In a world obsessed with data holding and storage, Master Data Management is the need of the hour. It’s no longer a ‘thing to think about in the future’, but an urgent, necessary process that businesses of all sizes need to make sense of their data.

In this value-driven comprehensive master data management guide, we will help you define & implement a master data management strategy while addressing key challenges.

The guide is categorized into three parts:

    1. Basics of Master Data Management: Understand the what, why, and how of MDM processes. This part acts as a refresher for professionals and a quick guide for beginners.
    2. Implementing a Master Data Management Initiative: This segment covers everything on MDM strategies, hidden MDM costs to look out for, challenges, and issues to prepare for.
    3. MDM Tools & Technologies: A list of MDM tools and solutions you can use to bolster your MDM efforts.


Let’s dig in!

Basics Of Master Data Management

Data by itself is meaningless.

In order to give data meaning, you have to clean, organize, and structure it in a way that allows for easy access and insights.

For instance, if you’ve got two decades’ worth of data but 40% of it is redundant or dirty data, you would not be able to reliable or accurate insights from it.

Failing to master your organization’s data would result in costly expenses, poor insights, and failed business strategies.

what is MDM Winpure

What is master data management? 

Put simply, Master data management or MDM is a type of data management architecture, governed by data quality standards and practices, designed to help businesses make sense of their data.

Think of master data management as the plumbing in your house. There’s a reservoir (a data hub) to which many pipes and lines are connected (software, tools, or dashboards). The plumbing is designed in a way that allows water to flow smoothly into the taps of your kitchen, bathroom, and other areas where water is required.

Similarly, MDM is designed in such a way that allows different departments in your organization to access data to perform key tasks like:

  • Marketing and sales
  • Budgeting and forecasting
  • Hiring and cost-cutting
  • New business or product line launches
  • New customer experience initiatives

If the system is not well designed, you’ll face many problems. For instance, your teams will not be able to access 360 degree views of the data to get the business information they need to grow and implement specific strategies.


A Quick History of MDM

Master Data Management originated in the 1990s to deal with lots of disjointed master data. For example, schools would find the same student represented in different systems for admission, registration, textbook sales, grading, career counseling, and alumni services. The student would engage with various departments for different needs, each place duplicated the same types of attributes, causing inefficiency and likelihood of error. Schools and other businesses looked to MDM to clean this overlapping raw data for use in any department.

Before computerized systems, MDM meant setting up, using, and maintaining a folder system in a filing cabinet combined with using interdepartmental mail. For instance, hospital volunteers called candy stripers would physically deliver patient data from one department (e.g., radiology) to another (e.g., a physician’s office filing cabinet). Some of the same data from a patient’s folder in a doctor’s office would go to a nursing station or admissions (usually through some standardized forms). This required workers to sift through paper and wait for information to be delivered.

master data management history

Today’s MDM systems use software and networking advances, people, and data governance processes to get work done more efficiently. Rather than paper, current MDM controls and operates on Master Data and Reference Data explained in the next sections.

What Is Master Data In Simple Words?

Using simple terms, master data is the core data that is needed to run operations in a business or organisation. Master data is a set of consistent and uniform identifiers. The identifiers can be prospects, customers, assets, suppliers, products, account charts, sites, hierarchies, locations, etc.

Typically, master data breaks down into four domains:

  • Customers
  • Products
  • Locations
  • Other

Master data tends to be harder to visualize as it describes core data assets distributed across many different databases. This associated information belongs to specific entities necessary for a business to operate and grow.

Each of the four domains can be further divided into subsections:

  • Customers –>  Company name, address, email, and phone.
  • Products –>  The good or service in use and the ingredient or part to make it.
  • Location –>  The region where the head office is located.
  • Other –>  Calendar of company events, licenses.

Likewise, these subsections can be split into further subcategories.

Any level of master data comprises a single source of truth or golden copy that remains consistent and non-duplicated. The power of master data lies in that it can be copied but not altered in more than one place, a hub.

Hubs containing stored and accessible master data come in different models:

  • Registry –>  checks the MDM hub to correct any contradictions among multiple sources.
  • Consolidated –> gathers master data from multiple systems into the hub where it is sourced.
  • Coexisted –> records duplicate master data with the master data in the hub and the duplicate in each source system.
  • Transaction-based –> enhances the data through algorithms ensuring the data is accessible in the source system.

