Master Data Management

Create a single, authoritative source of truth for your most critical business entities to eliminate duplicates, ensure consistency, and enable data-driven transformation.

Service Overview

Master Data Management (MDM) creates a single, authoritative source of truth for your organization's most critical business entities—customers, products, suppliers, employees, and locations. Without MDM, organizations struggle with duplicate records, inconsistent data, and conflicting business metrics that undermine decision-making and operational efficiency.

arqitekta's approach to MDM goes beyond traditional data cleansing and deduplication. We design master data architectures that support both operational efficiency and analytical insights, enabling real-time synchronization across systems while maintaining data quality at scale. Our methodology balances centralized control with federated ownership, ensuring master data serves both compliance requirements and business agility.

Whether you're consolidating after an acquisition, implementing customer 360 initiatives, or preparing for digital transformation, we help you build master data capabilities that scale with your business and adapt to changing requirements. The result is not just clean data, but a strategic foundation for analytics, automation, and customer experience excellence.

The Master Data Challenge

Why Master Data Matters

Business Impact of Poor Master Data

Customer Data Issues:
- 20-30% of customer records are duplicates
- 40-60% contain outdated information
- 15-25% have incomplete critical fields
- Result: Poor personalization, lost revenue

Product Data Issues:
- 30-50% of products have inconsistent descriptions
- 25-40% missing critical attributes
- 20-30% incorrect pricing or availability
- Result: Operational inefficiency, customer confusion

Supplier Data Issues:
- 35-45% duplicate vendor records
- 25-35% outdated contact information
- 30-40% incomplete compliance data
- Result: Supply chain disruption, compliance risk

System Proliferation Problems

Modern enterprises typically have:

50-200+ business applications
10-50 data sources for customer data
20-100 product catalogs
Multiple versions of "truth"

Business Consequences

Operational: Inefficient processes, duplicate efforts
Customer: Poor experience, lost opportunities
Financial: Increased costs, reduced revenue
Compliance: Regulatory risks, audit failures
Strategic: Poor analytics, wrong decisions

Our MDM Methodology

Phase 1: Discovery & Strategy

Weeks 1-4: Foundation Building

Business Case Development

Value identification and quantification
Stakeholder impact assessment
ROI modeling and justification
Success criteria definition

Current State Assessment

Data Landscape Analysis:
- Source system inventory
- Data volume and quality assessment
- Integration pattern analysis
- Ownership and stewardship review

Business Process Impact:
- Process dependency mapping
- Data flow analysis
- Decision point identification
- Pain point documentation

Technology Assessment:
- Infrastructure capabilities
- Integration architecture
- Tool and platform evaluation
- Skill gap analysis

MDM Strategy Design

Operating model definition
Governance framework
Technology architecture
Implementation roadmap

Phase 2: Architecture & Design

Weeks 5-8: Solution Architecture

Domain Modeling

Master data domain design:

Entity relationship modeling
Attribute standardization
Hierarchy definitions
Business rule specification

Technical Architecture

MDM Architecture Components:
├─ Data Acquisition Layer
│   ├─ Real-time interfaces
│   ├─ Batch integration
│   ├─ Data quality gates
│   └─ Change detection
├─ Master Data Hub
│   ├─ Golden record creation
│   ├─ Survivorship rules
│   ├─ Hierarchy management
│   └─ Version control
├─ Data Services Layer
│   ├─ Data APIs
│   ├─ Matching services
│   ├─ Workflow engines
│   └─ Event publishing
└─ Data Distribution Layer
    ├─ Real-time syndication
    ├─ Batch distribution
    ├─ Data virtualization
    └─ Conflict resolution

Integration Design

Source system interfaces
Target system syndication
Real-time vs. batch processing
Error handling and recovery

Phase 3: Implementation

Weeks 9-12: Build & Deploy

Platform Deployment

MDM tool implementation
Infrastructure provisioning
Security configuration
Performance optimization

Data Migration

Data profiling and cleansing
Initial load execution
Data validation and testing
Cutover planning

Integration Development

Interface development
Service implementation
Workflow configuration
Monitoring setup

Phase 4: Operationalization

Weeks 13-16: Go-Live & Optimize

Production Deployment

Phased rollout execution
User training delivery
Support process activation
Performance monitoring

Continuous Improvement

Quality monitoring
Process optimization
User feedback integration
Capability enhancement

