What is Master Data Management (MDM) and why is it important? (2023)

What is master data management (MDM)?

Master data management (MDM) is a process that creates a unified set of data about customers, products, suppliers and other business entities in various IT systems. One of the basic disciplines in its entiretydata managementMDM helps improve the quality of an organization's data by ensuring that identifiers and other key data elements about these entities are accurate and consistent across the enterprise.

A properly executed MDM solution can also improve data sharing between different business systems and facilitate data processing in IT environments with a variety of platforms and applications. In addition, effective master data management helps to improve the data used in Business Intelligence (BI) and analytics applications more reliable.

Master data management has evolved from the hitherto separate methodologies focused on data consolidation for specific entities, and especially the integration of customer data (CDI) and product information management (PIM). MDM has combined them into one category with a broader scope, although CDI and PIM aresubcategories still active.

The importance of master data management

Business operations depend on transaction processing systems, and BI and analytics are increasingly driving marketing campaigns,customer loyaltyefforts, supply chain management and other business processes. But many companies have no idea about their customers. This is usually because customer data varies from system to system. For example, customer records may not be identical across all order entry, shipping, and customer service systems due to differences in names, addresses, and other attributes.

Similar inconsistencies may also occur in product data and other types of information. Such issues cause business problems if critical data is unavailable or overlooked by end users. Master data management programs help avoid this by consolidating data from multiple source systems into a standardized format to provide the single view of business units you need.

For customer data, MDM harmonizes it to create a single master data set for use in all relevant systems. This enables organizations to eliminate duplicate customer records with mismatched data, allowing operations staff, business leaders, data analysts, and others to access rich customer information without having to manually combine different data entries.

What is Master Data?

Master data often becomes onegold recordinformation in the data domain that corresponds to the subject of the data control. Data domains vary by industry. For example, common to manufacturers include customers, products, suppliers, and materials. Banks can focus on customers, accounts and products, the latter being finance. Patients, equipment, and supplies belong to their respective data domains in healthcare organizations. For insurers, this means members, products and claims, and suppliers for health insurers.

Employees, locations, and assets are examples of data domains that can be applied across industries as part of master data management initiatives. Others are reference data that consists of country and state codes, currencies, order status entries, and other general values.

Master data does not include transactions processed in different data domains. Instead, it basically functions as a master file containing dates, names, addresses, customer IDs, item numbers, product specifications, and other attributes used in transaction processing systems and analytics applications. For this reason, well-managed master data is often described as:any source of truth- or, alternatively, one version of the truth - about organization data, as well as data from external sources that are fed into corporate systems to augment internal data sets.

Architecture MDM

There are two forms of master data management that can be implemented separately or together:analytical MDM, which aims to provide consistent baseline datadata warehouseone analysis system, oneoperational MDMwhich focuses on master data in core business systems. Both provide a systematic approach to master data management, typically enabled by implementing a centralized MDM hub where master data is stored and maintained.

However, there are different ways to design MDM systems, depending on how organizations want to structure their master data management programs and the connections between the MDM hub and source systems. The main styles of MDM architecture identified by data management consultants and MDM vendors are:

• Registry architecture.This style creates a unified index of master data for analytical use without altering the data in individual source systems. Considered to be the lightest MDM architecture it usesData cleaningand reconciliation tools to identify duplicate data entries in different systems and reference them in the registry.
• Consolidation approach.In this style, master datasets are pulled from various source systems and consolidated into an MDM hub, creating a centralized repository of consistent master data used primarily in business intelligence, analytics, and business reporting. However, operating systems still use their own master data to process transactions.
• Coexistence style.Similarly, this style creates a consolidated set of master data in the MDM hub. However, in this case, changes to master data in individual source systems are updated in the hub and can then be propagated to other systems so that they all use the same data, providing a balance between system-level management and centralized master data management. .
• Transaction architecture.Also known ascentralized architecturethis approach moves all master data and update management to the MDM hub, which publishes data changes to each source system. From an organizational point of view, this is the most invasive style of MDM due to the transition to full centralization, but it offers the highest level of corporate control.

In addition to a master data repository and software to automate interaction with source systems, this typically includes a master data management frameworkChange management, workflow and collaboration tools. Another technological option available is the use of data virtualization software to extend MDM hubs, creating virtually unified views of data from different systems, without the need to physically move data.

Benefits of master data management

Here are some of the key business benefits offered by an MDM solution:

• Increased data consistency for both operational and analytics use.A unified set of customer and third-party master data can help reduce operational errors and optimize business processes, for example ensuring that customer service reps see all data about each customer and shipping has the correct delivery addresses. It can also increase the accuracy of BI and analytics applications, which will hopefully result in better strategic planning and business decision-making.
• Improved regulatory compliance.MDM initiatives can also contribute to compliance efforts, such asSarbanes-Oxley-wetand the Health Insurance Portability and Accountability Act – better known asHIPAA-- in United States. New privacy and data protection laws - in particular the European Union's General Data Protection Regulation (Wed) and the California Consumer Privacy Act have become another driving force behind master data management, which enables companies to identify all the personal data they collect about people.
• More efficient data management.MDM is also on board with thisdata managementprograms that create standards, policies, and procedures for the general use of data in organizations. MDM can help you improvedata qualitymetrics typically used to demonstrate the business value of data management efforts. MDM systems can also be configured to provide aggregated views of master datadata managerresponsible for overseeing datasets and ensuring end-user compliance with data governance policies.