Keep in mind that master data, in any hub, is just information. To turn this data into insights and analytics, you would need to implement MDM governed procedures and processes to make sense of the data.

What Is Reference Data?

Consider reference data, a type of master data that functions as a “Rosetta Stone” for enterprise and departmental applications to understand and work with one another. Typically, other data in a database or external information needed to do business constitutes reference data.

Another example of reference data is with the USPS. They list the state, as pull-down menu values on a form. See below:USPS zip code

What’s The Difference between Data management, MDM & EDM?

Data management covers a broader concept than enterprise data management or MDM. It describes “a comprehensive collection and hierarchy of practices, concepts, procedures, processes, and a wide range of accompanying systems that allow for an organization to gain control of its data resources. This spectrum includes business strategies, databases, business intelligence, and MDM, among others. Data management’s importance lies in its tremendous monetary and social value, as the Harvard Business Review puts it.

Likewise, MDM does not work alone within an enterprise data management strategy. MDM requires good data quality and reference data management towards providing master data. The enterprise data architecture and privacy and security require appropriate access and control for master data, and all the other enterprise data. MDM cannot work alone as an enterprise data management solution.

In the 2010s, data management became particularly necessary. More significant and frequent data breaches disrupted banks, supermarkets, governments, and a hospital. Also, ransomware gained traction, with increasing monetary demands and dangers to privacy. Governments recognized the high financial and political outcomes from data. In response, legislation from Europe (the GDPR) and California (the CCPA) order companies to protect an individual’s privacy. Companies and individuals recognize that they need data management to prevent fines and mishaps and to take advantage of new opportunities like AI and machine learning.

What Is Master Data Management Architecture?

Master Data Management architecture is a way to record and document a business’ data assets, the architecture maps how data flows through the organisation’s systems. The goal of the architecture is to provide a framework of how the data is organised to guarantee that the data is properly managed to serve the business

MDM describes one type of data management architecture designed to connect many different data sources, easier. Since MDM keeps shared data standardized according to Master Data Governance, data is better controlled and more accessible. As a result, MDM gives companies a huge advantage to use all sorts of technologies and process improvements quickly.

Is Enterprise Data Management the Same as Master Data Management?

Using the house metaphor for enterprise data management provides another way to think of MDM. A building has many different systems besides plumbing. Water gets heated by gas or electricity to provide hot water. Most likely different companies provide gas and water. But you will not get hot water alone. You need the gas for plumbing and the house to be functional.

While MDM and enterprise data management make information more useful throughout the company, they have slightly different meanings.

Enterprise data management describes a “holistic framework comprising the people, processes, and technology that optimizes data from a variety of different sources, then makes it available when and where it’s needed.” Think of Enterprise Data Management as a type of blueprint, like a house. Within enterprise data management, MDM describes a specific platform that reduces transaction costs, integrates data, standardizes critical data, and simplifies data architectures across the company so that the entire business can function.

MDM cannot work alone in Enterprise Data Management without other data management components.

mdm vs edm
Difference between data management, master data management and enterprise data management

How to implement MDM initiative?

A master data management initiative consists of three parts:

1). Identify a Business Problem: An MDM process is either tied to a business goal or is initiated to solve a business problem. It’s necessary to have a clear vision of what you’re trying to achieve with an MDM activity.

2). A Master Data Management Strategy: An MDM strategy will lay the groundwork for the activity and will help you identify steps you need to prevent chances of failure.

Related: Here’s everything you need to know about what is a master data management strategy and why it is important.

3). Anticipating Challenges & Hidden Costs: What are the potential challenges you need to watch out for and what to do to avoid failure.

Let’s elaborate on these components in detail.

Identify Business Problems

Generally, companies don’t worry about master data management unless a crucial business process is being affected or there’s a business problem that needs to be resolved.

For instance, most companies would not bother with consolidated views unless they have to get homogenous data for a marketing initiative or targeted advertising for a new product line.

In these cases, the business requires a consolidated view of its data from multiple sources. Moreover, they will require data that is clean and duplicate-free.

