Just as the engine of a car controls its overall performance, MDM controls the usage of information throughout the enterprise.
In today's AI-driven business landscape, master data management (MDM) has evolved from a critical infrastructure component to an essential foundation for generative AI, agentic systems, and intelligent automation. Modern MDM programs don't just support 360-degree customer views—they enable AI models to ground their outputs in trusted enterprise data, prevent hallucinations, and deliver accurate, compliant automation at scale.
The stakes have risen dramatically. While past MDM initiatives focused on eliminating duplicate records and improving data quality for business intelligence, today's programs must serve as the authoritative knowledge layer for:
Mastering product, customer, and organizational data now directly impacts AI performance. If your taxonomies are fragmented, your metadata is inconsistent, or your governance is weak, AI agents will amplify those flaws—making bad decisions faster and at greater scale.
In this updated guide, we describe eight major components of a modern MDM program. Whether your company is just starting its MDM journey or is already implementing AI-powered workflows, you will find insights that help you properly design and govern your MDM program for the AI era. Although not all of these points need to be resolved in the early stages of discussion, they should be addressed before the project launches. The steps below are outlined in the order in which they should be done, but some can be executed in parallel or with overlap.
Without a data governance program in place, the progress of an MDM initiative is likely to be disrupted.
Many companies hesitate when consultants propose comprehensive data governance programs because they appear large and costly. However, the advantages of having governance in place—improved data quality, regulatory compliance, and AI readiness—far outweigh the initial investment. By establishing a strong data governance program before launching MDM, implementation can be streamlined. Without governance, MDM initiatives frequently stall as issues like data ownership, validation rules, and AI safety policies must be resolved on the fly, forcing teams to revise previous work.
A data governance program can start lean with just a small group of data stewards. The focus at this stage should be on key elements:
Data Ownership
Clearly define who owns each data domain (customer, product, location, supplier). In the AI era, owners must also approve how their data is used to train models, ground AI outputs, or feed agentic workflows.
Data Lineage
Track where data originates, how it moves through systems, and who transforms it. Lineage is now critical for AI explainability, regulatory audits, and troubleshooting model drift.
Data Quality Standards
Establish rules for completeness, accuracy, consistency, and timeliness. Poor data quality leads directly to AI hallucinations and faulty automation.
AI-Specific Governance
Define policies for:
During initial governance work, duplicate records will be identified and business rules for consolidating master data should be defined. This ensures everyone shares the same understanding of what MDM is intended to accomplish.
We consistently discover during early discussions that users have conflicting views on fundamental terms, including basics like:
These issues can be resolved through data governance, and it is critical to do so before the MDM program launches.
The number of data stewards needed depends on:
For example, in healthcare or financial services, match confidence must be very high before data is released to end users or AI agents. As the program matures and match quality improves, governance can expand to cover data enrichment, AI model oversight, and cross-domain integration.
This step is a must-have for successful MDM implementation. Organizations that skip data governance or fail to solidify it early are forced to address it after the first major issues arise—which is more expensive and by that time, opportunities and customer trust will be lost.
Understanding where your organization is in the journey and what value MDM will bring allows you to deliver results throughout the program. Whether implementing single-domain or multi-domain MDM, meeting the needs of data users—including AI systems—must always be the first priority. This means involving users early, listening carefully, choosing the right data sources, and developing an effective interface. It's challenging work, but one that pays off both in the near term and as your organization scales.
An MDM program without a clear vision and objective is unlikely to be funded, as executives will not understand the value it brings.
What problem are you trying to solve? Sometimes the stated objective of an MDM program can be too generic: "Create a golden record" or "Create a 360-degree view of the customer." But what do you mean by 360-degree view? What problem will it solve? What value will it bring? How will it move the organization closer to strategic goals?
An MDM program without a clear vision is unlikely to be funded because executives won't understand its value. Alternatively, decision-makers may have misconceptions about what MDM is—believing it's simply a data warehouse or reporting project. This leads to either:
Because of MDM's hybrid nature (IT infrastructure + business value), there are often debates about whether budget should come from IT or business units.
The main purpose of an MDM program is to create master data and make it available to end users, applications, and AI systems. MDM provides enterprise-wide infrastructure to:
Eliminate Duplicate Records
Duplication typically arises from:
Users unknowingly create duplicate records when systems can't locate existing entries. Master data helps navigate duplication issues across all domains.
