Autonomous data is data that exists independently from 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. However, autonomous data can be very difficult to implement in an integration-based data architecture—because it essentially goes against everything that’s been true about data for the past 40 years.
Before autonomous data, all data was tied to the specific application that created it. This is an artifact of the way data technology developed from the early mainframe computers: first came the app, then came its data. As database technology advanced, this relationship remained the same. In all likelihood, this was simply a matter of innovative inertia: the app-data relationship worked well enough, and it wasn’t until relatively recently that there was even a demand for app-independent data.
It’s interesting to imagine what today’s technology might look like if data had been the driving force from the start; it’s very likely that autonomous data would be the norm and that applications would have developed quite differently as a result. Had that been the case, today’s technological landscape would look far different than it does.
The benefits of autonomous data
Autonomous data supports active metadata, allowing it to automatically notify data managers of anomalies and in some instances even repair itself should it detect errors. Its independence from external applications makes it far easier to protect and manage, as you can work with the data directly and not have to worry about any app-related restrictions. Because autonomous data is self-describing, it’s easy to find and identify regardless of the tools being used to access it.
The app-agnostic properties of autonomous data make it a key component in ending today’s productivity-throttling paradigm of point-to-point integration. In a world built on autonomous data, there would be no more integration efforts or copying databases in order to get multiple applications to work together. These integration efforts currently eat up half the time and resources of any major IT project, so just imagine the sort of technological efficiency that would exist in a world built on autonomous data.
Want to learn more about Autonomous Data? Watch Cinchy Co-Founder & CEO Dan DeMers explain it in detail in Episode 3 of our Data Fabric learning series!
But that’s not the world we have, and the reliance on integration-based data architecture actually makes it very difficult to use autonomous data. Today’s legacy systems just aren’t built to take advantage of it. Instead, effectively leveraging autonomous data is best achieved with the help of additional infrastructure technology—namely, the Data Fabric.
This isn’t to say that you absolutely need Data Fabric to use autonomous data, it’s just that it makes it far more efficient and effective when the two technologies are used in tandem. Data Fabric provides the architectural underpinnings to build an interconnected network of data, and autonomous data can then exist independently within that network to interface effectively with any application you have connected to it.
The results are impressive and immediate. IT projects can be completed in half the time and at half the cost of traditional integration, and as your Data Fabric grows you’ll find the flexibility of autonomous data will allow for innovative problem-solving approaches.
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