MDM Implementation Patterns

Pattern 1: Registry Approach

Best for: Federated environments with strong source systems

Characteristics:
- Lightweight central registry
- Source systems remain authoritative
- Cross-reference management
- Minimal data movement

Advantages:
- Lower implementation complexity
- Faster time to value
- Minimal source system impact
- Lower infrastructure costs

Use Cases:
- Customer identification
- Product catalog linking
- Supplier cross-reference
- Location hierarchies

Pattern 2: Repository Approach

Best for: Central control with analytical focus

Characteristics:
- Central authoritative store
- Complete entity storage
- Rich data model
- Analytical optimization

Advantages:
- Single source of truth
- Rich analytical capabilities
- Complete data history
- Strong governance control

Use Cases:
- Customer 360 initiatives
- Product information management
- Regulatory reporting
- Data warehousing support

Pattern 3: Hybrid Approach

Best for: Complex, multi-domain environments

Characteristics:
- Registry for some domains
- Repository for others
- Flexible architecture
- Domain-specific optimization

Advantages:
- Optimized per domain
- Balanced complexity
- Evolutionary approach
- Risk mitigation

Use Cases:
- Multi-domain MDM
- Phased implementations
- Merger integrations
- Legacy modernization

Master Data Domains

Customer Master Data

Core Entities

Individual customers
Corporate accounts
Households
Prospects

Key Attributes

Identity Data:
- Name variations
- Contact information
- Identification numbers
- Demographics

Relationship Data:
- Account relationships
- Household composition
- Corporate hierarchies
- Contact preferences

Behavioral Data:
- Interaction history
- Preference profiles
- Segmentation data
- Lifecycle stage

Business Benefits

360-degree customer view
Improved personalization
Better customer service
Enhanced marketing effectiveness

Product Master Data

Core Entities

Products and services
Product hierarchies
Product variants
Product relationships

Key Attributes

Descriptive Data:
- Product names and descriptions
- Technical specifications
- Classifications and categories
- Marketing attributes

Commercial Data:
- Pricing information
- Availability status
- Sales channels
- Promotional rules

Operational Data:
- Manufacturing details
- Supplier information
- Inventory parameters
- Lifecycle status

Business Benefits

Consistent product information
Improved catalog management
Better pricing consistency
Enhanced customer experience

Supplier Master Data

Core Entities

Suppliers and vendors
Supplier hierarchies
Supplier categories
Supplier relationships

Key Attributes

Identity Data:
- Company information
- Contact details
- Registration numbers
- Certifications

Capability Data:
- Products and services
- Capacity information
- Quality ratings
- Performance metrics

Commercial Data:
- Contract terms
- Payment information
- Risk assessments
- Compliance status

Business Benefits

Streamlined procurement
Better supplier relationships
Improved risk management
Enhanced compliance

Data Quality Framework

Quality Dimensions

Accuracy

Data correctness validation
Business rule compliance
Source system verification
External data validation

Completeness

Required field population
Attribute completeness scoring
Missing data identification
Completeness trend tracking

Consistency

Cross-system data alignment
Standard format compliance
Reference data conformity
Relationship integrity

Timeliness

Data freshness monitoring
Update frequency tracking
Staleness identification
Real-time validation

Quality Rules Engine

Validation Rules

Field-Level Rules:
- Format validation (phone, email, etc.)
- Range checking (dates, amounts)
- Required field validation
- Pattern matching

Record-Level Rules:
- Relationship validation
- Business logic checking
- Dependency verification
- Completeness scoring

Cross-Record Rules:
- Duplicate detection
- Hierarchy validation
- Referential integrity
- Consistency checking

Data Stewardship Workflow

Issue identification and scoring
Automatic resolution attempts
Steward assignment and notification
Resolution tracking and approval
Quality metric updates

Matching & Survivorship

Matching Strategy

Probabilistic Matching

Fuzzy matching algorithms
Weighted scoring models
Threshold configuration
Match review workflows

Deterministic Matching

Exact key matching
Business rule-based
High confidence automation
Exception handling

Machine Learning Matching

Pattern recognition
Continuous learning
Adaptive algorithms
Performance optimization

Survivorship Rules

Rule Types

Source Priority:
- Trusted source hierarchy
- Data quality scoring
- Recency weighting
- Completeness factors

