What is Master Data Management?
Master Data Management (MDM) is a comprehensive methodology that focuses on identifying, governing, and maintaining an organization’s most critical data assets. It’s not just a technology solution, but a combination of tools, processes, and governance frameworks designed to create a single, trusted view of business-critical information. This article talks in detail about Master Data Management and it’s significance.
Breaking Down Master Data
Master data refers to the core, non-transactional data entities that are essential to an organization’s operations. These typically include:
Material data – Raw materials, components, specifications, substitution rules, compliance information, etc.
Customer data – Names, contact information, preferences, purchase history, and demographic details
Product data – SKUs, descriptions, specifications, pricing, and categorizations
Employee data – Personnel information, roles, reporting structures, and credentials
Supplier/vendor data – Company information, contracts, and relationship history
Financial data – Chart of accounts, cost centers, profit centers, legal entities, financial hierarchies, etc.
The very idea of implementing a Master Data system, one that relies on a “golden record” as a single source of truth is to enhance efficiencies, smoothen and simplify internal processes as they scale.
Broadly speaking, the challenges faced can be bifurcated into several non-exclusive buckets as detailed below.
1. Challenges while introducing an ERP system or migrating to a new version of an existing source system
2. Difficulties specific to maintaining the sanity of the data during operational phases
3. Technical and process driven-challenges
4. Strategic and Managerial Challenges that are inherent to large companies
The goal of this article is to list down some of these constraints specific to implementing or scaling MDM and offer the best possible solutions and course of actions that organizations can consider to remedy the issues.
Below are the key technical challenges organizations’ might face with their existing master data:
Technical Challenges
Due to the sheer volume of data that large organizations generate on a daily basis, datasets in the ERP master can grow to be enormous, leading to several challenges that are largely technical in nature
1. Resource Intensive Data Normalization
Organizations looking to fix poor-quality data often find that no single logic can standardize complex master data—especially for materials and MRO parts with unique, property-rich datasheets.
Without clear governance, data issues vary widely and can’t be resolved at scale with traditional rules alone. As a result, companies rely heavily on manual, human-led normalization. Industry-specific nuances further complicate this, requiring tailored processing approaches.
This is why legacy MDM firms often depend on large teams of manual analysts, typically based in low-cost regions, to manage and standardize data effectively.
In the span of five years, the usage of Artificial Intelligence in business processes has fundamentally changed Master Data Management. Yes, there are several issues like Hallucinations and the natural challenges of training an AI-model on several supplier, part and product data across several industries, but this is changing rapidly.
These changes are happening so fast that within a short span of 1 year, we at Verdantis, have moved our master data cleansing operations from human-driven to AI-driven and human-approved
This means that not only does the model ensure data normalization is much more efficient, but it also continuously trains itself on the proper way to manage individual master data records.
In any case, here are a few resource intensive data normalization challenges that are inherent to most ERPs
1. Duplicate Identification – Identifying L1 duplicates across Vendor, Customer and MRO master by using properties like Manufacturer Part Number, Supplier TIN (tax identification number) and customer emails. Identifying L2 duplicates by using a confidence score generated by AI.
2. Normalizing Data – Structuring loose text into clear, predefined fields for standardized, clean and automation-ready data. For example; normalizing company names or website addresses to one final standard format, Normalizing the Units of Measure for spare parts into one global unit of measurement.
3. Extracting Attributes – Extracting key information of a given master record. For example – Extracting Supplier’s city from the address, extracting part specifications from product description etc
Example: Normalizing MRO Spare Parts Data in a Global Manufacturing Firm
A global manufacturing company operating across North America, Europe, and Asia maintains over 300,000 material master records, including MRO spare parts, sourced from thousands of suppliers. Over the years, these records were entered by different teams across regions—each using their own naming conventions, units of measure (e.g., “mm” vs. “inches”), and languages. A single part like a hydraulic valve might be listed as:
HYD. VLV 2IN STEEL
Valve, Hydraulic, 2”
2-inch hydraulic valve, SS
With no standard format or taxonomy, the company struggled to identify duplicates, causing overstocking, maverick spend, and maintenance delays.
2. Disparate Systems
It is common for many to assume that companies usually work solely on one ERP system so an integration with that system will ensure total harmony of operations.
This is hardly the case though, several enterprises use multiple ERP source systems particularly across Geographies since each of them offer different advantages depending on the location, language and systemic requirements.
Moreover, it is not uncommon for companies to support older as well as newer systems of the same ERP or EAM system, thus amplifying the problem.
Since master data in each of these source systems may have been configured differently, the only logical solution in such cases is to integrate all the databases into one central Master Data Management Software.
