Following its 77th session, the UN General Assembly cited economic unrest, geopolitical developments, and the continued fallout from the COVID-19 pandemic and the climate crisis as the cause for the “fractured world” we find ourselves in.
Despite seemingly insurmountable challenges and increasing fragmentation, world leaders quickly identified the solution to such a fracture. They rightfully championed collaboration as the cement that could strengthen individuals, organizations, and industries against current and future challenges, citing several “pockets” of collaboration that prove what can be achieved when we are able to work together.
Hello collaboration, goodbye fragmentation
Just as world leaders can come together to unlock new insights, so too can organizations across a multitude of industries. Good intentions alone won’t make this possible; for true collaboration, organizations need to be able to pool their resources – in this case, their datasets – in a way that’s safe, secure, and benefits everyone involved.
So, rather than centralizing and risking their data, organizations should work on a federated data infrastructure that allows them to reap the rewards of data collaboration whilst protecting their intellectual property and other sensitive information.
There is still a misconception that these assets need to be shared between parties for them to garner any value from them. This misconception has led many organizations to view data collaboration and data sharing as one and the same when in reality, they are wholly different concepts.
Data sharing asks organizations to share their data with other parties, many of whom could be their direct competitors, so that all parties can access insights from combined datasets. The problem here is that it’s not just the data they need that’s on offer – third parties could see and steal intellectual property. Beyond the implicit trust required to make this happen, there is also the issue of regulatory compliance that makes data sharing, and any dreams of scaling or automating, hard to achieve.
Federated data collaboration, on the other hand, removes the degree of risk that’s common among data sharing agreements. It does this by making only specific data accessible to third parties without the need for it to be moved from the custodian or risking the integrity of other data in the process. Groups engaging in a federated data ecosystem can rest assured that their sensitive data is safe and protected while still mining previously unattainable insights from the unison of their dataset with others in their ecosystem.
The road to successful collaboration
Federated learning can be integrated into an organization’s data or machine learning stack, making the collaborative potential only as limited as the imagination of those involved. Data ecosystems can span individual departments, multiple partners, and even geographical and industry boundaries.
Internal collaboration is one such example for those looking to collaborate on scattered datasets within a single organization. Here, organizations with offices across different geographies that have to contend with various regulations can overcome the hurdles of moving, copying, or centralizing their data. Instead, through federated data, they can collaborate on a single source of accessible data that better informs customer knowledge, marketing decisions, operational effectiveness, and more.
For any data collaboration efforts that span beyond the confines of a single organization, the data ecosystem must be built on a foundation of a federated infrastructure that ensures security and privacy as standard. By implementing a federated infrastructure, organizations can also rest assured that all activities are logged for auditing, compliance, and other necessary oversights. Beyond this, all parties must agree on strict asset policies, privacy controls, and similar measures to protect all data in the ecosystem.