How to Build a Successful Master Data Management Strategy?

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By: Sagar Sharma

How to Build a Successful Master Data Management Strategy? 

To make informed business decisions, organizations need to become data-driven. However, reliable data is a prerequisite for this. Therefore, organizations rely on a set of core data entities commonly known as “master data” to ensure high levels of accuracy and consistency. By adopting a robust master data management strategy, organizations can ensure effective master data management.

Master data is a fundamental component of an organization’s data-related activities. It comprises crucial information like customer data, employee records, supplier data, and product details that offer a unified, consistent view. The process of enhancing data quality by guaranteeing the accuracy and consistency of key data elements and identifiers is called Master Data Management (MDM).

This article aims to provide you with valuable insights into the key elements of an MDM (Master Data Management) strategy. It will explain what master data management strategy is, highlight its importance, and discuss the essential components of a robust MDM approach.

Furthermore, it will shed light on the tools and organizational structures that are necessary for implementing an MDM strategy effectively, regardless of your company’s size or industry. By the end of this article, you will have a better understanding of how to craft a successful MDM strategy for your organization.

What is a Master Data Management Strategy?

The Master Data Management (MDM) strategy is a combination of systematic procedures which are effectively used to govern the process of data collecting, organizing, consolidating, standardizing, and maintaining the quality of data across an organization.

By providing a reliable and controlled approach, MDM facilitates the creation of a single source of master data, which can be used and maintained by multiple entities throughout a business. This approach ensures that data is consistent, accurate, and up-to-date, thereby enabling organizations to make informed decisions based on reliable information.

Why do Businesses Need the Right Master Data Management Strategy?

As companies strive to stay competitive in today’s fast-paced business landscape, they are increasingly in need of cost optimization, faster product launches, and more efficient regulatory compliance.

To achieve these goals, an effective master data management strategy is vital. Without it, there is a risk of cross-organizational data misalignment, which can lead to suboptimal decision-making and hinder growth.

However, developing master data management strategies and implementing them throughout an organization is no easy feat. It can be a complex process, and one of the biggest challenges faced by enterprises is achieving dependable data quality.

Despite these challenges, organizations that successfully implement a master data management strategy stand to reap significant benefits, such as improved decision-making capabilities, streamlined processes, and increased efficiency.

To understand the essence of master data management in your business, don’t miss reading Master Data Management Strategy: Explained with Example! It provides you with better insights into the real-time implication and results for considering the right master data management strategy.

Why do Businesses Need the Right Master Data Management Strategy?

Discover the top 5 compelling benefits of integrating master data management (MDM) into your organization’s operations and effectively communicate them to your colleagues and superiors.

Gain a deeper understanding of the true value of MDM strategy and how it can help streamline business processes, improve data accuracy, enhance decision-making capabilities, increase productivity, and drive overall business growth.

Don’t miss out on the opportunity to demystify MDM and unlock its full potential for your organization.

1. Establish trust in data

Master data management (MDM) is a process that establishes clear data governance and data quality practices, which can help instill trust in data among stakeholders.

By implementing MDM strategies, employees can gain better visibility into the data quality and data governing mechanisms that are being used. This can lead to bolstering their confidence in the accuracy and trustworthiness of the product data across the departments of an organization.

When individuals begin to rely on data for decision-making, they are more likely to incorporate it into their everyday work routines. This includes confidently presenting and analyzing data during meetings and discussions to conclude.

They may also encourage their colleagues to utilize data when presenting ideas or arguments. Overall, the habit of incorporating data into job functions becomes a natural and beneficial practice.

2. Drive better decision-making

In today’s business landscape, relying solely on intuition and opinions from experienced employees to make strategic decisions is no longer an effective solution. Instead, modern enterprises have come to realize the importance of leveraging data to drive well-informed and future-proof decision-making.

By analyzing and interpreting data, businesses can make informed decisions that are based on facts rather than subjective opinions. This approach helps organizations to improve their operational efficiency, optimize their resources, and achieve their business goals more effectively.

3. Manage exponential data growth

As your organization receives an increasing amount of data from various sources, your data repository is likely to expand rapidly.

To effectively utilize this data, it is crucial to filter out unnecessary information, identify and remove duplicates, and maintain consistency across different systems. Taking these steps will ensure that you have access to the most relevant and accurate data.

4. Leverage advanced technologies

Advanced analytics is a rapidly growing field that has gained significant attention in recent years. This approach to data analysis allows organizations to make more informed decisions by predicting future outcomes based on historical data.

Advanced analytics is particularly useful in situations where foresight and predictions are required, such as in predictive maintenance, asset performance, and customer buying patterns.

By leveraging the latest technologies, organizations can gain valuable insights that enable them to optimize their operations, reduce costs, and improve overall performance.