Enable AI and Automation
Modern MDM supports:
Augment and Enrich Data
MDM supports augmentation from internal systems or external vendors. For example:
An MDM program is not a data quality program. Data will be cleansed as part of implementation, but only for the purposes of matching records to create master data. An MDM program that focuses too intensively on data quality will bog down implementation. Overarching data quality initiatives should be addressed as separate programs.
MDM programs are typically implemented by IT, and business stakeholders may find it difficult to understand the need or appreciate the complexity. Too many organizations adopt a "build it and they will come" mentality—getting data into the hub to create golden records, then figuring out what to do with it afterward.
The full range of business stakeholders should be engaged early to:
An MDM hub can be used for operational or analytical purposes—or increasingly, for AI-powered automation. The choice significantly impacts architecture, integration patterns, and governance requirements.
Operational MDM (sometimes called transactional MDM) is used by core business systems:
Users retrieve master data from the hub or create new data to be fed into it. Because operational systems require current information, updates typically happen in real-time or near-real-time.
The challenge: Operational MDM relies heavily on integration technologies, which can affect performance. Modern architectures use:
AI Use Cases:
Analytical MDM supports decision-making and insights. Data may be stored in:
Updates are typically handled via batch processes (daily or multiple times per day), though streaming analytics are becoming more common. Hierarchies and relationships are more important in analytical MDM than in operational MDM because they enable:
AI Use Cases:
Modern organizations increasingly need both operational and analytical MDM to support AI initiatives:
Example: A customer service AI agent might:
The important thing [when designing the UI] is to consider actual usage.
The producers, consumers, and owners of master data may differ depending on whether the system is used for operational, analytical, or AI-powered purposes.
Many organizations want a custom user interface (UI) in addition to the data steward interface that comes with most MDM products. IT executives often feel they can use a custom UI to "show business executives the magic of MDM."
However, this capability brings little value on its own. The critical question is: How will users actually access and use this data?
For internal operational purposes (customer service, sales, production), consider:
An additional factor is usage by external customers:
Privacy and Security Considerations:
For analytical MDM, users typically access data through:
The IT department normally addresses these requirements, but users should be involved early to:
Modern MDM must support AI systems as first-class data consumers:
For LLMs and RAG Systems:
For Agentic AI:
For Machine Learning:
An MDM program that focuses too intensively on data quality will bog down the implementation.
The objective of your MDM program will dictate which data sources are loaded into the hub. The primary driver for most MDM implementations is resolving duplication or creating golden records from multiple sources.
MDM is essential when:
As a general rule: If a data source has no alternate, duplicate source and is not augmenting any data, an MDM solution may not be necessary—the match and merge functionality isn't needed. A master hub can be created without MDM software.
Example: If there's only one source of customer data with no duplication issues, match and merge aren't required, nor is a data steward interface needed.
Cloud-Based Systems
Many organizations now have data scattered across:
APIs and Microservices
Modern architectures generate data across distributed services. MDM must integrate with:
Third-Party Enrichment
Common external data sources include:
Critical decisions about historical data must be made upfront:
How many years of data will be included?
Are only active records being included?
Data augmentation timing:
For Training AI Models:
For Real-Time AI:
For Agentic Systems:
The main purpose of MDM is to match and merge records to create a single master record, not to build a data warehouse.
One of the most challenging questions every organization faces is defining the data domain. These definitional issues directly impact software licensing, storage costs, stakeholder involvement, and implementation timelines.
Customer:
Product:
Location:
Getting consensus across business units is extremely difficult and time-consuming. However, defining terms should be a priority because it will dictate:
Master data should be:
Examples of master data:
Examples of transactional data (NOT master data):
Drawing the line can be difficult. Ask yourself:
Master data serves as the foundation for AI grounding:
For RAG Systems:
For Agentic AI:
For Model Training:
The linkage of the elements of the master data is just as valuable as the data itself.
In many organizations, hierarchies, relationships, and groupings are treated as afterthoughts, decided during implementation. However, defining how master data elements relate to each other is critical for selling the value of MDM to end users—and especially for supporting AI systems that need contextual understanding.
This information is typically not available in other internal applications or from third parties, so identifying which are "must-haves" versus "nice-to-haves" will influence:
Hierarchies are typically more useful for analytical MDM than operational MDM, though they're increasingly important for internal departments and AI systems.
