From Sharing to Safeguarding Data

Data custodianship is a balancing act between innovation, privacyandcompliance. Consent management, Data audit and Access control are someofthe key parameters that can drive up corporate risk besides costs in governanceand implementation. Here is our perspective.

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Back in the day data sharing was a burgeoning frontier. Businesses, excitedbythe promise of centralized relational databases, eagerly pooled customerinformation to streamline operations and enhance services. This was theagewhere querying vast amounts of structured data brought unprecedented insightsand efficiencies.

As the internet exploded, so did the possibilities for data sharing. E-commerceplatforms and early social networks began collecting user information onamassive scale in the name of ‘better customer service’. This data, like ahiddentreasure, was mined to understand consumer behaviour, target ads, andboostsales. Corporates loved it. We enjoyed the services that came with it. And thisisexactly where things started to go south, as far as data privacy mattered. Fuelledby social media platforms unstructured data, like social media posts and videos, were collected at a scale that is beyond imagination, and the lines betweenuseful insight and privacy invasion was erased.

Now, with little or no regulations, data misuse became rampant. Personalinformation is shared without consent, leading to targeted advertising that feelsintrusive and at times, manipulative. Data has become the new currency,andoften without transparency. Data leaks and breaches led to concerns about data privacy practices and thesecurity of social networking platforms. For the first time we became aware aboutthe risks of releasing non-anonymized user data leading to ethical questions.

Governments worldwide responded with stringent data privacy laws. TheEuropean Union’s General Data Protection Regulation (GDPR), enacted in 2018,set a global benchmark, enforcing strict data handling practices and grantingusers 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 isabalancing act between innovation and privacy. Now, this is where Quasi CentralData Management comes in. Who can store user data is a matter of strictcompliance and regulation. Consent management, Data audit and Access controlare some of the key parameters that can drive up corporate risk besides costsingovernance and implementation. But the show must go on.

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

This is where Quasi-Central Database System finds its place; a hybridmodelcombining elements of both centralized and distributed database architectures. A

system that strikes a balance between centralization and distribution has its ownset of challenges and risks.

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

Technically, it may also improves query handling as the data is managed datalocally, aka Edge Computing, reducing latency and improving performanceforlocal operations compared to fully distributed systems. A sort of win-win situationadhering to legal requirements while maintaining user trust in anincreasinglydata-sensitive world.

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

Use Cases for Quasi-Central Database System

●  Consumer Durable Retail Chains: Each store might have its own databasefor local transactions but periodically syncs with a central databaseforinventory, sales data, and analytics.

●  Automobile Industry: Individual franchise maintain their local customerrecords but synchronize with a central database for aggregated data andcomprehensive service history.
●  Distributed Organizations: Companies with multiple regional offices mayuse a quasi-central system to allow each office some independencewhilemaintaining 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 thatblends centralized control with distributed data management, making it suitablefor various scenarios where balancing local autonomy and central coordination is crucial.