Launching a Master Data Management Program: Eight Key Steps in the Journey

INTRODUCTION

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:

• Large language models (LLMs) and retrieval-augmented generation (RAG) systems
• Agentic AI workflows that make autonomous decisions
• Real-time personalization engines across digital and physical channels
• Compliance frameworks like GDPR, CCPA, and AI governance regulations

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.

STEP 1: CREATE A DATA GOVERNANCE PROGRAM AHEAD OF THE MDM ROADMAP

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:

Core Governance 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:

• Which data can be used to train AI models
• How AI systems access master data (real-time vs. batch)
• Confidence thresholds for automated decisions
• Human-in-the-loop requirements for high-risk actions

Early Governance Activities

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:

• "Customer" (business customer vs. individual consumer vs. prospect vs. account)
• "Product" (SKU vs. product family vs. service offering)
• "Active" (purchased in last 12 months vs. opted-in to communications vs. has open contract)

These issues can be resolved through data governance, and it is critical to do so before the MDM program launches.

Scaling the Governance Team

The number of data stewards needed depends on:

• The quality of existing data
• The organization's risk tolerance
• Whether data feeds AI systems that make autonomous decisions

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.

Why This Step Is Critical

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.

STEP 2: SET CLEAR OBJECTIVES

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:

• No funding, or
• Perceived failure when outcomes don't match expectations

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 Core Purpose of MDM

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:

• Standardize data from disparate sources
• Integrate information with similar or duplicate attributes
• Establish an authoritative source for business operations and decisions
• Ground AI models in trusted, governed data

Common Business Drivers for MDM

Eliminate Duplicate Records
Duplication typically arises from:

• Multiple order entry, CRM, or sales automation systems
• Mergers and acquisitions over time
• Separate departments with independent systems
• Legacy systems lacking edit checks or search functionality

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:

• Retrieval-augmented generation (RAG) for LLMs
• Agentic AI workflows that need authoritative data
• Real-time personalization engines
• Predictive analytics and machine learning models

Augment and Enrich Data
MDM supports augmentation from internal systems or external vendors. For example:

• Matching customer records against third-party corporate hierarchies (D&B, ZoomInfo)
• Enriching product data with industry classifications or competitive intelligence
• Adding demographic or firmographic attributes for segmentation

What MDM Is NOT

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.

Engaging Stakeholders Early

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:

• Educate them about MDM and the value it brings
• Gather requirements for how they'll use master data
• Align MDM objectives with strategic business outcomes
• Establish clear success criteria tied to measurable KPIs

STEP 3: CHOOSE A FOCUS FOR THE MASTER DATA HUB

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

Operational MDM (sometimes called transactional MDM) is used by core business systems:

• Sales, service, order management
• Manufacturing, purchasing, billing
• Accounts receivable and payable
• Customer-facing websites and mobile apps
• AI agents that take real-time actions

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:

• Event-driven patterns (Kafka, event streams)
• API-first design
• Microservices with service mesh
• Caching layers for frequently accessed data

AI Use Cases:

• Real-time product recommendations
• Dynamic pricing engines
• Chatbots grounded in customer master data
• Agentic systems that update CRM or ERP records

Analytical MDM

Analytical MDM supports decision-making and insights. Data may be stored in:

• Data warehouses or data lakes
• Data marts for specific business units
• Feature stores for machine learning
• Vector databases for AI retrieval

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:

• Customer segmentation and cohort analysis
• Product affinity and cross-sell opportunities
• Organizational hierarchy navigation
• AI model training and evaluation

AI Use Cases:

• Training customer lifetime value models
• Building recommendation engines
• Feeding RAG systems for enterprise search
• Creating knowledge graphs for agentic AI

AI-Powered Automation (Hybrid Approach)

Modern organizations increasingly need both operational and analytical MDM to support AI initiatives:

• Operational access for real-time grounding of AI outputs
• Analytical access for model training and evaluation
• Hybrid patterns that combine real-time retrieval with batch enrichment

Example: A customer service AI agent might:

1. Retrieve real-time customer master data (operational)
2. Access historical interaction patterns (analytical)
3. Update customer preferences based on the conversation (operational)
4. Feed interaction data back for model retraining (analytical)

STEP 4: DETERMINE WHO WILL OWN/ACCESS THE DATA

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.

