Have you ever been notified that your personal data may have been compromised? If not, you must live on a remote island (with no internet access!) since data breaches seem to happen about as frequently as traffic jams these days. As a matter of fact, a recent survey by Gemalto shows that 75% of consumers believe that companies do not take the protection and security of their data very seriously.[i]
At the same time, data is what fuels the digital economy. Technology advances like artificial intelligence (AI), the internet of things (IoT), and virtual reality (VR) are making it easier to collect and analyze data than ever before. And when data from diverse sources is shared, combined and analyzed across organizational boundaries, it opens the door to a new world of deeper insights and shared value. That begs the question – how can organizations continue growing shared value while keeping private data safe?
It’s a tricky challenge that IT leaders across all industries need to solve, which is why data trusts is the topic for this installment of “How to Speak Like a Data Center Geek”
What is a data trust?
A trust is essentially a legal mechanism or agreement to enable a third party to hold and manage assets on behalf of a beneficiary. For example, as part of estate planning, you may work with an attorney or financial planner to create a trust for distributing your assets to your children upon your death and appoint your sister as the trustee. In this case, you (the “settlor”) agree to transfer your assets to your sister (the “trustee”), who will now manage those assets for the benefit of your children (the “beneficiaries”). The trust becomes the new legal owner of your assets and your sister becomes the new manager or fiduciary. As the fiduciary, your sister is ethically bound to act in your children’s best interests when it comes to making decisions about your assets.
A data trust works in much the same way. Data is deemed to be a formal asset held by individuals or organizations (the “trustors”). The trustors agree to grant trustees the right to make decisions about how the data can be used on behalf of beneficiaries. For example, what data can be shared with whom and for how long and for what purpose. Beneficiaries could include researchers, partners or developers, as well as those who could benefit from what is created from the use of the data, such as citizens of a smart city.
Types of data trusts
Data trusts can be structured in a number of different ways to address the needs of the trustor and beneficiaries. Here are a few examples:
1. Personal Data Store (PDS): Personal data stores are services that let an individual store, manage and deploy their key personal data in a secure and structured way. The individual retains ownership and control of their data and decides which applications have permission to use the data. Examples include MyDex, SOLID, CitizenMe, Hub Of All Things (HAT), digi.me and Meeco.[ii]
2. Public Benefit Trust / Data Collaborative: A data collaborative is a collaboration model in which participants from different sectors exchange their data to create public value. Examples include the Health Data Collaborative, Global Forest Watch, Cuebiq Data For Good, Counter-Tracking Data Collaborative, and Data For Climate Action.[iii]
3. Data Cooperative / Industry Data Trusts: A data co-op, or data commons, is a group that agrees to share its pooled data with all the members. All members have access to all of the data that is shared, with the benefit of getting better market insights on current and future conditions. Examples include the Grower Information Services Cooperative (GiSC), Salus Co-op (health data), Midata (health data), Springbot Exchange (retail) and Driver’s Seat (ride sharing).[iv] Industry data trusts may also evolve to more closely resemble data exchanges and include additional players such as alt data providers.
Data trusts and interconnection
Regardless of how a data trust is structured, the goal is the same – to provide maximum benefit to the intended beneficiaries safely and securely. Geeks are apt to recommend that you start with interconnection first when creating a data trust. How? By directly and securely interconnecting all the elements a data trust needs to work – people, things, locations, clouds and data – in proximity to each other at the edge. This will reduce the risk of security attacks and provide the added benefit of lowering latency and increasing performance for real-time data access.
Data trusts are likely to be part of larger digital ecosystems that include application services, data exchange and business processes such as productized APIs, digital payments, smart contracts and algorithms. This can result in multicloud, multi-partner, interactive workloads where latency directly impacts business capabilities and throughput diminishes as data volume increases. An Interconnection Oriented Architecture™ (IOA™) approach simplifies topology, reducing complexity by integrating services into one digital platform. It interconnects analytics, internet of things (IoT), digital payments and other digital services at the edge, where businesses interact with thriving ecosystems.
Download Equinix’s IOA Playbook to learn how leveraging IOA best practices make this happen.
Like to geek out? Check out our other “How to Speak Like a Data Center Geek” posts to binge on.
 Gemalto Thales Group, Infographic: Customer Loyalty, Trust and Data Breaches.
 Personal data store examples: Mydex, SOLID, CitizenMe, Hub Of All Things (HAT), digi.me and Meeco.
 Data collaborative examples include the Health Data Collaborative, Global Forest Watch, Cuebiq Data For Good, Counter-Tracking Data Collaborative, and Data For Climate Action.
 Data co-op examples include GiSC, Salus Co-op, Midata, Springbot Exchange and Driver’s Seat.