From Sharing to Safeguarding Data

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Back in the day data sharing was a burgeoning frontier. Businesses, excited by the promise of centralized relational databases, eagerly pooled customer information to streamline operations and enhance services. This was the age where querying vast amounts of structured data brought unprecedented insights and efficiencies.

As the internet exploded, so did the possibilities for data sharing. E-commerce platforms and early social networks began collecting user information on a massive scale in the name of ‘better customer service’. This data, like a hidden treasure, was mined to understand consumer behaviour, target ads, and boost sales. Corporates loved it. We enjoyed the services that came with it. And this is exactly where things started to go south, as far as data privacy mattered. Fuelled by social media platforms unstructured data, like social media posts and videos, were collected at a scale that is beyond imagination, and the lines between useful insight and privacy invasion was erased.

Now, with little or no regulations, data misuse became rampant. Personal information is shared without consent, leading to targeted advertising that feels intrusive and at times, manipulative. Data has become the new currency, and often without transparency.

Data leaks and breaches led to concerns about data privacy practices and the security of social networking platforms. For the first time we became aware about the risks of releasing non-anonymized user data leading to ethical questions. Governments worldwide responded with stringent data privacy laws. The European Union’s General Data Protection Regulation (GDPR), enacted in 2018, set a global benchmark, enforcing strict data handling practices and granting users control over their data. Similarly, the California Consumer Privacy Act (CCPA) empowered users with rights over their personal information.

Data ownership is the new thorn in the flesh. Today, data custodianship is a balancing act between innovation and privacy. Now, this is where Quasi Central Data Management comes in. Who can store user data is a matter of strict compliance and regulation. Consent management, Data audit and Access control are some of the key parameters that can drive up corporate risk besides costs in governance and implementation. But the show must go on.

So, where do we go now is the big question. Who can ‘store’ customer data and with what levels of anonymity. With both CCPA and GDPR in play, there will always be a common mantra of exposure… “your data sharing partners can put your business out of compliance…”

This is where Quasi-Central Database System finds its place; a hybrid model combining elements of both centralized and distributed database architectures. A system that strikes a balance between centralization and distribution has its own set of challenges and risks.

We leverage the advantages of both centralized and distributed systems while minimizing the risks associated with Data Ownership, Data Privacy and Data Governance as I’d pointed above.

Technically, it may also improves 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.

Keeping local databases synchronized with the central hub can be complex and challenging especially when dealing with conflicts and ensuring data consistency, we leave that discussion for another day – ‘How our Quasi-Central Database Systems has ‘Zero-Synchronization Complexity’.

Use Cases for Quasi-Central Database System

• Consumer Durable Retail Chains: Each store might have its own database for local transactions but periodically syncs with a central database for inventory, sales data, and analytics.
  
  
• Automobile Industry: Individual franchise maintain their local customer records but synchronize with a central database for aggregated data and comprehensive service history.
  
  
• Distributed Organizations: Companies with multiple regional offices may use a quasi-central system to allow each office some independence while maintaining a centralized repository for critical business data.
  
  

Centralised Vs Distributed Vs Quasi-Central Database

The Quasi-Central Database System is the future of data management that blends centralized control with distributed data management, making it suitable for various scenarios where balancing local autonomy and central coordination is crucial.

Ravi Kumar,
CEO | Cubastion Consulting