The Custom UI Question

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?

Operational Access Patterns

For internal operational purposes (customer service, sales, production), consider:

• Will users access data through another application?
• Will it be through current application sets (ERP, CRM, sales automation)?
• Do AI agents need API access to master data?
• What are the latency requirements?

External Customer Access

An additional factor is usage by external customers:

• Will customers access master data through your website or mobile app?
• If yes, data governance and stewardship become exponentially more important
• Data stewards must be 100% accurate on matched records to ensure one customer doesn't see another's information
• This risk is heightened when AI systems retrieve and display customer data

Privacy and Security Considerations:

• GDPR, CCPA, and other regulations require explicit data handling policies
• AI systems accessing customer data must respect consent and preferences
• Audit trails must track all access, especially by automated agents

Analytical Access Patterns

For analytical MDM, users typically access data through:

• Data warehouses or data marts (not directly from the hub)
• Business intelligence platforms (Tableau, Power BI, Looker)
• Feature stores for ML pipelines (Feast, Tecton)
• Vector databases for AI retrieval (Pinecone, Weaviate, Chroma)

The IT department normally addresses these requirements, but users should be involved early to:

• Determine which data is needed
• Define refresh frequencies
• Establish data quality thresholds
• Buy into the program

AI and Agent Access

Modern MDM must support AI systems as first-class data consumers:

For LLMs and RAG Systems:

• API endpoints for real-time retrieval
• Metadata to help AI understand data context
• Confidence scores for data quality
• Version tracking for data lineage

For Agentic AI:

• Write-back capabilities (with governance)
• Transaction logging for audit
• Rate limiting and cost controls
• Fallback mechanisms when data is unavailable

For Machine Learning:

• Feature engineering pipelines
• Training/validation data splits
• Bias detection and monitoring
• Model performance tracking tied to data quality

STEP 5: DECIDE ON THE DATA SOURCES

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.

When Is MDM Necessary?

MDM is essential when:

• Duplication exists within the same source system
• Duplication exists across multiple source systems
• Third-party data is used to augment internal data
• AI systems need a single source of truth across domains

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.

Modern Data Source Considerations

Cloud-Based Systems
Many organizations now have data scattered across:

• SaaS applications (Salesforce, HubSpot, Zendesk)
• Cloud data warehouses (Snowflake, Databigquery, Redshift)
• Marketing automation platforms (Marketo, Eloqua)
• E-commerce platforms (Shopify, Adobe Commerce)

APIs and Microservices
Modern architectures generate data across distributed services. MDM must integrate with:

• REST APIs
• GraphQL endpoints
• Event streams (Kafka, Kinesis)
• Webhooks and real-time notifications

Third-Party Enrichment
Common external data sources include:

• D&B, ZoomInfo for firmographic data
• Experian, Acxiom for consumer data
• Industry-specific data providers
• Social media and web scraping (with appropriate consent)

Historical Data Decisions

Critical decisions about historical data must be made upfront:

How many years of data will be included?

• Longer history typically means worse data quality
• Balance completeness with usability
• Consider regulatory retention requirements

Are only active records being included?

• How is "active" defined?
• What about dormant customers who might return?
• Should prospect data be included?

Data augmentation timing:

• Doesn't need to happen at project onset
• Can be deferred to later phases
• Should be planned in the roadmap

AI-Specific Data Source Considerations

For Training AI Models:

• Ensure data is representative and unbiased
• Document data provenance for regulatory compliance
• Version control for reproducibility

For Real-Time AI:

• Low-latency access patterns
• Caching strategies
• Fallback data sources

For Agentic Systems:

• Write-back capabilities for agent learning
• Audit trails for all data modifications
• Sandbox environments for testing

STEP 6: DETERMINE WHICH DATA SHOULD BE MASTERED

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.

