The data sovereignty dilemma: Protecting data without sacrificing global data quality
Data sovereignty is growing in popularity. It’s due to the rise of AI, which is fuelled by data, evolving geopolitics and a growing dependence on a small number of foreign cloud providers. It means maintaining control over data is becoming increasingly important not only for businesses, but also for national security.
The rise of sovereign and regional clouds
A growing number of states are developing sovereign cloud infrastructure to ensure that citizen and business data remains within national borders and subject to domestic laws and regulations. Countries including France, Germany, India, the United States and Switzerland are among those pursuing sovereign cloud initiatives.
Once it’s in a sovereign cloud data can’t be accessed by foreign governments or single cloud company monopolies. This way states gain greater control over critical data, strengthening their ability to safeguard national interests in an evolving geopolitical landscape. Additionally, it ensures that organisations operating within their jurisdiction can access the data and infrastructure they need to remain competitive.
Regional cloud initiatives are also emerging. In Europe, for example, France and Germany have started developing Gaia-X - a project designed to create a secure, federated system for sharing data and connecting cloud services. It’s built around European standards and regulations, including GDPR.
Data silos are booming
Sovereign and regional clouds support the creation of data silos, because those operating across borders often need to separate and therefore end up duplicating data across different regional systems to comply with local storage laws. This can complicate building a single customer view (SCV), let alone deliver a comprehensive global view of their data. It limits an organisation’s ability to provide insight that can aid improvements in operational efficiency and in evolving their services. According to a study by McKinsey & Company data silos are estimated to cost the global economy $3.1 billion annually through lost revenue and productivity.
Sovereign clouds can also create challenges around data portability and interoperability, because shifting workloads between national cloud environments can be difficult, as data formats and technical standards may differ. And anyway, regulatory requirements may restrict whether data can be transferred across borders at all. Also, differences in APIs are a common issue with organisations often having to use various APIs to manage data.
All of these factors can have a significant impact on data quality, particularly contact data such as customer names, addresses, telephone numbers and email addresses, which can quickly become outdated or inconsistent across siloed datasets. Then there’s wider challenges around compatibility, consistency and duplication caused by siloing that sovereign clouds create, making it harder for organisations to maintain accurate, reliable, unified data.
As more countries develop sovereign clouds organisations with operations worldwide are increasingly required to silo customer data across different jurisdictions. This creates a growing need for consistent global data quality, to ensure that customer communications and marketing activities are based on accurate, reliable information, as well as enabling AI to generate insight on customers that can improve services and operations. Ultimately, AI will stop hallucinating and generate reliable insights when it has access to clean data.
Explore global data quality platforms
With data silos continuing to proliferate, access to data quality platforms capable of cleansing and verifying names, postal addresses, email addresses and telephone numbers in real time across countries and territories is becoming increasingly important. Ideally, these platforms should work securely via the web or on-premise, provide a browser-based data quality console with fast, scalable processing capabilities, and offer the flexibility to meet the specific needs of the end user.
This approach makes it possible to maintain global data quality and consistency with siloed data, with data parsed, matched, cleansed and profiled to the same high standards, while still complying with the specific rules and regulations governing sovereign and regional clouds.
Advanced fuzzy matching for data deduplication
Importantly, a data quality platform should also provide advanced fuzzy matching capabilities to support the deduplication of global data. This is particularly important because, as already touched on, data siloing can lead to duplicate customer records, which can confuse AI applications and increase the time and cost associated with customer communications. Sending duplicate messages to customers can also undermine the customer experience and damage the sender’s reputation.
Once duplicate and inconsistent records have been consolidated and removed, organisations can create a ‘single user record’ that provides an optimised SCV that enables AI to derive more accurate insights.
In summary
The proliferation of sovereign clouds, each governed by their own local regulations and data formats, presents significant challenges for organisations with a global customer base. The main one is the growing need to silo customer data across different jurisdictions, which can create compatibility, consistency and broader data quality issues for those operating internationally.
A key solution for maintaining data quality and consistency across siloed records is a global data quality platform that can be securely accessed via the web or deployed on-premise, while providing advanced fuzzy matching capabilities to identify and deduplicate records. It will deliver more accurate and reliable customer data, enabling organisations to gain deeper customer insight, provide more effective communications, and improve services and operations worldwide.
