Master Data Management (MDM) projects in life sciences often aim to create a single source of truth for customer data. With customers ranging from healthcare providers (HCPs) and practices to wholesalers, distributors, and hospitals, MDM is positioned as a key enabler for data-driven decision-making, providing an analytic backbone across a variety of transactional internal and external datasets. However, many such initiatives fall short of their objectives. As a solution architect with 30 years’ experience in customer data management, I’ve seen these failures firsthand and can trace them back to a few recurring pitfalls that could be mitigated with a strategic approach.

1. Inability to Accommodate Customer Nuance and Roles

In life sciences, customers are multifaceted. A single customer can take on roles like sold-to, ship-to, payer, buyer or influencer, depending on the transaction. Additionally, customers often operate across different classes of trade¹, such as retail and specialty pharmacy, and some participate in multiple classes, like 340B² and commercial. In the specialty and rare disease space, outpatient treatment centers and infusion centers contribute heavily to this account classification challenge.

MDM projects frequently fail because they attempt to simplify these nuances rather than building a model flexible enough to handle them. This leads to misaligned data structures and creates confusion when data is integrated across systems and business processes, ultimately hampering cross-functional collaboration.

2. Failure to Recognize and Adapt Business Rules Across Units

Each business unit and brand within a life sciences manufacturer often has its own unique set of business rules for interacting with customer data. These rules may vary depending on factors like therapy area, product lifecycle, distribution model, product differentiators, or even regional regulatory requirements. A common mistake in MDM projects is applying a one-size-fits-all approach when codifying master data processes. This failure to recognize and adapt to the specific needs of different units can lead to user resistance, misaligned data models, and inconsistent data quality, eroding the trust in the MDM system.

3. Lack of Centralized Data, Key Identifiers, and Automation

A robust MDM system should consolidate customer data into a single, centralized repository, ensuring consistent and accurate information. However, many projects fail to establish centralized master data, particularly when it comes to key identifiers like DEA, State License, HIN, and NPI numbers. Instead, these identifiers are often managed separately in various systems, resulting in duplicated maintenance and data hygiene and conflicting data across platforms. This lack of centralization is often compounded by a failure to automate data ingestion and updates, leaving teams to rely on manual processes. Without automation, keeping master data up-to-date becomes labor-intensive, prone to errors, and slow to respond to evolving business needs.

4. Proliferation of Alternate MDMs That Should Be Subordinate

Without a clear central authority in master data management, alternate MDM systems often spring up across different departments or business units. These alternate systems, designed to meet specific local needs, become entrenched, creating a fragmented data landscape. Instead of acting as subordinate, specialized repositories that feed into or consume from a centralized MDM, these systems often compete with the primary master data source. This leads to data inconsistencies, conflicting records, and a lack of trust in the central MDM system, as users become unsure which version of the data is correct. When too many systems claim to be the “source of truth,” the whole purpose of MDM is undermined.

5. Overloading the Master with Too Much Data

Another pitfall is the tendency to capture too many data points within the master data set. While it may seem advantageous to store as much information as possible, this often includes temporal data that changes frequently, such as pricing or point-in-time eligibility data. Incorporating such dynamic information into the master data repository undermines its stability, turning it into a constantly evolving data store rather than a reliable foundation. This creates challenges in data governance and impacts the usability of the master data for analytical purposes.

6. Strangling the System with Overly Strict Data Governance

While data governance is crucial for ensuring accuracy and compliance, overly strict governance frameworks can choke the agility of MDM systems. For example, a rigid “four-eye” principle, requiring multiple layers of approval for every data update, can lead to significant delays. When these governance rules are too rigid, they slow down the speed at which customer data can be updated, preventing the MDM system from keeping up with business needs. This results in frustration among stakeholders, reduces user adoption, and causes reliance on workarounds or shadow systems.

7. Confusing Source Data with MDM Data

A common mistake in MDM projects is assuming that external, authoritative source data (such as third-party provider databases or market-purchased datasets) can serve as the master data repository. While buying high-quality source data is a crucial first step, managing that data to fit your business’s unique rules, processes, and systems is far more effective when done within your own MDM framework. External sources rarely capture the complete picture of how a customer interacts with your company.

For example, critical customers or data points often first emerge in your own transactional systems, such as syndicated sales, dispense, patient services. claims or CRM data, long before they are recognized by the source. In a competitive environment, waiting for external sources to catch up could mean missing valuable opportunities. Moreover, source data should be treated as an input—enriched and transformed to fit your company’s context. Confusing the source with the MDM can lead to inconsistencies, as external datasets will not align perfectly with your business needs. A properly managed MDM system incorporates external data while ensuring that internal transactions and proprietary data play a central role in maintaining the accuracy and relevance of your master records.

The Path Forward: A Strategic and Adaptive Approach to MDM

To avoid these pitfalls, life sciences manufacturers need to adopt an MDM strategy that balances flexibility with governance and ensures accessibility. This means designing data models that accommodate the varied roles of customers, recognizing the unique business rules across units, and maintaining centralized control of key identifiers without overburdening the system with volatile data. Streamlining governance processes to align with business agility and incorporating automation for data updates is also essential. Moreover, ensuring that alternate MDM systems act as feeders and/or consumers, rather than competitors, to the central repository can restore trust in the master data’s integrity.

Master Data Management in life sciences is inherently complex, but with a strategic, tailored approach, companies can overcome the challenges that have plagued many initiatives. As a consultant in this space for 30 years, I’ve seen the difference it makes when MDM programs are built with the realities of the business in mind—recongnizing hierarchies and relationships as well as eliminating duplicative or temporal data—and the payoff is well worth the effort.

Is your MDM working for you, or causing friction? Contact us today for a no obligation consultation on your master data ecosystem.

Footnotes

Class of trade: This refers to the categorization of customers based on the type of business they conduct. In life sciences, classes of trade may include retail pharmacies, hospitals, specialty pharmacies, wholesalers, and physician practices. Each class often has distinct pricing, regulatory, and operational considerations.
340B: A U.S. federal program that allows eligible healthcare organizations, such as hospitals and clinics serving low-income or uninsured patients, to purchase outpatient drugs at significantly discounted prices. The program is designed to help these entities stretch federal resources and provide more comprehensive services to underserved populations.

Why Master Data Management Projects Fail in Life Sciences: A Consultant’s Perspective