Existing independently of any application, autonomous data controls itself, protects itself, and describes itself. Because of this autonomy, cross application usage and reporting are both simplified and accomplished more readily. However, because it functions counter to the paradigm that has existed for the past four decades, implementation within integration-based data architecture can be challenging.
Fortunately, API connectors can make this easier to accomplish.
What’s the Point of Autonomous Data?
Before autonomy was introduced, data was explicitly tied to the application with which it was created. This goes back to the way computing evolved. Computers were designed to rely upon applications to generate data, thus data became secondary to the application. This mindset continued as database technology emerged, likely because it was easier to continue thinking this way.
In some ways, it was something of an, “If it ain’t broke don’t fix it,” situation.
However, as digital transformation emerged and the sheer volume of data has become overwhelming, organizations are clamoring for tools with which to manage it more efficiently. In other words, the value of data is now recognized for what it is: the end–all and be-all of computing.
After all, the overriding goals of computing are the creation, analysis, and implementation of data in some fashion. Had early developers thought this way, the concepts of autonomous data and data centricity would likely have emerged much sooner, if not right off the bat.
The Benefits of Autonomous Data
Self-service insight for users is key among the advantages of autonomous data, as it becomes easier and faster to accomplish. Autonomous databases are also easier to secure because they keep track of access. They are also far more flexible, so capacity can be added or reduced in accordance with the needs of a business unit. Manual installation and analysis are eliminated, which delivers critical insights to decision-makers far more rapidly.
Active metadata support enables autonomous data to notify its managers of anomalies and repair itself (in some cases) when errors are perceived. Further, because autonomous data is app-independent, protecting it and managing it is more readily accomplished. What’s more, its self-describing nature makes autonomous data easier to locate and recognize, with whatever tool a user might have at hand.
Application integration without copying databases becomes possible with autonomous data. Users can draw from a central database, which speeds the operations and the development of applications and automations significantly. Given that these efforts can consume half the time and resources allocated to a major IT initiative, it becomes easy to recognize the technological efficiencies resulting from the widespread adoption of autonomous data.
Data silos are eliminated with autonomous data because the data is readily available to all authorized users. Despite the development of numerous applications designed to eradicate silos, the reality is that these solutions needed their own databases to function, thus creating more silos, rather than eliminating them.
Agility is increased substantially, as users have direct access to the data needed to perform their tasks. Freed of the need to copy data to support integration efforts, IT personnel can focus on creation and innovation, rather than incessant duplication. Meanwhile, the immediate needs of users are met because users can glean insights from data without calling upon the IT group to retrieve data and package it according to their needs. The centricity afforded users by autonomous data also enables the creation of custom APIs with simple interfaces in no-code/low-code environments.
Simply put, autonomous data disrupts the inefficiencies of the traditional cart and horse paradigm by prioritizing the cart — as it should have been all along.