Common Hierarchy Types:
Customer Hierarchies:
Product Hierarchies:
Organizational Hierarchies:
Multiple hierarchies may exist for the same data. For example, customer hierarchies might differ based on:
Sources for Hierarchies:
AI Applications:
Relationships between master data elements are extremely valuable but often can only be captured manually, especially party-to-party relationships in the customer domain.
To decide whether relationships should be captured in MDM, ask:
Common Relationship Types:
Customer Relationships:
Product Relationships:
Cross-Domain Relationships:
AI Applications:
Groupings are similar to relationships and many MDM systems handle them identically internally. The most valuable groupings include:
Household Grouping:
Geographic Grouping:
Behavioral Grouping:
AI Applications:
Knowledge Graphs:
Modern MDM increasingly leverages knowledge graphs to represent complex, multi-dimensional relationships. This is especially valuable for:
Dynamic Groupings:
AI-powered MDM can automatically identify and maintain groupings based on:
Start with a small number of sources and provide meaningful information to the business as soon as possible.
The best implementation plans start small and deliver meaningful business value quickly. Problematic plans tend to be either:
Phase 1: Quick Win (3-6 months)
Phase 2: Expand & Integrate (6-12 months)
Phase 3: Scale & Optimize (12-24 months)
1. Achieve Quick Wins
When a project delivers specific objectives quickly, the business sees MDM value even if scope is small. This provides justification for continued funding.
2. Prepare a Long-Term Roadmap
Once initial success is achieved, prepare a roadmap showing how subsequent stages will be accomplished, consistent with strategic objectives.
3. Engage Business Stakeholders
MDM programs require employees on both business and technology sides to work together. Involve business users from day one.
4. Govern from the Start
Don't defer governance to "phase 2." Bake it into the pilot. This includes:
5. Plan for AI Integration
Even if not implementing AI immediately, design MDM architecture to support future AI needs:
❌ Scope Creep
Starting with "just customer data" but gradually adding:
Solution: Maintain strict scope discipline. Park additional requirements for future phases.
❌ Perfection Paralysis
Waiting for 100% data quality before launching.
Solution: Set realistic quality thresholds (e.g., 95% match accuracy) and improve over time.
❌ Technology-First Thinking
Selecting MDM software before defining business requirements.
Solution: Start with business outcomes, then choose technology to support them.
❌ Ignoring Change Management
Assuming users will automatically adopt new processes.
Solution: Invest in training, communication, and incentives for adoption.
Cloud-Native Deployment:
AI-Ready Architecture:
Agile Delivery:
The development of an MDM program is a journey that requires careful preparation. Like all programs, it should be thoughtfully planned before implementation begins.
In 2025, MDM is no longer just about eliminating duplicates or creating golden records—it's about building the authoritative knowledge foundation that enables AI systems to:
An MDM program requires employees on both the business and technology sides to work together to solve a variety of challenges. Proper planning and strategy ensure program success.
1. Governance First
Establish data governance before launching MDM. This prevents costly rework and ensures AI systems operate on well-governed data.
2. Clear Objectives
Define specific business problems MDM will solve. Generic goals like "360-degree view" are insufficient for securing funding and measuring success.
3. Right-Sized Scope
Start with operational OR analytical focus (or hybrid for AI). Expand thoughtfully based on business value.
4. User-Centric Design
Design for how users (humans and AI systems) will actually access and use master data.
5. Quality Over Quantity
Focus on high-quality data for critical domains rather than mediocre data across all domains.
6. Relationships Matter
Hierarchies, relationships, and groupings are as valuable as the data itself—especially for AI applications.
7. Iterative Delivery
Deliver quick wins, then build on success. Avoid "big bang" approaches.
8. AI Readiness
Design MDM architecture to support current and future AI initiatives, even if not implementing AI immediately.
Although every company and situation is different, the eight steps described in this white paper provide a solid roadmap for a successful MDM journey—one that will serve as the foundation for your organization's AI-powered future.
Earley Information Science is a professional services firm dedicated to helping organizations become AI-powered, customer-driven enterprises. For over 25 years, we've helped leading organizations design and implement the information architecture, taxonomy, and data management foundations that make AI initiatives successful.
Our expertise spans:
We work with Fortune 500 companies across retail, pharmaceuticals, manufacturing, and financial services to transform how they structure, govern, and activate their data and content.