Data Health Checklist
A Complete Data Health Checklist

If they choose to ignore dirty data and continue to advertise or market products, they will suffer critical issues as:

  • Loss of customers caused by poor targeting
  • Loss of brand reputation by ineffective marketing
  • Direct financial losses caused by poor planning
  • Internal conflicts and employee lay-offs
  • Irked stakeholders & plummeting shares

The hit to business efficiency & productivity is severe. This is why when a business plans for an MDM initiative, it’s necessary to tie it to a business goal or process so outcomes can be proactively measured.

Create a Master Data Management Strategy

Organizations are realizing that they need to create a single, reliable, updated, and accurate view of their data. In fact, many companies are beginning to implement MDM processes for the sake of getting a single view of their multiple data sources.

But how do you begin to create an MDM strategy without being overwhelmed or risking failure?

To help you develop an efficient MDM strategy and MDM project plan, here are five steps to creating a master data management strategy. 

Master data management steps

Step 1: Get C-level Buy-in for MDM Initiatives  

Decision-makers and C-suite executives are often hesitant when it comes to addressing data issues. They are risk-averse and any initiative that threatens to remove, delete, or even move data is considered a significant risk.

Therefore, getting the buy-in of decision-makers is the first step to a successful MDM project plan.

Here’s how you can do that.

    • Start with a small pilot project. Show how a consolidated view of one department’s data sources helped solve a business problem. For instance, organizing customer data for targeted advertising. Demonstrate how the removing of redundant or duplicate data, led to lower advertising costs & higher ROI. After a pilot project is successful, you can then use this as a case study to demonstrate how poor data quality or poor data management is affecting business outcomes.
    • Involve business stakeholders. They can best articulate how data is affecting their operations and needs. As in the example above, involving your marketing managers is the best way to demonstrate how an MDM strategy can fix a critical marketing problem.
    • Record real business value. Master data initiatives are built around business value. Connect your MDM initiative with a business driver and use the KPIs your executives use to measure the success of this initiative. Some examples of real-business value or KPIs that you can use to convince C-level executives:
  1. Improve customer experience rate
  2. Improve marketing campaign conversion rates
  3. Improve organizational efficiency
  4. Automate regulatory reporting
  5. Deliver consistent customer experience 
  6. Streamline mergers and acquisitions 

Without demonstrating these KPIs or resolution of business problems, your C-level executives may not give the green light to an MDM plan!

Step 2: Plan Your MDM Roadmap

When you’re trying to implement an MDM initiative, it pays to know exactly where you are today and where you need to be a few months from now.

How do you know where you are today?

By doing a health check on your data across the organization. Questions to ask:

  • How is the data captured, stored, and managed?
  • How many sources of data are connected to your central database 
  • How well-governed is your data? Do you have data governance standards implemented?
  • How much of the data is structured, semi-structured, or unstructured?
  • How much do you spend manually fixing data as opposed to automating data management?
  • How are your teams aligned with each other when it comes to accessing & processing data?
  • Do you have regular internal conflicts between IT and business users?

And the most important question:

  • What is the state of your data quality? Is your data timely, complete, accurate, unique and follows standardization rules?

Related: How to Stop Ignoring Data Cleansing & Take Control of Your Data Quality

Your answers to these questions will help determine your roadmap and milestone planning. It will also be instrumental in helping you choose the right MDM tools or MDM solutions for the business.

Step 3: Define Data Owners & Key Roles

An MDM project is not just an IT activity. It is a business process, involving business users who are the owners of their data.

However, before assigning roles, it’s important to understand the needs of your business in relation to the master data, especially with regard to user access rights and domain ownership.

For instance, if you’re a small business with only product or CRM data to worry about, you can assign your business managers to own the data and implement standards.

On the other hand, if you’re an enterprise, like a bank, or a retail store, it’s likely that you will need a much larger number of team members to access, amend, and manage data. In such cases, it is necessary to implement organizational processes and system routines to ensure consistency.

Step 4: Call for MDM Vendor RFPs

Choosing a master data management vendor is a mind-boggling task. There are hundreds of MDM solutions in the market, each claiming a special service, feature, or technology. There are different solutions to similar problems and choosing the right one can make or break your MDM plans.

If you’re able to be specific on what you want to achieve up front and what features would be deal-breakers, you can narrow down the options more quickly and avoid using a solution that is not the right fit for your business.

