool to use
    • Centralize and standardize the master data with these tools
    • Integrate the master data into an analytics hub
    • Create customizable dashboards and workflows
    • Use pixel-perfect reports and report formatting templates
    • Implement dynamic data refresh, data share, and in-Excel collaboration
    • Provide mobile access and third-party scheduling reports to enable users to access and share data anytime, anywhere

    basic graph showing analytics hub in master data management systems

    7. Integrate and Synchronize External Data

    MDM tools can also be utilized to integrate and synchronize external data. Yet, integrating external data can be challenging due to data mapping and transformation. Use an MDM tool that features data profiling/cleansing, as well as data mapping capabilities.

    How can you do this? Once you have identified the sources of external data and profiled the data, map the data to the MDM model. Finally, you will be able to synchronize the data back to the sources.

    8. Develop an Enterprise Data Architecture

    Establishing a clear data governance framework is important. Include policies, workflows, as well as roles that ensure data quality and consistency across all applications. 

    Integrating data from diverse sources, such as reference data, metadata, and master data assets, is part of a holistic approach to data management. This method enables a more thorough comprehension of the data and its linkages.

    9. Implement a Data Discovery Process

    An essential practice for managing master data with MDM technologies is putting a data discovery strategy in place. You can navigate this process using the following step-by-step approach:

    • Define the scope of the discovery process.
    • Identify the processes and data domains that are critical to your organization.
    • Identify data sources and assess their quality.
    • Develop data governance policies to ensure that data is managed effectively and securely throughout its lifecycle.
    • Collaborate with IT and business stakeholders to understand their data needs and priorities.
    • Leverage MDM tools to automate and streamline the data discovery process. 

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    MDM systems and their features

    1. Kohezion Online Database as master data management systems

    Technically, Kohezion is an online database platform that can be used as master data management systems. Kohezion can do a great job of guaranteeing the accuracy and consistency of shared data inside an organization. 

    Kohezion gives businesses a set of tools they need to develop web applications with more encryption and security and users can create applications for data. 

    Kohezion can act as:

    • Master Data Management system
    • System of record 
    • Business Management Software
    • Non-Profit CRM
    • Clinical Data Management System
    • Compliance Management Software

    Kohezion provides a simple, low-code solution (actually a database builder) that lets organizations be independent of developers while empowering them to focus on other activities and be more efficient. 

    2. Microsoft MDM

    Microsoft offers a Master Data Management (MDM) solution called Master Data Services (MDS). An organization's master data can be managed in one place using the web-based MDS application. Through semantic reconciliation of master data, it facilitates the worldwide identification, linkage, and synchronization of master data across heterogeneous data sources. A central, persistent system of record or index of record for master data is also created and maintained by this process.

    example of microsoft mdm dashboard

    3. Informatica MDM

    Informatica MDM solution offers a single view of data from various, redundant, and incompatible sources. Data integration, business process management, data security, and AI and machine learning capabilities are all part of Informatica MDM. It integrates a range of data types, including transactional and supplier data, and enables different domain MDM.

    It can be installed on-prem or in the cloud. While its data governance features may be less developed than those of other MDM solutions, it nonetheless offers a highly efficient solution that may enhance data management.

    4. Stibo Systems MDM

    A complete set of Master Data Management (MDM) technologies is available from Stibo Systems to assist enterprises in connecting, governing, enriching, and syndicating data across domains. The Multidomain product integrates data across the enterprise, applications, and systems and consolidates master data from many sources into a single source. For data on products, customers, assets, suppliers, employees, references, and locations, STEP, the company's MDM solution, provides multi-domain MDM.

    However, Stibo Systems' MDM system has some downsides, such as a convoluted approval process for processes, a lack of proactive data governance, underdeveloped reference data management capabilities, and few possibilities for cloud adoption. 

    5. Reltio MDM

    Master data, reference data, transaction data, interaction data, and social data are all combined and related into a single unified view via Reltio's next-generation Master Data Management (MDM) platform, which delivers a cloud-native, scalable, and secure solution. Hierarchies, Connected Graphs, Progressive Stitching, Identity Resolution, Data Quality, Dynamic Survivorship, Universal ID, and Data Governance capabilities are some of its key characteristics.

    6. Oracle MDM

    The master data management (MDM) products from Oracle are made to facilitate the organization, governance, and sharing of master data within the company. Oracle's cloud-based MDM solutions were created specifically to handle the complex business models and products of today, in contrast to legacy systems. 

    oracle master data management feature in the footer of their website

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    Frequently Asked Questions

    Several MDM software categories are used, including data integration tools, data quality tools, and data governance tools. Data integration tools (like SnapLogic) unite disparate data while creating high-quality master data reference sources with the use of data science and machine learning. Data quality tools (e.g. Talend) ensure that data is accurate, consistent, and standardized. As for the data governance tools, they provide a framework for managing the policies and processes for enterprise data. Or, use one single source of truth like Kohezion online database to avoid needing multiple MDM categories.

    All data inside an organization must be gathered, arranged, and made accessible through the processes of data management. A type of data management known as master data management (MDM) focuses on the characteristics of key business entities such as clients, vendors, products, and assets. MDM makes sure all crucial data throughout the organization has a single source of truth.

    Master Data Management systems are capable of managing various types of data, including:

    • Product data includes information such as product color, size, and materials.
    • Location data includes information like zip codes, area codes, and three-letter airport codes used by airlines.
    • Customer data includes essential info about customers like their names, contact information, and purchase history.
    • Other data includes reference data, such as healthcare codes used between organizations.

    MDM software can collect and organize enterprise data from a wide variety of sources, including internal and external data sources, legacy systems, and cloud-based systems. Master Data Management systems help connect dispersed data points by acting as a central repository for data management, cleaning, and verification. Organizations may manage all master data on a single, integrated data management platform thanks to multidomain MDM technologies.

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