GDPR
GDPR compliance ensures organizations meet European data protection requirements including data subject rights (access, rectification, erasure, portability), consent management, data minimization, and the right to be forgotten. This use case addresses managing fragmented customer data across systems, tracking complete data lifecycles from lead to customer, integrating data from all touchpoints and systems, maintaining audit trails, and enabling data discovery while respecting privacy rights and maintaining necessary compliance records.
The Challenge¶
Financial institutions face significant challenges in GDPR compliance:
- Data fragmentation — Customer data is scattered across multiple systems, making it difficult to track and manage
- Data subject rights — Must be able to respond to requests for data access, rectification, erasure, and portability
- Consent management — Tracking and managing customer consent for data processing across different purposes
- Data minimization — Ensuring only necessary data is collected and processed
- Right to be forgotten — Ability to delete customer data when requested, while maintaining necessary records for compliance
- Data lifecycle tracking — Tracking and tracing all activities and all facts around the end-to-end life cycle of a customer, starting as a lead or prospect
- Cross-system integration — Tying not only CRM systems to the EKG but also all their transactions, emails, documents, phone calls, time registration, contracts, invoices, budgets, costs, projects, SLAs, issues, tickets, market data, competitive analysis, interest graphs, pattern detection, ML and AI
- Audit requirements — Must demonstrate compliance with GDPR requirements and data processing activities
Traditional siloed systems make it difficult to maintain a comprehensive view of customer data while ensuring GDPR compliance.
Why EKG is Required¶
Enterprise Knowledge Graphs provide powerful GDPR compliance capabilities:
- Holistic data view — By creating a holistic view of the data within the organization, all customer data can be managed in one logical (but not physical) place
- Data subject management — Track all data related to a customer across systems and processes
- Consent tracking — Maintain complete records of customer consent and data processing purposes
- Data discovery — Allows for "discovery" of anything around a customer, enabling colleagues to learn about the customer and gain understanding or even knowledge, overseeing it all, seeing the customer and all their subsidiaries and global operations as one whole with infinite drill down capability
- AI-ready knowledge — That knowledge can not only be provided to humans but also to AIs
- Data minimization — Identify and manage only necessary data for each processing purpose
- Right to be forgotten — Identify all instances of customer data across systems for deletion when requested
- Compliance documentation — Maintain complete audit trail of data processing activities
Business Value¶
- Regulatory compliance — It would make any organization compliant with GDPR for instance
- Customer trust — Demonstrate commitment to data protection and privacy
- Operational efficiency — Centralized data management reduces complexity and improves data quality
- Risk mitigation — Avoid GDPR fines and penalties through effective compliance
- Data quality — Improved data quality through unified data management
- Customer insights — Better understanding of customers through holistic data view
- Competitive advantage — GDPR compliance as a differentiator in the market