Artificial Intelligence for IT Operations (AIOps) can be a real deliverance for overloaded IT departments. Applying advanced automation to countless routine IT processes will reduce the burden on IT departments and allow them to focus on more meaningful things, such as digital transformation, continuous integration and software deployment.
However, there is a problem here: AIOps requires the right data at the right time, but many of these data are either not ready or need a thorough revision. AIOps functions on the basis of data points such as logs and metrics of the system, performance statistics, events, real-time translation of operations and data related to incidents. However, many of these data may be incomplete or far hidden. That is, if the data is not at the proper level, AIOps may fail or, even worse, send technological solutions in the wrong direction.
Robotic data automation (RDA) can help solve this problem, writes Forbes. Unlike Robotic Process Automation (RPA), which automates business processes, workflow data, and user tasks, RDA automates data pipelines using bots.
According to a recent study by the analytical company Enterprise Management Associates (EMA), the forms of automation that are supported by AIOps include “workflow in IT” (60%) and “automation of Runbook modules or IT processes” (49%). Another 43% of IT professionals surveyed turn to AIOps for more intelligent notifications based on warnings.
However, AIOps is difficult to implement. Despite the obvious advantages, many people will find the implementation of artificial intelligence for IT operations too difficult. The main problems include the accuracy and availability of data, conflicts within IT, fear or distrust of AI, and lack of skills.
RDA solves AIOps-related data problems and also helps fill skill gaps.
The observability of data often depends on how many people can be thrown into quality assurance in the data pipeline, either by hiring more employees or engaging consulting firms. This increases the total cost of ownership and increases payback time, and this is where many AIOps implementations fail.
With the help of RDA, software bots can be deployed in data pipelines in order to simplify and abstract many data operations and machine learning. This is the key to data automation. Using software bots in pipelines and automated workflows, it is possible to achieve the data quality required for AIOps.