The problem of fragmented data and the Customer 360 concept
Building a holistic view of the customer (Customer 360) is a critical task for large enterprises, especially in the financial sector. In practice, data about a single customer is often scattered across dozens of systems (CRM, ERP, billing, etc.). This leads to inconsistent information, degraded data quality, operational inefficiencies, and regulatory risks regarding personal data protection.
To address these challenges, it is essential to establish a single, trusted master record that serves as the common source of truth for the entire organization.
Why it matters for the industry
For modern enterprises, fragmented customer data leads to severe operational bottlenecks, degraded customer trust, and lost revenue due to poor personalization. In highly regulated sectors like finance, failing to maintain a single source of truth results in compliance penalties and heightened security vulnerabilities, especially as businesses begin integrating AI tools that rely on this data.
Distribution of roles in Data Governance
Effective data management requires a clear organizational structure and defined responsibilities:
- Data Owner: a business unit leader responsible for data quality, relevance, and compliance with business requirements.
- Data Steward: a specialist directly involved in data cleansing, deduplication, and maintaining data quality.
- Data Architect: designs data models, integration flows, and technical infrastructure.
- Cybersecurity team: protects the master record from unauthorized access and leaks.
- AI Governance team: manages risks associated with using artificial intelligence in customer data processing.
AI and cybersecurity challenges
Integrating AI into business processes introduces new risks to data integrity. AI models can automatically modify records or introduce biased changes. To mitigate these threats, Data Governance policies must be combined with AI risk management frameworks (such as NIST AI RMF) and cybersecurity standards (specifically, CISA CPG). This requires mandatory auditing of all automated changes and enhanced access control.
Architectural approach: why CRM is not the source of truth
A common mistake is attempting to use CRM as the sole repository for all customer data. CRM systems are not designed to aggregate specific financial or legal data from other platforms, and such overloading leads to duplicated logic and performance degradation.
Instead, implementing a dedicated Master Data Management (MDM) layer is recommended. A centralized MDM hub collects data from all sources, cleanses it, eliminates duplicates, creates a single "golden record," and distributes it to other systems via APIs or integration buses.
Steps for businesses
To build a reliable customer master data management system, enterprises should adopt a centralized MDM architecture, establish clear Data Governance roles, and align security policies with AI risk frameworks.
Steps for implementing an MDM system
- Assessment of the current state and inventory of data sources.
- Development of Data Governance policies incorporating AI and cybersecurity requirements.
- Selection of the MDM technology platform.
- Phased integration of systems, starting with pilot projects.
- Integration with cybersecurity tools and SIEM systems.
- Regular staff training and continuous data quality auditing.
Prepared by a Software Ukraine member. Original publication.