5. Improve and enhance business outcomes

The integration of various data sources is an important step toward understanding the purpose of implementing master data management (MDM). By utilizing reliable, accurate, and consistent data, MDM can improve decision-making and facilitate the development of innovative applications, ultimately leading to the attainment of strategic objectives.

Having standardized and accurate supply chain data including material, supplier, and asset masters can offer several benefits. One of these benefits is improving the visibility of the interoperability and correlation with each of the datasets, which can lead to increased efficiency and less wastage.

When duplicate materials are identified and eliminated, strategic sourcing can be improved and relationships with high-performing suppliers can be maintained. This can help achieve the goal of reducing supply chain costs and ultimately improving the overall bottom line.

How to Draft a Master Data Management Plan?

To ensure a well-crafted plan with high-quality data, it is important to follow certain steps. These steps include:

1. Understand your business plan and strategy

An MDM system is designed to streamline business operations throughout the entire organization. However, it’s important to understand your business and how different departments work together to effectively utilize the system.

Without this knowledge, the benefits of an MDM system may not be fully realized.

2. Do a data management assessment

To effectively establish a master data management system, it is vital to identify the existing data strategies, governance policies, data qualities, technologies, processes, and roles.

Proper analysis of these factors will provide valuable insights into how to proceed with implementing an effective master data management system.

3. Identify your data quality requirements for Master Data Management

To ensure the usability and relevance of your data, it is recommended to combine the information from your business plan and strategy with the enterprise-wide data management assessment.

By doing so, you can identify your data quality requirements and determine what measures are necessary to improve your data. This will help you make informed decisions and derive meaningful insights from your data, which can lead to better business outcomes.

4. Be clear about what defines your master data

Master Data Management (MDM) systems are designed to organize and manage master data across different domains and sub-domains, such as customers and products.

It is important to carefully consider and select the customer MDM strategy scheme that aligns with your master data definition and meets your specific business needs. By doing so, you can ensure that your master data is effectively maintained and utilized.

5. Some departmental data will live outside of the MDM

Certain data may be confined to a specific department, such as public transit reimbursement. This is generally acceptable unless the data is considered to be master data that is necessary for activities that span across different areas of the business.

6. Execute your Master Data Management strategy

To ensure high-quality data, it is recommended to incorporate data quality activities and processes into your Master Data Management (MDM) strategy. This can include techniques such as data cleansing and matching, which are aimed at identifying and correcting any errors or inconsistencies in your data.

By implementing these practices as part of your MDM approach, you can enhance the accuracy and reliability of your data, leading to better decision-making and improved business outcomes.

7. Periodically Improve Your MDM Plan

In response to the evolving business landscape and advancing technologies, it is important to adjust your Mobile Device Management (MDM) strategy accordingly. Regularly evaluating the effectiveness of your MDM approach can help determine the necessary changes to optimize its impact.

If you are interested in digging deep into the effective roadmap for creating an effective master data management strategy, you must visit Steps & Roadmap to Create Strategic MDM.

Most Common Challenges A Company Faces While Implementing MDM Strategy

When it comes to implementing a master data management strategy, businesses may face certain challenges that can make the process difficult. Being aware of these challenges is crucial for companies that are planning to roll out a master data management strategy. Some common challenges that businesses may encounter during implementation include:

Data Complexity

Master data management strategy can present several challenges related to data quality, especially when data is sourced from multiple systems and departments. These challenges can be complex and require careful consideration to ensure that data is consistent and accurate across the organization.

Duplicate Data

Implementing a Master Data Management (MDM) strategy can sometimes lead to the occurrence of duplicate data. This is particularly common when companies or organizations are handling several master data domains simultaneously.

Common Standard

Organizations that utilize multiple data management platforms or software may face challenges in establishing a cross-platform standard for data. This can result in variations in data formatting, input methods, storage protocols, and more.

How to Develop a Powerful Master Data Management Strategy?

Here are the 6 effective steps that should be considered by businesses while developing and implementing a successive Master data management strategy.

Step 1: Do the research

A product master data management plan is available through many systems integrators and can be a valuable component of larger implementations. While the option can be expensive, it is worth considering.

As part of the discovery process, you can connect with one or more integrators to learn about their experience in treating data as an asset that drives performance.

Integrators with this experience will have the expertise and knowledge to capture, clean, organize, and integrate data in a way that makes it easily retrievable and analyzable by business users.

Step 2: Include every department

Master data management is a valuable tool that can benefit users across different geographical locations and business departments, including marketing, sales, product development, supply chain management, and others.

To establish effective master data management practices, it is crucial to involve cross-functional input from leadership and end users from each department.

This will help in identifying the technical requirements and workflows that will be essential to achieving business objectives. Additionally, it is important to consider the various systems that need to be integrated through master data management, such as e-commerce, point of sale, ERP, CRM, and inventory management systems.

