There is a common misconception that data sharing is necessary when trying to derive value from data held across multiple organizations in a value chain. In reality, data sharing does not equal data collaboration.
Data sharing implies that useful data is owned by the organization that captured it and that making it available to other organizations across the value chain requires an unavoidable degree of risk. Federated data collaboration, meanwhile, allows organizations along the value chain to access and collaborate on data and AI and share their expertise without ever risking the integrity (i.e., privacy or intellectual property) of the raw data, machine learning (ML) capabilities, or other assets.
Data sharing also requires organizations to implement predetermined agreements to ensure required levels of privacy and protection are met. Doing so is typically costly and time-consuming and, in tandem with existing processes and policies around intellectual property risk and other factors, can stop data sharing in its tracks.
Even with implicit trust between all organizations and strict, legally-binding data sharing policies in place, data sharing can only derive so much value. Personal information simply cannot be shared due to regulations such as GDPR, and any data that can be shared loses its history and, therefore, its value as it passes hands. Provenance and sovereignty in particular are compromised when this happens, limiting the explainability and traceability of the data as stripping the owner of control. This is bad news for large businesses, many of which are finding themselves under increasing pressure to ensure data provenance and sovereignty.
Simply put, data is only valuable if it’s accessible and usable in a privacy-preserving manner, which is challenging to guarantee through data sharing. Data collaboration, on the other hand, allows just that: all the benefits of safe, secure cross-organizational collaboration without the need for data ownership to ever pass from one group to another.
Seamless, scalable, secure data collaboration
It’s no secret that today’s business challenges can only be overcome through collaboration and that a single organization should not lock away the data required for that collaboration. As such, shifting from a mindset of limited data sharing to true data collaboration should be a top priority for organizations looking to get the most from their data ecosystems. But to achieve this, all data owners and stakeholders must align to a shared goal that delivers a result greater than the sum of its parts.
At Apheris, we believe that data collaboration is key to unlocking the potential of AI and ML, and enabling advanced analytics. To realize that potential, organizations need the ability to process data across cloud, multi-cloud and hybrid environments – without the barriers and security issues associated with historic data sharing practices. Following the right privacy and security controls, data must be made accessible without ever having to leave the owner’s environment. This requires a federated approach, whereby data scientists are empowered to send their computations to the data rather than centralizing it, as is necessary for data sharing agreements.
All of this is only possible with good governance. With the right governance in place, there is no need for implicit trust or security concerns: security, privacy, compliance, access permissions, and more can be taken care of systematically. From there, organizations can explore data and unlock new insights while the data itself remains safe and secure.
After all, seamless collaboration should never come at the expense of security. Apheris’s federated data platform enables organizations to collaborate on distributed data while adhering to strict security and privacy standards, meaning they no longer have to choose between the two. Insights are accessed and shared, and data remains firmly in the owner’s secure environment. Should the need arise, the platform offers clear traceability of statistics, ML operations, and more to ensure trust and transparency.
Data sharing concerns quickly become a thing of the past when adopting a federated, collaborative approach, allowing data applications to be built, deployed and operationalized safely and securely, regardless of where individual datasets sit.