Use Cases

Collaborate over data silos and sensitive data

By September 3, 2021September 7th, 2021No Comments

While the value of data has become widely recognized, organizations still struggle to realize its full potential. Data is worthless on its own – it has to be accessible and usable by others otherwise it won’t create value. And that’s where things went wrong. 

With the rise of Big Data, organizations focused their efforts on collecting and maintaining data, rather than making it accessible and usable by others. Most failed to set up effective processes for data access and instead – as a result of incompatible technologies, poor governance policies, or simply no secure way to share data – set up data silos. 

Modern leaders understand the benefits of expanding their data ecosystem beyond their organization walls. But doing so has always been a daunting task riddled with risks and uncertainties – at least, until now. And that’s where Castalise comes in. A platform built to help you collaborate over data silos by simplifying privacy, compliance, and regulatory requirements while integrating different data assets and tools within a collaborative secure environment with built-in auditability and distributed ownership.  

Deliver secure and compliant access remotely

  • Set up a secure collaborative project environment for your team and partners 
  • Work with others across different jurisdictions while keeping your data safe in the cloud 
  • Maintain full control and auditability over your data 

Secure computation on confidential data sources

  • Ensure collaboration meets compliance and privacy requirements 
  • Avoid reputational and regulatory fallback from data breaches or data leaks 
  • Manage legal commitments, terms, and conditions 

Blinded scoring and pattern identification 

  • Cross-check entries between datasets without exposing raw values 
  • Run cross-party privacy-preserving queries to check for criteria or suspicious values  

Data aggregation and confidential data pools  

  • Pool your data without exposing it to other parties  
  • Aggregate different datasets with distributed data ownership and control 
  • Improve data quality by building richer and aggregate datasets 
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