Example: A global company might run SAP in Europe, Oracle in the U.S., and a homegrown tool in Asia. Without unified master data governance, the same material could be listed three times with slight variations—causing inaccurate inventory levels, order delays, and financial reporting errors.
3. Data Management & Sensitivity
1. GDPR Compliant – Managing master data, especially specific to customers and prospects, can be tricky given regional and national laws like CAN SPAM, GDPR
2. Data Laws – Key data records, especially in some asset-intensive industries is governed by separate company-specific policies to prevent proprietary theft
3. Sensitive Data – Master data records for some industries and companies are particularly sensitive, like defense, government owned organizations etc, which makes data management
Things get complicated further when first party information needs to be consolidated from multiple non-ERP systems like a CRM software, third party asset maintenance software, data platforms etc.
Example: Mishandling Sensitive Customer Data in a Global Retail Company
A global retail company rolled out customer data across the regions where they operated but did not put the appropriate access controls in place to the data. For example, sensitive fields like opt-out preferences and purchase history were all accessible to multiple teams. Therefore, a European customer who opted out under GDPR ended up receiving a marketing email from their U.S. system, which then launched a compliance investigation. The payment vendor’s banking details were active in unrelated departments. In other words, there was a lack of role-based access and data ownership, which revealed regulatory and major internal risk.
4. Missing Information
Due to either human errors or unavailability of information, some Master Data Records may simply not contain information or data that should have been mandatory.
Many other records may not contain information on fields that could have been useful but not mandatory.
This means that for the data to be of any tangible use, the records would have to be enriched with the relevant information and data.
Like data standardization, this was also extremely resource-intensive, led to cost overruns and was difficult to resolve in a short turnaround time before the advent of Artificial Intelligence.
Please watch this short video in order to learn more about our AI Enabled Auto Enrich AI
Now, AI models like Auto Enrich AI can autonomously crawl and analyze the open web, several hordes of supplier catalogs and organization’s internal data to enrich data individual records at scale.
5. Data Silos and System Integration Issues
Most organizations grow organically, accumulating different systems for various functions—CRM for sales, ERP for operations, specialized tools for marketing, and so on. Each system becomes a potential silo, storing its own version of customer, product, or vendor information.
The challenge lies in integrating these disparate systems to create a unified view of data without disrupting existing business processes. In one real-world case, a telecommunications company we worked with discovered they had the same customers represented in seven different systems, each with conflicting contact information and service details. This led to redundant marketing efforts and confusion in customer service.
Example: Vendor Data Silos in Manufacturing
A global manufacturing firm using different ERP systems across regions had fragmented vendor records—same suppliers listed with different names, contacts, and terms. This lack of integration caused duplicate payments, sourcing delays, and poor visibility into vendor performance. The absence of a unified master data view led to operational inefficiencies and increased costs.
Managerial & Strategic Challenges
Arguably some of the tougher problems to solve in MDM, These issues mainly stem due to poor-governance protocols and natural errors due to human-errors and a the absence of clearly charted approval workflows.
1. Data Stewardship
Enterprises in their growth-phase are plagued with poor data stewardship standards and protocols. The very idea of setting up a standard operations procedure for something as rudimentary as supplier or vendor data may seem like a foreign and rudimentary concept.
However, it’s a wise move and one that can eliminate the need for repetitive and expensive data corrections.
The process begins top-down though, an organization that is focused on making data driven decisions will naturally drive “data stewardship” top-down and across the board; an organization that views it simply as a checklist item is far more likely find itself in the midst of an ERP disaster due to data management issues.
Once defined, technology based solutions can enforce data stewardship by mandating data in required fields, validating the same, converting formats and units-of-measure into standardized datasets.
1. Inconsistent Taxonomies
2. Conflicting Data Sheets
3. Unclear U-O-Ms
2. Governance and Ownership Complications
Who “owns” the customer data—marketing, sales, or customer service? Who decides how product information should be structured—merchandising or the supply chain team?
The challenge lies in establishing clear data governance frameworks that define ownership and create accountability for data quality. Without this clarity, data management initiatives often fall apart because no single team feels responsible for ensuring data accuracy and completeness.
3. Business Team Buy-In
It is easy for journalists, vendors and software firms to claim that “Data is the new Oil”. However, the ground reality is that most business teams struggle to see first-hand, the value of abundant, relevant and complete data.
While the advantages of clear, reliable and consistent data is well-known, business teams generally aren’t able to see the value from a birds-eye-view.
Master Data, especially in asset-heavy industries, tend to be pervasive across organizational functions and affect maintenance, procurement, production, supply chain, product, sales and demand management teams.
For each of them to upload, update and be accountable for what seems like a redundant and repetitive task seems to be a tall order. Hiring separate personnel for the same is another investment that draws the ire of finance and HR teams.
