Overview
In today’s complex business environment, organizations—especially in regulated sectors like banking in Thailand—face a common challenge: customer data spread across multiple online and offline touchpoints, resulting in fragmented, inconsistent, and duplicated records. This fragmentation, often caused by siloed operational systems across on-premise, SaaS, and cloud environments, undermines core business processes, regulatory compliance, and data integrity.
This case study illustrates how a robust Master Customer Data Management (MCDM) solution can lay the foundation for digital transformation and operational excellence.
The Challenge
The client faced critical challenges caused by fragmented customer data across various legacy and digital systems. As the organization embarked on modernizing its personal banking experience, key business entities—such as customer records, product catalogs, and marketing data—remained trapped in disconnected silos. This disjointed data ecosystem led to:
- Inconsistent, duplicate, and inaccurate master data, undermining trust and usability across systems.
- Operational inefficiencies, including cross-selling irrelevant products and service errors, due to reliance on unreliable customer data.
- Regulatory compliance challenges, such as fragmented customer consent data and risks related to fraudulent or non-compliant records.
- Limited business intelligence, as cross-functional data analysis was hampered by discrepancies and inconsistent definitions of key entities.
- Increased operational costs and reduced confidence in strategic decision-making due to the absence of a centralized, trusted data source.
The Solution
To address these issues, we implemented a Master Customer Data Management (MCDM) solution—designed to consolidate, govern, and synchronize critical customer information into a single, authoritative source of truth.
The MCDM platform integrated data from enterprise systems and established robust workflows for consistent and secure data sharing across services. It served as a centralized hub for managing essential customer entities, ensuring data accuracy, consistency, and accessibility across the entire banking ecosystem.
Key Outcomes
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- Single Source of Truth: Unified master data across systems into a consistent, accurate “golden record” for key entities such as customers, products, and suppliers.
- Improved Data Quality: Enforced data standards, deduplication, and enrichment to improve data trust and minimize operational errors.
- Strengthened Compliance & Governance: Established robust data governance frameworks, ensuring adherence to regulatory requirements and internal policies, thereby mitigating risks associated with inaccurate or non-compliant data.
- Empowered Business Intelligence & Analytics: Provided a foundation of high-quality, consistent master data, enabling more accurate reporting, deeper analytical insights, and more confident, data-driven decision-making across all business functions.
- Operational Efficiency: Streamlined customer-facing processes and reduced manual reconciliation efforts by providing clean and reliable master data.
- Stronger Compliance & Governance: Established governance policies and controls to meet regulatory requirements, reduce risks, and align with PII data protection regulations.
- Better Analytics & Insights: Delivered high-quality, consistent data to analytics teams, enabling better reporting, smarter insights, and stronger decision-making across departments
- Single Source of Truth: Unified master data across systems into a consistent, accurate “golden record” for key entities such as customers, products, and suppliers.
Our Approach
Our delivery approach focused on aligning data strategy with the business's operational and regulatory needs:
1. Discovery of the Banking Ecosystem: We assessed the existing service landscape, mapping how customer data flows across onboarding, engagement, and service journeys. We worked closely with product owners to understand each service's data needs, business use cases, and compliance requirements. These insights helped define the scope and objectives of the MCDM initiative.
2. Data Integration Design: We created sequence, context, and component diagrams to map how data is exchanged across services—from legacy systems to digital platforms. This enabled the definition of integration methods (API, Kafka, etc.) to ensure timely, consistent data exchange between systems connected to the new personal banking application.
3. Data Architecture Modeling: We adopted the BIAN framework as a data modeling standard, mapping source data to BIAN-aligned architecture. This ensured semantic consistency and industry alignment across the banking data model.
4. Implementation Roadmap: We evaluated the entire technology stack and documented integration patterns with guidance from steering committees. We developed a detailed implementation roadmap, including timelines, resource planning, and recommended squad composition.
5. Development & Rollout: To ensure smooth execution, we worked with subscribing product teams, legacy data teams, and compliance departments to meet security, integration, and governance standards. We delivered a functional MVP, validated with beta users, and followed the bank’s development framework for quality assurance.
Conclusion
By establishing a single source of truth through Master Customer Data Management, the bank overcame long-standing data quality and governance challenges that had hindered operational performance and regulatory compliance. The MCDM solution not only streamlined customer data across legacy and modern systems but also enabled cross-functional teams to trust, access, and act on consistent information. As a result, the organization can now operate with greater efficiency, reduce risk, and make more confident decisions.
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