Step 3: Gather use cases

To effectively cater to the needs of sales, marketing, and engineering, it is recommended to conduct interviews with relevant personnel from each department. This will provide insight into the challenges that each department faces, which can then be documented for further analysis.

Additionally, it is important to educate your team about the daily operations, processes, roles, goals, and objectives of each department.

For example, understanding the product lifecycle from ideation to design and build, documenting the steps involved in launching a new product, and analyzing how data is managed across different systems are all important factors to consider.

Step 4: Create the business case

It is important to ensure that your master data management strategy aligns with your company’s strategic business objectives. This will ensure that it is not only approved but also delivers the necessary functionality to serve your company’s vision, mission, and goals.

To achieve this, it is recommended to identify the three to five business initiatives that can be improved with master data management. This should not be a difficult task, as it can involve launching products faster, improving warranty program sales, reducing refund requests, cutting supply chain complexity, or shortening the product lifecycle.

Implementing efficient master data management solutions can provide a single authoritative view of information and eliminate costly inefficiencies caused by data silos for each of these business initiatives.

Step 5: Identify implementation styles

If you are considering a master data management strategy, it’s important to determine the implementation style that best suits your organization’s needs.

There are four common implementation styles to choose from, and factors such as data requirements, access needs, and device usage should be taken into account. Additionally, the decision of whether to use on-premise deployment or managed services should be carefully considered.

Thankfully, there are many options available today that can meet your current needs and grow your business. For example, Stibo Systems can serve as a single point of contact responsible for your cloud-based master data management needs, including hosting, monitoring, maintenance, and installation.

Step 6: Request a proof of concept

To ensure that vendors can meet your business objectives effectively, a proof of concept is the best approach. This allows you to test the strategies you have created and refine the program scope accordingly.

During the proof of concept, you can define and test the data elements to be included, establish consistent definitions, and map out related processes.

The purpose of this is to check if your proposed master data management solution can serve as the core component to manage, centralize, organize, categorize, localize, synchronize, and enrich data according to your business rules.

By conducting a proof of concept, you can begin building a targeted pilot and make any necessary adjustments before embarking on an enterprise-wide project that may be too ambitious and ultimately lead to failure.

5-Core Elements of a Robust Master Data Management Strategy

Here are the lists of 5 core elements on which businesses should focus more on creating an effective MDM strategy.

1.  Data Governance

The process of governance aims to guarantee the reliability, uniformity, and precision of data. To achieve this, it encompasses the creation of data regulations, the identification of data stakeholders, and the establishment of protocols for data utilization and availability. It is important to address various inquiries such as:

  • What are the critical data elements (CDEs) which are essential for an organization to be managed, and who owns them?
  • What are possible data retention policies an organization can have, and how can they be implemented to ensure data comply with the regulations?
  • What are the procedures for managing data changes and updates, and how are they documented and tracked?

2. Modeling

Modeling is a crucial process that involves creating a comprehensive data model for an organization. This master data management strategy for an enterprise is designed to establish clear relationships between various entities, enabling the data to be structured consistently and logically across the organization. Modeling helps to answer important questions such as:

  • How the data entities are identified and defined across the organization’s data models including their key attributes?
  • What are the possible relationships an organization may find between various data entities, and how can these relationships be defined?
  • Are there any rules defined for the creation of data, updating data, and deleting data entities? If so, what are they? And how are they going to be implemented?

3. Data Quality

To ensure data quality, it is essential to establish data quality standards, identify any current or potential issues with data quality, and implement strategies to enhance data quality over time. This process requires addressing the following questions:

  • How is the quality of data quality measured and monitored in an organization?
  • What are the possible outcomes and costs an organization needs to face due to poor data quality?
  • How do the data quality metrics communicate across the departments of an organization?

4. Data Integration

Integrating data from various sources across an organization is a crucial task as the number of sources increases. To formulate an effective MDM (Master Data Management) strategy, it is essential to consider the following factors:

  • What are the possible data sources an organization may have?
  • How do data sources within an organization may vary or be misaligned?
  • What are the data integration technologies and tools that can be used to automate this process?

5. Data Security

To ensure the safety and integrity of your data, it is imperative that your MDM strategy clearly outlines the necessary security measures to prevent unauthorized access, data loss, or corruption. This will address important questions such as:

  • Who has access to the master data, and how is access granted and managed?
  • How is data encrypted, and what key management processes are in place to secure data?
  • How is the data backup and restoration process carried out in the event of a data loss or during a security breach situation?

6-Critical Aspects of Master Data Management Strategy that Every Business Should Consider

Organizations these days should prefer to establish the best practices for their data management process, to leverage more from the data. This will pave the path toward narrating a good MDM strategy. Here are the 6 critical aspects every business should consider for leveraging more from MDM.