Defining Core Entities

Customer:

• Does the term include inactive customers or prospects?
• Business customers vs. individual consumers?
• Accounts vs. contacts vs. users?
• How do you handle household relationships?

Product:

• SKU-level vs. product families?
• Physical products vs. services vs. subscriptions?
• Do accessories and components count as separate products?
• How are bundles and configurations handled?

Location:

• Physical building vs. department vs. delivery point?
• Ship-to vs. bill-to vs. service location?
• How are virtual/remote locations handled?

Getting consensus across business units is extremely difficult and time-consuming. However, defining terms should be a priority because it will dictate:

• Software license costs
• Storage requirements
• Stakeholder involvement
• Implementation timeline
• AI model design

Master Data vs. Transactional Data

Master data should be:

• Lightweight – Core attributes that define the entity
• Less volatile – Doesn't change very often
• Shared – Used across multiple business processes
• Authoritative – Single source of truth

Examples of master data:

• Customer name, address, contact information
• Product SKU, description, category
• Employee ID, department, role

Examples of transactional data (NOT master data):

• Purchase history
• Support tickets
• Web session logs
• Real-time inventory levels

Drawing the line can be difficult. Ask yourself:

• Does this data define the entity, or describe an interaction with it?
• Is this data relatively stable, or does it change constantly?
• Do multiple systems need to agree on this data?

AI Implications

Master data serves as the foundation for AI grounding:

For RAG Systems:

• Product master data prevents AI from inventing features
• Customer master data ensures personalization accuracy
• Organizational master data maintains compliance boundaries

For Agentic AI:

• Agents must know authoritative values before taking actions
• Master data defines what agents are allowed to modify
• Hierarchies guide agent decision-making

For Model Training:

• Clean master data improves model accuracy
• Consistent definitions reduce bias
• Proper scope prevents overfitting

STEP 7: IDENTIFY HIERARCHIES, RELATIONSHIPS & GROUPINGS

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:

• Vendor selection
• Implementation strategy
• Data governance model
• AI architecture design

Hierarchies

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:

• Corporate structure (parent company → subsidiaries → locations)
• Geographic rollups (region → country → state → city)
• Account relationships (account → contacts → users)
• Buying groups or consortiums

Product Hierarchies:

• Category → subcategory → brand → SKU
• Product family → model → configuration
• Service tiers or subscription levels

Organizational Hierarchies:

• Company → division → department → team
• Manager → direct reports
• Budget centers or cost allocation

Multiple hierarchies may exist for the same data. For example, customer hierarchies might differ based on:

• Sales territory assignment
• Marketing segmentation
• Financial reporting structure
• Customer service routing

Sources for Hierarchies:

• Internal systems (Salesforce for account relationships, HR for organizational structure)
• Third-party providers (D&B, ZoomInfo for corporate hierarchies)
• Manual curation by data stewards

AI Applications:

• Agentic systems use hierarchies to escalate decisions
• RAG systems use hierarchies to understand context
• Recommendation engines use product hierarchies for substitutes/complements

Relationships

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:

• What value do these relationships bring?
• Is this information already captured elsewhere?
• Is the value worth the cost of manual capture or third-party data?