MDM best practices

Best practices for managing MDM programs include the following:

• Involve business stakeholders in the MDM process.While MDM is supported by technology, it is both an organizational or human process as well as a technical one. That's why it's important to involve business leaders and users in MDM programs, especially if master data is centrally managed and updated across operating systems through an MDM hub. Different owners of data and business processes in the organization should influence decisions regarding the structure of master data and the rules for introducing changes to them in systems. It is best to start at the very beginning, when the scope of the MDM initiative is defined.
• Document potential business benefits in advance.Aligning the expected benefits of MDM in the use of data resources with business strategies and business goals is generally necessary to gain management support for the program, which is both necessary to secure funding for the work and to overcome possible internal resistance.
• Include end-user training and education in your program.Business units and analytics teams should receive training on the MDM process and its objectives prior to the start of the program.
• Plan long term and structure your program accordingly.MDM should be viewed as an ongoing initiative rather than a one-off project as frequent updates to master data records are often required. Some organizations have established MDM Centers of Excellence to build and manage their programs to help overcome hurdles in efforts to embed common master data sets into enterprise systems.

Master data management challenges

Despite the benefits it offers, MDM can be a challenging endeavor. Here are some common challenges for organizations:

• Complexity.The potential benefits of master data management grow as the number and variety of systems and applications in an organization increases. For this reason, MDM has more value for large enterprises than for small and medium-sized enterprises. However, the complexity of enterprise MDM programs is limiting their adoption even among large companies.
• Misunderstandings about corporate data standards.One of the biggest hurdles is for different business units and departments to agree on common master data standards. MDM efforts can lose momentum and stall if users argue about how data is formatted on their particular systems.
• Project scope issues.Another frequently mentioned obstacle to successful MDM implementations is the scope of the project. Efforts can become unmanageable if the scope of the planned work gets out of hand or if the implementation plan does not adequately cover the stages of the required steps.
• Inclusion of the acquired companies in MDM programs.When businesses merge, MDM can help streamlinedata integration, reduce non-compliance and optimize operational efficiency in the newly merged organization. However, the challenge of reaching consensus on master data between business units can be even greater after a merger or acquisition.
• Dealing with big data collections.Growing usebig datasystems in organizations can also complicate the MDM process by adding new forms of unstructured and semi-structured data stored on different platforms, includingHadoopclusters, other speciesdata moresystems and abovedetails of the lake houseenvironment.

Key steps of the MDM process

MDM initiatives are typically lengthy projects that involve several phases and tasks, including the following key steps:

1. Identify all relevant data sources for your domain and the business owners of each data source.
2. Work with various business stakeholders to agree on common master data formats across all systems.
3. Create a master data model that formalizes the structure of master data records and links them to various source systems.
4. Also, work with stakeholders to determine the type of MDM architecture to implement based on your business needs and planned applications.
5. Implementation of any new systems or software tools required to support the MDM process.
6. Clean, consolidate, and standardize data to align with the master data model using data quality management anddata transformationtechniques.
7. Merge duplicate data records from multiple systems and combine them into single entries as part of the final list of master data.
8. If necessary, modify the source systems so that they can access and use the underlying data during data processing operations.

Software that can be used to automate master data management tasks is available from a variety of vendors, including MDM specialists and larger vendors offering a full range of data management tools. MDM software typically includes data cleansing, data matching and merging, workflow management,data modelingand other functions. In addition, it often includes data management and data management functions or is integrated with related tools that provide them.

Key roles and participants of the MDM initiative

Due to their complexity and wide impact on business operations, MDM programs must involve a wide range of people in the organization. The level of involvement varies by role: some data management professionals work full-time on MDM, while others devote part of their time to it, and business stakeholders tend to participate sporadically but regularly.

Here are some of the key positions and participants in the MDM process:

• MDM Manager.This person oversees the planning, development, and implementation of the MDM program. The position can also be MDM Director, MDM Program Leader, or some other variation. In organizations with MDM and data management programs aligned, one manager can lead both initiatives.
• Masterdata specialist.As the title indicates, this is a technical role that focuses on creating and maintaining master data. Tasks typically include initial cleaning, data matching and merging, and ongoing data quality troubleshooting. In some organizations, the role is a Master Data Analyst or MDM Analyst.
• Dane stewardesy.As part of monitoring datasets in specific domains, data stewards are often involved in MDM programs. For example, they may take on some data management and maintenance tasks or work with master data specialists on these functions. They can also ensure that business units comply with internal standards for master data.
• Other data management specialists.Severalmembers of the data management teamit can also play a role in MDM programs. That includesdata architects and data modelers, as well as data quality analysts who help with data cleansing, and ETL developers who create extract, transform, and load jobs to collect master data from various source systems.
• Main sponsor.MDM programs are large and often costly initiatives that can lead to internal disputes over master data standards and resistance from business units. As a result, they often need an executive sponsor who can ensure the program receives the required funding, help resolve conflicts, and promote adoption.
• Business stakeholders.Business leaders affected by the MDM program should be involved in core data decision making activities. Alternatively, they can appoint subject matter experts to represent their business units. In many cases, the stakeholders involved are organized into a steering group that meets regularly. Organizations with an existing data governance board can instead use it to make MDM-related decisions.