What is
Data Collaboration?
Data Collaboration defines an approach to digital innovation where diverse groups of contributors—both humans and digital systems (including AI/ML)—act to enrich and extend data and generate Collaborative Intelligence which can be activated with minimal friction.
In addition, by reducing (or in some cases eliminating) data silos and duplicate data, the owners of the data products involved in Data Collaboration gain meaningful control of their information, including access, portability, and deletion.
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Use cases for Data Collaboration and Collaborative Intelligence include AI enablement, compliance automation, smart applications, system augmentations, predictive analytics, agents and automations, and legacy system modernization.
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Data collaboration empowers multiple stakeholders to deliver a shared outcome while retaining control of their data
Collaboration everywhere,
but not on data?
Many folks will remember when writing a business document involved sending email attachments back and forth among contributors.The versions immediately got out of sync and it was basically a nightmare for everyone involved.
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Then came Google Docs, Dropbox, and Asana and we quickly learned the simplicity and power of real-time collaboration between people and software.
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Interestingly, collaboration seems to be happening everywhere except on the data that we use to power our organizations and digital technology.
So what's the deal?
Problem 1:
Apps create silos
You've probably heard the expression "There's an app for everything"?
Here's the full version:
"There's an app for everything, and a database for every app"
Even small businesses now use 100s of apps to run their business while enterprise and public sector organizations maintain thousands.
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The big problem with silos is that when we need to bring the data back together (to build apps, dashboards, and automations) we generally do it by making copies.
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Data warehouses and integration hubs do not prevent the proliferation of app-specific data silos
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Current approaches to application development are based on the unrestricted exchange of copies between app silos
Problem 2:
Silos require copies
The routine exchange of copies between applications is known as point-to-point data integration.
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Doing this at scale makes it nearly impossible for teams to work on the same data in order to deliver a shared goal e.g. building a new app, dashboard, map, or automation.
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Copies also make data access, governance, privacy, and compliance a HUGE challenge.
Towards
data collaboration
The good news is that organizations are already adopting new standards and technologies that make it possible to reduce and even eliminate silos and copies in order to support true data collaboration.
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But how do we encourage more innovators and developers to join the movement?
Adoption will be driven by the natural incentives of saving time and money.
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It's time to meet The Data Collaboration Flywheel...
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Shared data architectures are replacing data silos and
copy-based data integration
THE DATA COLLABORATION FLYWHEEL
Building new digital solutions from a foundation of control kickstarts a cycle of compounding efficiency
Smarter solutions protected by access controls and powered by Collaborative Intelligence drive demand for more collaboration.
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CONTROL
The elimination of copies makes meaningful control and ownership possible for data stakeholders.
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INTELLIGENCE
Outcomes
Data ownership
Data minimization
Federated governance
Democratized IT
Increased capacity
Simplified compliance
Improved auditability
AI enablement
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ACCESS
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COLLABORATION
Collaboration accelerates data enrichment and the production of Collaborative Intelligence.
Control creates agency and encourages the granting of access for data collaboration.
WORK IN PROGRESS
While the potential for Data Collaboration is incredibly exciting, it would be a mistake to assume that the shift from data silos to controlled environments will happen overnight.
Similarly, it would be naive to assume that the citizens, nonprofits, and businesses who contribute data to digital ecosystems have the time or inclination to manage access (hey there, Privacy Paradox).
Imagine if every app required end users to maintain a unique set of access controls - it wouldn't be long before we'd all be required to set hundreds or even thousands of controls, or to be more accurate, give up and not do any of this. 🤣
It's likely that we'll see the emergence of new professions (e.g. Data Access Consultants) that will help to fill the gap, or perhaps AI agents will take on the role of our "robotic data custodians".
But as the futurist William Gibson once observed; "The future is already here, it's just not very evenly distributed."
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At the Alliance, our goal is to help scale-up the technologies, standards, protocols, and best practices that will make Data Collaboration the new normal.