Some key questions to ask when choosing an MDM solution would be:

An illustration that explains how to choose an MDM Solution
how to choose an MDM solution

Once you have answers to these questions, you can begin your search on finding the best master data management tool. Make a list of top 10 vendors and request for RFPs. Choose your vendor based on features, specific answers to your requirements, and customer service.

Step 5: Set Milestones and Measurable KPIs

It’s very easy to get lost in the overwhelming process of master data management. To stay on track, treat the initiative like a project management task.

Assign milestones and responsibilities. Measure KPIs on the achievement of milestones. For instance, record how much cost you were able to cut down by simply sending direct mails to 1,000 unique records instead of 3,000 duplicated records!

You can save thousands of dollars on postal mail alone if you have clean data!

An MDM strategy could take years to plan and perhaps tens of millions in quotations by vendors. WinPure hopes to help reduce that to 30-60 days for presales, and a total of 60-90 days to start using the solution.

What are MDM tasks and activities?

It is a general presumption that an MDM activity is just about consolidating data to provide a golden record or a single source of truth for the business.

A master data management activity, however, does more than this. Consolidation is just part of a much bigger picture.


Some of the key tasks of an MDM activity include:

  • Identifying data sources containing information about the entity to be consolidated.
  • Creating a target or expected view of the master data model for the selected model.
  • Developing scripts or using an MDM tool to pull data from multiple sources.
  • Performing data cleansing and data profiling of the extracted data.
  • Normalizing unstructured and semi-structured data.
  • Developing merging rules for record linkage by identifying key attributes.
  • Purging duplicates or redundant data.
  • Create a master index through which entity identification and resolution can be performed.
  • Load the consolidated record into a master repository.
  • Develop automation to repeat processes periodically.

The objective of an MDM task is to create a single source of truth or a golden record which is expected to be the “clean” “master” data combined from multiple sources. This marks the end of the project and thereafter business rules are created to maintain or manage the master data.

What are the Challenges of MDM Plans?

It’s crucial to note that these tasks are just the technical aspects of MDMs and they do not represent significant challenges. Businesses fail with MDM projects due to a number of business challenges such as:

14 Key Challenges of an MDM Initiative

  1. Data quality issues were largely ignored.
  2. Selection of MDM solutions that are not agile
  3. Lack of data governance policies and business rules
  4. Focus on technology acquisition over the fulfillment of business needs.
  5. Uncoordinated MDM activities initiated in isolation
  6. Issues with scope definition and poor business process planning.
  7. Lack of 100% support from C-level executives.
  8. Incorrect prediction of costs or failing to identify hidden costs.
  9. Hiring the wrong talent to lead the project.
  10. Neglecting basic risk management protocols.
  11. Loss of data during a linkage or merging attempt.
  12. Loss of meaning when data consolidation is done in isolation by IT staff
  13. Misalignment with business scope.
  14. Failing to address third-party or external data connections

MDM is an enterprise-wide activity that must go beyond the needs of any single business function, so it is important to address any recognized barriers to success. It’s not merely a task of consolidating data sources – it is a business process that requires collaboration from multiple departments, and must take into account consumer preferences, employee buy-ins, and identifying touchpoints for improvement in data recording and storage.

What are the Hidden Costs of an MDM Project Plan?

To be honest, you cannot apply a dollar value to the costs of an MDM project plan as it differs from company to company and solution to solution.

For some companies, the expenses of an MDM project can rise up to $100K/month taking into factors like software licensing, storage, implementation, talent hiring, training and maintenance, and support.

For others, it could be just $10K/month if they already have a team and are just using an affordable MDM solution to master departmental records.

MDM costs you need to calculate:

  • MDM Implementation – The software license costs for an average size implementation of an enterprise MDM solution range anywhere from $500,000 to $2 million
  • Hardware costs – servers, storage units, systems etc can amount up to $1million
  • MDM development and deployment – talent and consultancy costs can go up to $50K a month
  • Ongoing data governance – costs depend on the solution used
  • Ongoing IT and maintenance cost
  • Organizational change management

According to an article on CIO, some large organizations will need a potential investment exceeding $10 million for MDM success.

What is the ROI of MDM?

Sure, an MDM initiative is a costly affair, but the ROI on MDM overrides the costs. Here’s a neat breakdown of MDM ROI by Kelvin Loi, of Mastech Info Trellis.

