How to Leverage Secure Data Collaboration Across Teams
Building a new apartment complex requires a lot of moving parts. Contractors each provide their own schematics, pricing, and labor, the city needs to review the plans and grant a building permit, and the commissioner must be kept up to date on the progress and total cost. If the stakeholders involved in this project are not given access to the same information – floorplans, budget, timelines, etc. – there’s almost no way it’ll be completed.
Like those involved in a construction project, today’s data teams must be able to collaborate efficiently and securely in order to drive business goals and objectives. This requires secure data collaboration capabilities that streamline teamwork without sacrificing data security.
What is Secure Data Collaboration?
How can data – especially sensitive data – be aggregated in a safe and accessible manner for collaborative usage?
Secure data collaboration is the process of collecting and connecting data from different sources to be shared among users for a common purpose. This process allows you to combine data from disparate sources in order to generate new insights. It also gives stakeholders across teams, verticals, and even separate organizations timely access to data that can help drive important projects.
Securely combining data sets without hindering accessibility enhances your ability to collaborate across teams. For example, a developer working with your organization’s user experience (UX) team to troubleshoot a product interface issue could leverage a collaborative data repository to ensure they’re seeing the same user metrics as their UX counterparts. As long as access is provisioned based on the principle of least privilege, this developer and UX team could work together to safely translate the user data into new and improved data-driven product features.
Data Collaboration vs. Data Sharing
While these two concepts sound virtually the same, data collaboration and data sharing are not exact matches.
Data collaboration, as we’ve examined, is a process that collects and connects various data sources to augment collective work. This is a process focused on the holistic combination and accessibility of data from different sources. It doesn’t necessarily involve a transaction between parties.
Data sharing, on the other hand, is a more transactional process. Simply put, data sharing is the process of provisioning information from one party to another – either internally, externally, or via a data exchange platform. This means that data sharing can be carried out through a collaborative medium, but in the end it is still the transaction that gets data from one party to another.
Types of Secure Data Collaboration
How are today’s teams leveraging secure data collaboration to work together with their peers and drive collective success? Here are a few common methods of data collaboration:
Data exchanges, sometimes referred to as data marketplaces, are a form of data collaboration that connect data sources in a repository from which various users can access and share. Data can be safely uploaded, requested, and accessed by a range of stakeholders within and/or across organizational boundaries.
Think of a data exchange like a local farmers’ market. Organizers allow people to bring their own wares to a dedicated collective space in which they can buy, sell, and trade them – in accordance with the market’s rules – with anyone else in the market.
Every data exchange includes data providers and consumers, as well as regulation and compliance implications to ensure that exchanges are secure. Some data exchanges are created for users to buy and sell data from one another, while others are strictly for sharing without monetary compensation. Regardless, providing a secure space for collaboration enables teams to more efficiently share their resources with one another.
A data cooperative (or co-op) is a repository where various individuals, businesses, and/or other types of organizations can simply upload their first-party data into a large pool of collective information. Where data exchanges are built to share data for specific purposes or money, data co-ops are focused on the collective results of data combinations.
A data co-op is like a public review service. Users can share their first-hand experience or information to contribute to the collective, while simultaneously using others’ information to inform their own decisions. A restaurant with many 5-star reviews will be of interest to a foodie, and their own honest review could influence future diners to visit the establishment.
There is no direct transfer of information in a data co-op. Instead, data is uploaded to the repository for group members’ use. This creates a diverse source of data that can provide benefits to anyone who is given access to it. To maintain the integrity of the co-op, there must be guarantees of data quality, data privacy, and compliant access applied to both contributors and consumers.
Data Clean Rooms
Data clean rooms take the concept of a data co-op and ramp up its preventive security measures. Sometimes referred to as “technical data environments,” clean rooms provide groups with the opportunity to aggregate vast amounts of anonymized data for mutual benefit.
Using data anonymization tools, the data added to clean rooms is stripped of any of its identifying factors that could link it back to whoever generated it. This is all done with the intention of garnering insights while keeping sensitive personally identifiable information (PII) secure and private.
Data clean rooms are used frequently by content platforms like social media, streaming services, and ecommerce websites. They can interact with advertisers without giving up their users’ personal information – just their anonymized search data. This can help drive business opportunities without violating user privacy.
Improving Secure Data Collaboration
Ensuring that data collaboration is carried out in a secure and compliant manner is crucial to the benefit of data users and subjects alike. Whichever form this collaboration takes, it needs to be subject to data access governance and compliance measures to ensure that users are sharing and analyzing only the data they have the right to access.
With the right tools, teams can apply important security measures like data discovery and classification, dynamic data access controls, and continuous data monitoring without sacrificing ease of collaboration. To learn how one healthcare non-profit enabled collaborative public Covid-19 research without risking the privacy of protected health information (PHI), read this case study.