However, with technology being more democratized today, business teams are increasingly understanding the value of building reliable datasets that can be trained to improve tangible outcomes. Organizations can proactively schedule demos, discussions, workshops and success stories that clearly validate the value of investing in a data-first culture.
Unfortunately, this is one such challenge that requires leadership and managerial buy-in and no technology or software based solution can expedite or solve this.
1. Alignment between Business & IT Teams
2. Deployment Timelines
3. Resistant to Change
4. Approvals & Reviews
Approval processes and reviews are essential for maintaining master data quality, but they often create operational and strategic bottlenecks. Delays arise from unclear roles, lack of urgency, and ambiguous ownership—especially when critical updates like new supplier entries or item creation are needed.
Without a consistent framework, different business units handle approvals and validations differently, leading to uneven data quality and weakened enterprise governance. Additionally, the absence of audit trails, documentation, and role clarity increases compliance risks and reduces visibility into who approved changes and why.
To overcome these issues, organizations must adopt standardized, automated workflows that enforce business rules and ensure accountability. When master data governance is built into a centralized system, it creates traceable processes with consistent, timely reviews and approvals across departments.
Such solutions not only streamline decision-making but also enhance data accuracy and regulatory compliance. Purpose-built software tools for master data governance support this by promoting structured management, traceability, and transparency—critical for achieving high-quality, reliable data at scale.
5. Multi-Domain Master Data Effectiveness
As discussed earlier, a centrally-managed master data aids process optimization and effectiveness across teams, and the value it drives, even in silos is undisputed.
However, for driving real, tangible value by virtue of a clean Master Data, it’s effectiveness lies in merging the information available across multiple master data disciplines.
Unfortunately, very few enterprises are equipped enough to make sense of Master Data across business functions while most adopt these practices across a few select disciplines like procurement, supply chain or supplier master
These are some of the leading and most prominent difficulties with Master Data Management. We’ve covered some more pointers below for a more comprehensive list of challenges that enterprises generally face.
How to Overcome These Challenges
Start Small and Scale Strategically
Rather than attempting a company-wide MDM implementation all at once, consider:
- Beginning with one critical data domain (like customer or product data)
- Focusing on high-impact business processes
- Demonstrating value before expanding
Invest in Data Quality Tools and Processes
Modern data management platforms offer powerful capabilities for:
Automated deduplication
Standardization of formats and values
Data enrichment from trusted sources
Ongoing data quality monitoring
Establish Clear Governance Structures
Effective MDM requires:
- Defined data ownership roles and responsibilities
- Clear procedures for data changes and updates
- Regular data quality reviews
- Cross-functional data governance committees
Balance Technology and Process Improvements
Remember that MDM is not just a technology challenge:
- Technology enables better data management
- But processes and people determine success
- Training and change management are essential components
Tie MDM to Business Outcomes
The most successful MDM initiatives directly connect to business goals:
- Improved customer experience
- More effective marketing
- Enhanced operational efficiency
- Better regulatory compliance
- More accurate reporting and analytics
Conclusion: The Path Forward
Master Data Management challenges can be complicated. That said, they are not impossible to overcome. If your organization takes a strategic approach to MDM (governance, technology, and focus on business outcomes), then you can convert data from a burden into a competitive advantage.
MDM is a must for every company that deals with data. This is despite the common challenges of master data management. However, if you knew what challenges you might face in the implementation process, you would be able to identify and prevent those issues before they turn into problems.
In an environment that hears data referred to as ‘the new oil‘, MDM will be the refinery turning the raw data into valuable business information. By learning about, and tackling the following challenges, organizations will carve out a clear path so they can maximize the value of their data to make better, more confident decisions.
The often–difficult journey towards effective Master Data Management is a worthwhile endeavor for organizations that overcome their challenges and realize clearer data, leaner operations, and greater adherence to digital transformation initiatives.
Master Data Management challenges can be complicated. That said, they are not impossible to overcome. If your organization takes a strategic approach to MDM (governance, technology, and focus on business outcomes), then you can convert data from a burden into a competitive advantage.
MDM is a must for every company that deals with data. This is despite the common challenges of master data management. However, if you knew what challenges you might face in the implementation process, you would be able to identify and prevent those issues before they turn into problems.
In an environment that hears data referred to as ‘the new oil‘, MDM will be the refinery turning the raw data into valuable business information. By learning about, and tackling the following challenges, organizations will carve out a clear path so they can maximize the value of their data to make better, more confident decisions.
The often–difficult journey towards effective Master Data Management is a worthwhile endeavor for organizations that overcome their challenges and realize clearer data, leaner operations, and greater adherence to digital transformation initiatives.