A table showing the ROI of MDM

Details of this five-year MDM ROI projection are given here.

As you can see, an MDM process resolves business bottlenecks which in time does not only reduce expensive costs but also increases ROI.

Rethinking the MDM Concept & Going Beyond the Golden Record Vision

Traditionally, MDM is considered a technical activity that solely seeks to combine data sources, master them, and deliver a golden record master repository.

But now times have changed. Data is no longer an IT asset. It is an organizational asset that is owned by everyone in the organization.

The presumption that data has to be managed within siloed budgets and governed by only specific groups has led many MDM projects to failure.

It is critical for companies to rethink the MDM concept and choose to think beyond the Golden Record Vision.

Here’s how you can rethink MDM and ensure an MDM initiative results in success.

  • Ensuring Consistency: In simple words, the structure and representation of data across the organization must be consistent. For example, if customer names, location, and the number have varied data structures (Sean vs Shawn) or (UK vs United Kingdom), then merging and eliminating those variations without assessing the impact will lead to process flaws downstream. Setting consistent standards and assessing current data models against those standards will help save you from costly mistakes and process flaws during the MDM activity.
  • Collaboration b/w IT and Business Teams: The IT team is often relied on to make strategic decisions about data governance and data management, even though they are often not the owners of data. Imagine the impact of handing over sales or customer service data to the IT team who may not be familiar with commonly used business terms or other metadata descriptions – conflicts will escalate when data is merged or purged without addressing context. Business process owners need to be part of the data consolidation process because no one else understands context, relevance, and purpose better than them
  • Data Quality Management in MDM: The first step to a successful MDM plan is implementing a data quality plan. It’s imperative that organization-wide data requirements be balanced with an organizational framework for data quality assurance and management. This includes setting data quality standards, providing data quality training to people responsible for holding, storing, or entering the data, cleansing when necessary, and consistently measuring/monitoring data quality.
  • Establishing Data Governance in MDM: Putting the right amount of data governance policies in place is critical to ensuring consistency of data models and resolving data issues on time.
  • MDM for Improved Customer Experience: MDM shouldn’t be an activity just to get consolidated views. It must be tied with a key goal of improving customer experiences. For example, if your marketing or sales team is still targeting the wrong customers with irrelevant products or campaigns, your MDM initiative has failed to optimize customer experience, thereby making it a tragic failure.

Master data management is not a one-time activity. It’s not just a tool that you install and fill with data. It is a huge business undertaking that attempts to contribute to an information strategy that implements best data management practices to enable reliable, accurate, up-to-date, and comprehensive access to information and preserve information visibility.

Related: A guide to figuring out the benefits of master data management?

What are the Top Five MDM Tools for Businesses?

In this last section, we’ll cover the top five MDM solutions you can consider.

  1. IBM InfoSphere Master Data Management

IBM InfoSphere is ideal for enterprise data management. It provides advanced data matching capabilities for consolidating different data sources and gives users an up-to-date and accurate view of the data. Users get actionable insight and compliance with data governance, rules and policies across the enterprise.

Pros: It’s a cornerstone for building an ecosystem and connecting crucial data sources in the enterprise data management system.

Cons: Upgrades can be painful and requires specialized talent to use the product.

Pricing: The platform has not provided pricing information, however, according to general reports and reviews online, the monthly price can range from $31,000 – 80,000 depending on business size and a number of individual records.

  1. Informatica MDM

Informatica has always topped the ranks for best Master Data Management (MDM) software tools. It is quite popular among the large enterprise segment, where it is used to get 360-degree views of enterprise data.

Companies use Informatica to make strategic decisions about cutting costs, increasing reviews, improving business operations, and identifying hidden opportunities. It is the industry’s most trusted end-to-end MDM solution as it provides data integration, data quality, data governance, business process management, and master data management under one platform.

Pros: Offers everything you need to manage enterprise data and get 360-degree views.

Cons: Steep learning curve, excessive documentation, and requires specialized talent.

Pricing: The basic cost of an Informatica license starts from $1,000 per month. Of course, this is just a basic price. Expect higher costs depending on your data volume and business requirements.