1. Process

When dealing with data, it’s essential to consider its source and its effect on quality. For instance, for businesses that depend on location data, it’s crucial to ensure that the data is current and accurate as addresses, boundaries, and postcodes tend to change frequently. Using reliable data sources such as GeoPostcodes can aid in this endeavor.

However, it’s essential to note that regardless of the source of the imported data, the organization bears the responsibility for its accuracy and reliability.

2. Organizational structures

To ensure the effective implementation of processes, it is crucial to establish organizational structures that are responsible and accountable for their execution. One widely used approach to achieve this is the RACI matrix, which assigns roles and responsibilities within the organization.

It is recommended that a data management function, such as a Director of Data Management, should assume ultimate accountability for most of these processes. With a clear structure in place, organizations can be better equipped to achieve their data management goals.

3. People, Skills, and Capabilities

An effective MDM strategy should include a comprehensive plan for identifying and acquiring the necessary people, skills, and capabilities needed to successfully implement and maintain master data. It is important to identify the essential skill sets required for this process, which may include:

Data and/or analytics engineering

The process of extracting, loading, and transforming data involves converting raw data into a format that is easy to understand and use. This process results in the creation of a data product that can be readily consumed and analyzed.

Data Analytics

Assisting business stakeholders in addressing their queries related to data interpretation and usage, providing the necessary support to help them make informed decisions.

Data management

To effectively manage data, it is important to establish clear principles and guidelines for its handling. This involves defining a data model that accurately represents the information being managed, as well as specifying the serving layer that will be used to make the data accessible to users.

By taking these steps, organizations can ensure that their data is properly structured and easily accessible when needed.

4. Infrastructure

When undertaking an MDM (Master Data Management) initiative, it’s important to avoid one of the most common mistakes – focusing solely on the technology aspect.

The strategy should always take precedence over the tools used. It’s crucial to ensure that the chosen tools align with the overall goals and objectives of the MDM program.

Ultimately, the success of an MDM project depends on the effective implementation of a well-conceived strategy that takes into account business objectives, data governance, and data quality, among other factors.

5. Policies and Procedures

An effective MDM (Mobile Device Management) strategy involves defining policies that govern the management of mobile devices. These policies can cover a range of areas, including:

Data cleansing policy

To ensure effective data cleansing, it is essential to establish clear guidelines and frequency for the process. Accountability should also be assigned to appropriate individuals or teams.

It is important to have documented means such as methods, solutions, and tools to address any challenges that may arise in the data cleansing process. These measures will assist in maintaining the accuracy and reliability of the data.

Data management policy

The process of data product development involves several key stages, starting from the initial idea generation and culminating in the archiving of the product. The product data life cycle typically includes the creation of the product, followed by its production, deployment, and eventual archival.

Each stage of the process is important in ensuring that the resulting data product is efficient, effective, and able to meet the needs of its intended audience.

6. Culture

When developing a data management strategy, it’s important to consider the organizational culture you want to establish to ensure the success of all components. Specifically, two cultural properties are particularly relevant when it comes to Master Data Management (MDM).

Shared responsibility of data assets: An effective MDM strategy outlines the accountability and responsibilities of various stakeholders involved in managing data quality.

All individuals need to be diligent in identifying and reporting any data quality issues that may arise, as this contributes towards ensuring the accuracy and reliability of the data.

Awareness around integrity, quality, accuracy & completeness: It is important to establish measures to ensure that all parties involved are informed about the expectations and specifications of the master data. This helps to promote a shared understanding and facilitate effective collaboration among stakeholders.


Master data management is crucial for maintaining data integrity and accuracy, which are essential for business agility and profitability. An ad-hoc approach to MDM can have flaws, which is why it is imperative to adopt a well-defined strategy to achieve success. Careful consideration of your MDM strategy is necessary to ensure long-term business prosperity.

Frequently Asked Questions

Q:1 – What is master data?

Ans: Master data basically represents the important business objects, which store the critical, valuable, and agreed-upon business information shared across an enterprise organization.

Q:2 – What is a Master Data Management Strategy?

Ans: A Master Data Management strategy is a method of helping an enterprise business link all of its business-critical data to a single point of reference. In fact, when you properly implement a master data management strategy, it can improve data quality and streamline the data-sharing process across your organization.

Q:3 – What is the main function of master data management?

Ans: The main function of master data management is to provide all business stakeholders with a single point of reference to define and manage all critical data.

Q:4 – What are the advantages of master data management?

Ans: The key advantages of master data management are the following:

  • Eliminates poor-quality data
  • Effective prioritizing
  • Improved efficiency
  • Improved decision-making

Say goodbye to data chaos and embrace the power of PIM/MDM to supercharge your business growth!