Master Data
Management (MDM)

Why MDM?

Customer, product, supplier, and other master data are often created in different systems and maintained by different departments. This leads to duplicates, conflicting information, and unclear responsibilities.

We support companies in consistently managing their business-critical master data, linking it together, and making it available for all relevant processes and applications – from strategy and data modeling to governance, architecture, and technical implementation.

Let’s discuss your MDM requirements:
experts@striped-giraffe.com

Challenge

When Multiple Systems Claim the Same Truth

In established system landscapes, there are often different versions of the same customer, product, or supplier data. ERP, CRM, PIM, commerce platforms, and custom applications use different structures, identifiers, and quality rules.

Typical consequences include: Master Data Management establishes a standardized framework for consistently defining, maintaining, and making this data available company-wide.

• Duplicate and conflicting data records
• Unclear master systems
• Manual reconciliations and corrections
• Erroneous or delayed processes
• Inconsistent customer and product views
• Difficulties with reporting, analytics, and AI
• Increased risks related to compliance and regulatory requirements
• High effort required for system migrations and integration projects

What MDM
does

A Reliable Foundation for Business-Critical Data

MDM integrates business rules, responsibilities, processes, and technology. The goal is not necessarily a single, centralized database, but rather a consistent and controlled view of master data across the entire system landscape.

Unified Data Models

We develop common definitions, structures, and relationships for business-critical data objects. These include, for example, customers, products, suppliers, locations, organizational units, and reference data.

Golden Records and Trusted Views

Data from various sources is reconciled, consolidated, and evaluated according to clear rules. This results in reliable data records or reconciled views that are available to the respective business processes.

360° Customer View

Customer data is often distributed across CRM, ERP, commerce, marketing, service, and billing systems. Master Data Management reconciles this information, merges related data records, and creates a consistent, cross-system view of customers and accounts.

This creates a reliable data foundation for sales, marketing, service, finance, analytics, and compliance. It also enables customer hierarchies, corporate structures, and relationships between accounts to be mapped more transparently.

Matching and Duplicate Removal

We define rules that can be used to identify, match, and consolidate identical or related data records.

Maintenance Processes and Workflows

MDM organizes how master data is created, modified, validated, approved, and distributed. Roles and processes are designed to ensure data quality is maintained over the long term.

Integration into the System Landscape

We integrate MDM with ERP, CRM, PIM, commerce, analytics, and other enterprise systems. In doing so, we take into account both existing architectures and future modernization initiatives.

Governance and Traceability

Clear responsibilities, approval rules, version history, and auditability ensure that changes are traceable and data can be used reliably.

Relevant Master
Data Domains

MDM Is More Than Just Customer Data

Customer MDM

Consistent customer, account, and contact data for sales, service, marketing, commerce, finance, and compliance.

Product MDM

Reliable product master data for development, procurement, production, sales, commerce, and after-sales processes.

Supplier MDM

Uniform supplier data for procurement, risk management, compliance, and supply chain processes.

Location and Organization Data

Consistent information on locations, plants, branches, subsidiaries, and organizational units.

Reference Data Management

Centralized management of controlled value lists, classifications, codes, and hierarchies used across numerous systems.

Multi-Domain MDM

Linking multiple master data domains to map complex relationships between customers, products, suppliers, and organizational units.

Business
Benefits

Fewer operational inefficiencies, better decisions

More efficient processes

Consistent master data reduces manual checks, follow-up inquiries, error corrections, and data discontinuities.

Lower Costs

Incorrect, incomplete, or redundant master data constantly causes avoidable expenses. These include manual corrections, follow-up inquiries, duplicate data maintenance, incorrect orders, delivery problems, and time-consuming reconciliations between systems and business units.

Master Data Management reduces these costs through consistent data, automated quality rules, and clearly defined maintenance processes. At the same time, the effort required for integrations, migrations, and the implementation of new applications is reduced.

Reliable Customer and Product Experiences

Consistent information ensures that customers, partners, and employees access the same data across all channels.

Better Foundations for Analytics and AI

Analytics and AI require clearly defined, high-quality data. MDM creates a robust semantic and operational foundation for this.

Faster Integration and Modernization

Clear data models and responsibilities facilitate cloud migrations, ERP transformations, commerce projects, and the introduction of new platforms.

Greater Transparency and Compliance

Traceable data provenance, version history, and controlled maintenance processes support regulatory requirements and internal controls.

Greater Business Agility

New products, channels, markets, and business models can be integrated more quickly when master data does not have to be realigned for every project.

MDM as the
foundation
for ai

AI Requires Unambiguous Business Data

AI applications require not only large volumes of data, but also unambiguous entities, consistent relationships, and reliable contextual information. Otherwise, models cannot reliably map customers, products, or suppliers.

Master Data Management supports AI initiatives by, among other things:

• unambiguous identities and relationships
• harmonized terms and classifications
• consistent product, customer, and supplier data
• documented provenance and quality rules
• controlled access to sensitive master data
• reliable contextual data for search, analytics, and automation applications

In this way, MDM not only improves the foundation for training and analysis but also enhances the quality of operational AI applications.

Our approach

From MDM Strategy to a Production-Ready Solution

Phase 1: Analyzing Objectives and the Current Situation

We examine the relevant business processes, data sources, systems, and responsibilities. Together, we identify the master data domains and use cases that offer the greatest benefit.

Possible outcomes:

• Analysis of the system and data landscape
• Stakeholder and process analysis
• Identification of critical data objects
• Assessment of data quality and redundancies
• Prioritization of use cases
• MDM business case

Phase 2: Develop the target state and data model

We define how master data will be managed, owned, and distributed in the future.

Possible outcomes:

• Business and technical target vision
• Domain and data model
• Definition of leading systems
• Golden record and survivorship rules
• Role and governance model
• Integration and migration strategy

Phase 3: Design the platform and architecture

We evaluate suitable MDM approaches and technologies based on the requirements and the existing system landscape.

Possible outcomes:

• Selection of the appropriate MDM architecture model
• Tool selection or make-or-buy assessment
• Integration architecture
• Interface and event concept
• Security and authorization concept
• Implementation roadmap

Phase 4: Implement and Scale Gradually

We start with a prioritized domain or a specific use case and expand the solution iteratively.

Possible outcomes:

• Proof of concept or pilot
• Data cleansing and migration
• Implementation of matching and quality rules
• Workflows and user interfaces
• System integration
• Rollout, enablement, and handover to operations

MDM
Architecture

The Right Approach for Your System Landscape

Depending on your specific situation, different MDM architecture models may be suitable:

Registry

Master data remains in the source systems. The MDM establishes identities and relationships and creates a comprehensive view.

Consolidation

Data is gathered from various systems into a central MDM solution and consolidated for analysis or reference purposes.

Coexistence

Source systems and MDM exchange changes. The MDM enhances and harmonizes data without completely taking over all maintenance processes.

Centralized or Transactional MDM

Master data is created and maintained centrally in the MDM and then distributed to the connected systems.

Federated MDM

Responsibility and data management remain partially within the domains, while common standards and governance enable consistent usage.