Common Relationship Types:

Customer Relationships:

• Parent-child (family relationships)
• Spouse-to-spouse
• Employer-employee
• Influencer-decision maker
• Board memberships or affiliations
• Broker/dealer-to-customer (indirect channels)

Product Relationships:

• Product-to-accessory
• Substitute products
• Complementary products
• Upgrade/downgrade paths

Cross-Domain Relationships:

• Customer-to-product (ownership, preferences, restrictions)
• Customer-to-location (ship-to, service address)
• Customer-to-contract
• Employee-to-customer (relationship manager)

AI Applications:

• Recommendation engines use product relationships
• Chatbots use customer relationships for context
• Agentic systems use relationships to route actions
• Graph neural networks leverage relationship networks

Groupings

Groupings are similar to relationships and many MDM systems handle them identically internally. The most valuable groupings include:

Household Grouping:

• Individuals at the same address
• Family purchasing power
• Shared preferences and behaviors
• Privacy considerations (one household member's data affecting others)

Geographic Grouping:

• Region, territory, or market
• Service area or delivery zone
• Regulatory jurisdiction
• Address is typically a prerequisite

Behavioral Grouping:

• Customer segments (high-value, at-risk, growth)
• Product affinities
• Channel preferences
• Lifecycle stages

AI Applications:

• Segmentation models for personalization
• Cohort analysis for experimentation
• Lookalike modeling for acquisition
• Agentic systems that take group-level actions

Modern Considerations

Knowledge Graphs:
Modern MDM increasingly leverages knowledge graphs to represent complex, multi-dimensional relationships. This is especially valuable for:

• AI systems that need semantic understanding
• Complex B2B relationship networks
• Multi-brand or multi-division enterprises

Dynamic Groupings:
AI-powered MDM can automatically identify and maintain groupings based on:

• Behavioral patterns
• Transaction clustering
• Social network analysis
• Real-time segmentation

STEP 8: DEVELOP A CLEAR, FEASIBLE IMPLEMENTATION PLAN

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:

• "Big Bang" approaches with many sources, multiple integration points, and complex data
• "Build it and they will come" approaches with no specific objective in mind

Recommended Approach: Iterative Value Delivery

Phase 1: Quick Win (3-6 months)

• Select 1-2 high-value data sources
• Focus on one domain (customer OR product, not both)
• Implement core match/merge capabilities
• Deliver to one critical business use case
• Establish data governance foundation

Phase 2: Expand & Integrate (6-12 months)

• Add additional data sources
• Implement hierarchies and relationships
• Integrate with key operational systems
• Enable AI/ML use cases
• Measure and communicate business value

Phase 3: Scale & Optimize (12-24 months)

• Expand to additional domains
• Implement advanced features (real-time, workflows)
• Optimize performance and cost
• Support agentic AI workflows
• Build center of excellence

Critical Success Factors

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:

• Data stewardship processes
• Match/merge rules documentation
• AI safety policies
• Audit and compliance requirements

5. Plan for AI Integration
Even if not implementing AI immediately, design MDM architecture to support future AI needs:

• API-first design
• Metadata richness
• Real-time access patterns
• Version control and lineage

Common Pitfalls to Avoid

❌ Scope Creep
Starting with "just customer data" but gradually adding:

• Product data
• Location data
• Hierarchies
• Historical data going back 10 years

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.

Modern Implementation Considerations

Cloud-Native Deployment:

• SaaS MDM solutions reduce infrastructure overhead
• Cloud data platforms (Snowflake, Databricks) simplify integration
• Serverless patterns reduce operational burden

AI-Ready Architecture:

• Design APIs for both human and AI consumption
• Implement semantic metadata for AI understanding
• Build observability for AI-driven access patterns

Agile Delivery:

• 2-week sprints with working software
• Continuous integration/deployment
• Automated testing including AI use cases

CONCLUSION

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:

• Ground their outputs in trusted enterprise data
• Make accurate, compliant autonomous decisions
• Learn and improve while maintaining governance
• Deliver personalized experiences at scale

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.

Key Takeaways

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.

ABOUT EARLEY INFORMATION SCIENCE

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:

• Information Architecture & Taxonomy Design
• AI & Knowledge Engineering
• Master Data Management
• Product Data Management
• Customer Engagement Strategies
• Enterprise AI Readiness Assessment

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.