Data Collaboration Overview
This page provides a brief overview of Data Collaboration
Last updated
This page provides a brief overview of Data Collaboration
Last updated
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Dataware is the emergence of a common group of technologies that solve data related problems across many business use cases. One of the most exciting new categories within this group is Data Collaboration.
Data Collaboration solves the costly, time-consuming, and ineffective integration processes born from silo-ing your data in a traditional app-centric environment. Instead of your data serving your applications, data collaboration refocuses and pivots to a model where your data is at the forefront and is being served by your apps (Image 1).
Harnessing this power, Cinchy connects unlimited data sources within a networked architecture, offering persistent delivery of real-time solutions, without complex integrations. And the more data you connect into your data network, the more powerful your processes can be.
When a new IT project is green-lit, you often pay a hefty fine called the integration tax where you're continuously building new integrations between applications, just to reuse data that is already available in your systems.
Over time, this never-ending cycle of copying data between fragmented apps gets more complex, resulting in delayed launches, budget overruns, and “shadow IT” projects.
This process of making copies now consumes up to 50% of the resources on large IT projects, and it's the reason that delivery often takes months, sometimes years, and costs millions (Image 2).
How can this be fixed?
With data collaboration, you shift your approach from integration for data sharing, to access for data collaboration (Image 3).
For every app you build where you leverage data collaboration, you’re able to access the network to reuse information for future apps. Your IT team will find that previous projects have already connected many of the data sources they need to the network.
Cinchy's data collaboration platform does for application development what the power grid does for individual buildings. In the same way that buildings no longer need to generate their own power thanks to the power grid, with a data collaboration platform applications no longer need to manage, integrate, and protect their own data (Image 4).
Organizations can build data collaboration applications in half the time, while enabling effortless and copyless data sharing across applications. Using Cinchy is unique in that it eliminates the "integration tax" that today consumes half of most enterprise IT budgets -- that is to say, data collaboration makes integration obsolete (Image 5).
Now, instead of connecting apps to gain access to data through costly point-to-point integration, your apps serve your data by leveraging and connecting it all together via Cinchy. In this way, you can still gain the best usage out of your apps through zero-copy integration while avoiding the disadvantages of data silo-ing. You have both full access to and full control of your data (Image 6).
Simply putting pipes between data silos, and centralizing a few housekeeping tasks, is not data collaboration. What that's actually doing is leading you down a path of managing endless copies. True data collaboration not only connects your data but upgrades it as part of an interconnected, autonomous network.
Autonomous data exists independently of any application. It is self-controlled, self-protected, and self-describing. This creates a number of benefits compared to traditional app-dependent data, including the ability to simplify cross-application usage and reporting. And when you use autonomous data in an interconnected network, wherein individual contributors maintain their roles and priorities as they apply their unique skills and leadership autonomy in a problem solving process, you get: Collaborative Autonomy.
Collaborative Autonomy is thus the principle underpinning Collaborative Intelligence, the entire basis of Dataware and Cinchy.
Individuals are not homogenized, as in consensus-driven processes, nor equalized through quantitative data processing, as in collective intelligence. Consensus is not required. Problem resolution is achieved through systematic convergence toward coherent results. Collaborative intelligence relies on the principle of collaborative autonomy to overcome “the consensus barrier” and succeed where other methods have failed.
One of the most significant advantages of dataware is the ease with which data owners can set universal data access controls at the cellular level, and automate data quality (Data Governance) with a “golden record” of data.
In effect, it is removing the need to maintain access controls within individual apps and centralizing these functions in an incredibly efficient way.
Compare this with designing and maintaining controls within thousands of apps and systems independently. It’s not only incredibly challenging and costly, but virtually impossible to maintain consistency (Image 7).
Dataware is a game changer for IT delivery: it produces network effects, where each new solution actually speeds up delivery times and reduces costs
Network-based designs scale beautifully and become more efficient as they expand. Consider the human brain; its neuroplasticity helps it learn more as it grows. The more interconnected it gets, the better. The neural pathways are reorganizing themselves such that the fewer connections, the higher the intelligence, because information is more efficient to operationalize.
It's the same with dataware. The more you connect your data, the harder and better it works. It's the ability to have the platform take care of your whole data journey and transformation. You don't have to manage the changes of all your applications, regression testing, the QA you have to go through, etc.
And it's also your time machine - you can have applications based on different points in time of your data, and it's all done through network-based design.
Now that you know how efficient and secure things can be there is no going back.
Let's build the connected future, together.
