Master Data Synchronization & Distribution

Real-time master data distribution to all consuming systems with conflict resolution, versioning, and subscription-based updates.

Business Outcome
reduction in time for initial synchronization
Complexity:
Medium
Time to Value:
3-6 months

Why This Matters

What It Is

Real-time master data distribution to all consuming systems with conflict resolution, versioning, and subscription-based updates.

Current State vs Future State Comparison

Current State

(Traditional)

Batch file transfers or manual data exports from master data systems to consuming applications (nightly or weekly). Each consuming system maintains its own copy of master data that drifts out of sync. No versioning or conflict resolution when multiple systems update the same record. Failed synchronizations require manual intervention and troubleshooting. Consuming systems unaware of master data changes until next batch sync.

Characteristics

  • Microsoft Dynamics 365 Business Central
  • SAP S/4HANA
  • Informatica MDM
  • IBM InfoSphere
  • Kafka
  • AWS Kinesis
  • Excel

Pain Points

  • Complexity in data mapping across heterogeneous systems.
  • Latency in synchronization due to reliance on batch processes.
  • Scalability issues with large volumes of master data.
  • Conflict resolution challenges from multiple data sources.
  • High implementation and maintenance costs for MDM solutions.
  • Lack of real-time updates in traditional processes.
  • Dependency on scheduled jobs can lead to temporary data inconsistencies.
  • Manual intervention required for conflict resolution can slow down processes.

Future State

(Agentic)

Event-driven master data synchronization architecture publishes master data changes to message bus in real-time. Consuming systems subscribe to relevant data domains (e.g., eCommerce subscribes to product and customer MDM). AI-powered conflict resolution automatically handles scenarios where multiple systems attempt to update same master record using priority rules, data quality scores, and business logic. Versioning system maintains history of all master data changes enabling point-in-time recovery. Failed synchronization attempts automatically retry with exponential backoff and alert data operations if persistent. Change data capture (CDC) minimizes data transfer volumes by sending only deltas. API-based synchronization enables consuming systems to query master data on-demand. Subscription management portal allows systems to self-service subscribe/unsubscribe from data feeds.

Characteristics

  • MDM golden records (customer, product, supplier, location)
  • Change data capture (CDC) logs
  • Consuming system subscriptions and preferences
  • Conflict resolution rules and priorities
  • Data quality scores by source system
  • Sync error logs and monitoring metrics

Benefits

  • Real-time sync (seconds vs 1-7 days lag)
  • 99%+ sync success rate (vs 85-90%)
  • Automated conflict resolution (vs manual intervention)
  • 100% data freshness in consuming systems
  • 70-85% reduction in data synchronization issues

Is This Right for You?

50% match

This score is based on general applicability (industry fit, implementation complexity, and ROI potential). Use the Preferences button above to set your industry, role, and company profile for personalized matching.

Why this score:

  • Applicable across multiple industries
  • Moderate expected business value
  • Time to value: 3-6 months
  • (Score based on general applicability - set preferences for personalized matching)

You might benefit from Master Data Synchronization & Distribution if:

  • You're experiencing: Complexity in data mapping across heterogeneous systems.
  • You're experiencing: Latency in synchronization due to reliance on batch processes.
  • You're experiencing: Scalability issues with large volumes of master data.

This may not be right for you if:

  • Requires human oversight for critical decision points - not fully autonomous

Related Functions

Metadata

Function ID
function-mdm-master-data-sync