Field-Level Rules:
- Most complete value
- Most recent update
- Highest quality score
- Business rule override

Conditional Rules:
- Context-dependent logic
- Business scenario handling
- Exception management
- Override capabilities

Golden Record Creation

Attribute selection logic
Confidence scoring
Audit trail maintenance
Version management

Technology Platform Selection

Leading MDM Platforms

Enterprise Solutions

Informatica MDM: Comprehensive enterprise platform
SAP Master Data Governance: ERP-integrated solution
Oracle Enterprise Data Management: Full-suite offering
IBM InfoSphere MDM: Analytics-integrated platform

Cloud-Native Solutions

Microsoft Master Data Services: Azure-integrated
Salesforce Customer 360: CRM-focused platform
Reltio: Cloud-first, real-time MDM
Semarchy: Smart data platform

Open Source Alternatives

Talend Open Studio: Data integration focus
Pentaho: Analytics-integrated approach
Apache Spark: Big data processing
Custom solutions: Flexible development

Selection Criteria

Functional Requirements

Domain support capabilities
Matching algorithm sophistication
Workflow and stewardship tools
Integration and API capabilities

Technical Requirements

Scalability and performance
Cloud deployment options
Security and compliance features
Development and customization tools

Business Requirements

Total cost of ownership
Implementation complexity
Vendor stability and support
Industry-specific features

Industry Applications

Financial Services

Regulatory Compliance Focus

Key Requirements

Customer due diligence
Regulatory reporting accuracy
Risk data aggregation
Know Your Customer (KYC)

Implementation Approach

Customer 360 platform
Real-time risk monitoring
Regulatory data lineage
Automated compliance reporting

Results Achieved

95% reduction in duplicate customers
80% faster KYC processing
60% improvement in regulatory reporting
$2M annual compliance cost savings

Healthcare

Patient Safety Priority

Key Requirements

Patient identity matching
Provider credentialing
Drug and device catalogs
Care coordination

Implementation Approach

Master patient index
Provider registry
Clinical data standards
Interoperability focus

Results Achieved

99.8% patient matching accuracy
50% reduction in medical errors
30% improvement in care coordination
$5M annual savings from efficiency

Retail

Customer Experience Excellence

Key Requirements

Omnichannel customer view
Product catalog consistency
Supplier data accuracy
Inventory optimization

Implementation Approach

Customer 360 platform
Product information management
Supplier onboarding automation
Real-time data syndication

Results Achieved

40% improvement in conversion rates
60% reduction in product data errors
25% faster supplier onboarding
$10M increase in annual revenue

Governance & Stewardship

Data Stewardship Model

Stewardship Roles

Business Data Stewards:
- Domain expertise
- Business rule definition
- Quality issue resolution
- Process improvement

Technical Data Stewards:
- System administration
- Integration management
- Performance optimization
- Technical issue resolution

Executive Data Sponsors:
- Strategic direction
- Resource allocation
- Conflict resolution
- Success measurement

Stewardship Processes

Data quality monitoring
Issue management workflow
Change request processing
Training and certification

Governance Framework

Policy Areas

Data ownership and accountability
Quality standards and thresholds
Change management procedures
Access control and security

Decision Rights

Data model changes
Quality rule modifications
Source system priorities
Exception approvals

Success Metrics

Data Quality Metrics

Accuracy Metrics:
- Field accuracy rates: >95%
- Business rule compliance: >98%
- Validation error rates: &lt;2%

Completeness Metrics:
- Required field population: >90%
- Critical attribute completeness: >95%
- Profile completeness scores: Improving

Consistency Metrics:
- Cross-system alignment: >95%
- Standard format compliance: >98%
- Reference data conformity: >99%

Timeliness Metrics:
- Data freshness: &lt;1 day lag
- Update frequency: Real-time capable
- SLA compliance: >99%

Business Impact Metrics

Operational Benefits:
- Process efficiency: 30-50% improvement
- Manual effort reduction: 60-80%
- Error resolution time: 70-90% faster

Customer Benefits:
- Customer satisfaction: 20-40% increase
- Personalization accuracy: 50-70% better
- Service quality: Significant improvement

Financial Benefits:
- Cost reduction: $1-5M annually
- Revenue increase: 10-25%
- ROI: 200-400% over 3 years

Master Data Management