Where Lies The Next Generation Data Management

I’d written about how we ended up with laws safeguarding customer data. Now, in a world where ‘good customer service’ is the key differentiator between brands, and data is an important factor to providing one, how does an organisation become a custodian of customer data and yet remain compliant.

For data heavy companies that have amassed enormous amounts of data over the years, the challenges go beyond GDPR & CCPA. Bottlenecks, delays, and bad data are all too frequent and counter-productive. Not to mention Data redundancy – a challenge that can lead to revenue loss and inconsistencies in analysis and reporting.

Now these questions are relevant when your data is centralised. On the other hand Decentralised or Distributed databases also have challenges of their own. But if you can imagine how your database systems is built, in terms of ‘How’ is it accessed and ‘Who’ stores customer data, we may have a solution in a hybrid approach combining elements of both centralized and distributed database architectures. A system that strikes a balance between centralization and distribution and yet leverages the advantages of both centralized and distributed systems while minimizing the risks associated with Data Ownership, Data Privacy or Data Governance.

Technically, it may also improve query handling as the data is managed data locally, aka Edge Computing, reducing latency and improving performance for local operations compared to fully distributed systems. A sort of win-win situation adhering to legal requirements while maintaining user trust in an increasingly data-sensitive world.

Edge-Computing or Quasi-central database systems can have significant implications for data privacy and improved customer relations for Automotive, Telecom, Retail and Insurance & Financial Services sectors. I’ll list a few advantages and challenges.

Data Privacy Benefits of our Quasi-Central DBMS

1. Localized Data Control: Local databases in quasi-central systems can manage sensitive data independently, reducing the need to transmit personal or confidential information across the network. This localized control helps in complying with data privacy regulations that mandate keeping certain data within specific jurisdictions.
2. Granular Access Controls: With quasi-central databases, one can implement access controls specific to each local node, ensuring that only authorized personnel have access to sensitive information. This reduces the risk of unauthorized access and improves compliance with privacy standards.
3. Data Minimization: Local databases stores only the essential data needed for local operations, minimizing the exposure of sensitive information. This approach aligns with data minimization principles in privacy regulations, such as the GDPR.
4. Enhanced Security Protocols: Quasi-central systems can incorporate advanced security protocols tailored to each node, enhancing the overall security framework. This distributed security can make it harder for attackers to compromise the system as a whole.
5. Decentralized Data Handling: By distributing data processing tasks, quasi-central systems reduce the load on the central hub, thereby limiting the points of vulnerability where sensitive data might be aggregated and exposed.

Data Privacy Challenges in Quasi-Central Systems

1. Synchronization Risks: Periodic synchronization between local nodes and the central hub can expose data to interception or unauthorized access if not properly secured. Ensuring secure communication channels is crucial to protecting data privacy during synchronization.
2. Complex Compliance Management: Managing compliance across multiple local databases and the central hub can be complex. Different regions may have varying privacy laws, and ensuring that the system adheres to all applicable regulations requires meticulous oversight.
3. Data Replication Concerns: If sensitive data needs to be replicated across the system, ensuring that all copies of the data are equally protected can be challenging. Inadequate replication controls could lead to inconsistencies in data privacy protections.
4. Audit and Monitoring Complexity: Monitoring and auditing a quasi-central system for compliance with data privacy standards can be more complex than in fully centralized systems. Ensuring consistent audit trails and monitoring mechanisms across the system is essential.
5. Data Deletion and Retention: Implementing data deletion and retention policies in a quasi-central system requires careful coordination between local nodes and the central hub to ensure that all copies of the data are properly managed according to privacy laws.

The connection between Quasi-Central Database Systems and data privacy is significant, offering both advantages and challenges. By combining centralized coordination with localized data management, quasi-central systems can enhance data privacy through localized control and granular access. However, they also require careful management of synchronization, compliance, and security protocols to mitigate privacy risks.

See what our QCDS expert can show you over a cup of coffee.

Ravi Kumar,
CEO | Cubastion Consulting