Implementing AIOps to Boost Company Productivity
Artificial Intelligence for IT operation (AIOps) is an emerging, soon to be the buzzword of the digital world. This multi-layered technology platform embraces big data analytics, machine learning, and Artificial Intelligence to solve IT issues and enhance operations and has already been successful in advancing and automating the modern IT centered solutions. It also improves IT performance, infrastructure, cloud, and processes with lesser stress.
Typically the AIOps architecture comprises a central data platform for aggregating raw logs and data from different monitoring tools. It uses machine learning algorithms to help identify patterns, reduce noise in the collected data, analytics to help IT firms see and manage multiple systems from a central interface, and lastly, automation tools that allow IT to communicate status, route issues, and auto-respond to common problems.
Automation also helps in delivering insights in a prompt, effective, and efficient manner, thereby augmenting organizational culture change. Additionally, these architectures generally provide a data lake or a similar repository to simplify the collection, manipulation, and dispersion of the data.
Irrespective of the application, AIOps solutions can be grouped into two forms viz, domain-centric, and domain agnostic. Domain-centric AIOps focus on specific data types and sources, e.g., Kentik, while domain-agnostic AIOps are more open and ingest a variety of data points, e.g., Scalyr, Moogsoft.
AIOps environment is boon to companies where increasing IT complexities portray traditional, domain-centric monitoring, and operations management insufficient as they fail to correlate the outpouring and complex data created by various IT domains. These data might be infrastructure data, application data, business metrics, and IT service management (ITSM) data. Being instrumental in bridging and managing multi-cloud and multi-vendor circumstances, AIOps will empower support teams to address the challenges proactively and monitor them. Also, due to sophisticated machine learning abilities, AIOps can report on alerts as well as focus on the rest of the outlier operating conditions.
Besides, these platforms unleash a few couples of other benefits like data security and detection of anomalies on user, application, and infrastructure level. Due to its prescriptive and predictive functionality, the concerned department can mine more data centers and hubs, which shall aid in deriving better actionable insights. It eases the possibility of remote work and digital business transactions, which is now crucial than ever due to coronavirus pandemic. Lastly, it helps in providing an enhanced employee experience for better productivity and talent retention.
So, to sum up, leveraging AIOps can help organizations to upgrade from silo-ed space to a collaborative network. Without it, companies with obsolete data and lack of application, user-oriented framework, will suffer a collapse when trying to integrate Artificial intelligence or machine learning systems in it. Hence it is now mission-critical to invest in an AIOps environment.