  1. SAP Master Data Governance

SAP is amongst the top master data management solutions allowing for easy integration of supplier data residing in SAP and third-party sources. It supports data cleansing, data standardization, and data deduplication and lets you master data governance across cloud and on-premise applications. The solution is preferred by retail enterprises for its effective data workflow that allows companies to get a unified view of their business. You can pull together all your master data and manage it centrally.

Pros: Does a great job of emphasizing data governance as a crucial part of master data management.

Cons: Many reviewers have also reported a difficult user interface and desired improvements in integration.

Pricing: A subscription-based pricing that starts at $59/5000 objects/month. Additional costs include hiring SAP-certified talents to run the solution.

  1. Oracle Master Data Management Solutions

Oracle offers three different types of MDM solutions: customer data management, product data management, and enterprise data management. Its multi-entity MDM solutions are a set of purpose-built, cloud-based, SaaS applications that provide data cleansing, data standardizing, data consolidation for ERP, supply chain management, product line management, and customer experience needs.

Pros: It offers great CX data integration and provides a single, clean & AI-driven data source.

Cons: Product cost remains one of the biggest concerns for Oracle users!

Pricing: Licensing costs can go up to $150,000 for one component only. Additional technologies will require additional licenses. Software update costs can go up to $33,000.

  1. Microsoft Master Data Management Solutions

Microsoft MDM allows users to structure their data into models, establishes rules for updates, and enables better control & management of the organization’s data. Unlike other tools, MDS is designed to handle any data type and virtually any data schema.

Additionally, it also allows for specialized controls and multiple hierarchy types ready to be rapidly deployed for any domain within an organization. The biggest drawback, however, is the MDS does not do data matching, data merging, or any of the data cleansing features that are the basics of master data management. To work on data quality you will have to use the Data Quality Solution separately.

Pros: An MDS solution that can easily be implemented by business users with no programming knowledge.

Cons: Reviewers have complained about an outdated UI and lack of compatibility with heterogeneous environments. Additionally, it also lacks a high-quality data matching and merging tool which is the backbone of a data management solution.

Pricing: The MDS alone is a free product that comes with the complete package of the Microsoft SQL Server. You will get the MDS in the SQL Server Pro version or the licensed version of SQL Server. You will also get the SSIS and DQS.

Note: These solutions are million-dollar expenses and are only recommended for retail enterprises that have a very clear vision and accurate specifications of what they need from an MDM solution. These tools are not recommended for young startups, small businesses, or departmental MDM due to their excessive costs, complexity, and demand for specialized talent.

Not Ready for Complex, Expensive MDM Solutions? WinPure Can Help You Kickstart an MDM Pilot Project

As we said earlier, MDM is an expensive endeavor and not one that you should get into lightly. Plus, in order to get approval from C-level decision-makers, you will have to prove your case for MDM.

In that case, WinPure’s master data management solution can help you kick off a pilot project where you can master the records of one department or one entity to determine your next steps. For enterprise organizations where an MDM project can take months to plan and get approvals, WinPure can accelerate the process faster with a test run.

On the other hand, if you’re a small or medium-level business that does not have up to $10 million to invest in MDM projects, you can use an affordable solution like WinPure to achieve your MDM goals.

Here’s everything you need to know about WinPure’s MDM solution and how it can help you achieve a single view of your data without impacting your business processes or draining your budget.

Get in touch to see how we can help you with making sense of your data through a simple, easy-to-use, affordable MDM solution. 

  • Hidden
  • This field is for validation purposes and should be left unchanged.


By Michelle Knight & Farah Kim | June 1st, 2022 | Posted in Master Data Management (MDM)

About Michelle Knight & Farah Kim

Michelle Knight has a background in software testing, a Master's in Library and Information Science from Simmons College, and an Association for Information Science and Technology (ASIST) award. At WinPure, she works as our Product Marketing Specialist and has a knack for explaining complicated data management topics to business people. Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. Farah holds a BS degree in Computer Science and a MA degree in Linguistics. She is fascinated with data management and aims to help businesses overcome operational inefficiencies caused by ineffective data management practices.

Any Questions?

We’re here to help you get the most from your data.

Download and try out our Award-Winning WinPure™ Clean & Match Data Cleansing and Matching Software Suite.

WinPure, a trusted innovator in Data Quality and Master Data Management Solutions.
Join the thousands of customers who rely on WinPure to grow faster with better data.