12 Steps Excellence In AIOps.

AIOps platform integrates big data and artificial intelligence or machine learning functionalities to improve or partially replace a variety of IT operations processes such as availability and performance monitoring, event correlation and root cause analysis, IT help desk activities, and automation. According to Gartner, by effectively implementing AIOps solutions, a company can approximate an integrated end-to-end picture of its changing IT environment and offer valuable insights into anticipated future events and incidents. Therefore Gartner breaks down the difficulty of using AI in IT operations into 12 steps in order to support the IT operation team.
12 steps to excellence in AI for IT operations
I&O leaders must drastically change IT operations duties and processes in order to get the most value out of artificial intelligence for IT operations (AIOps) platforms by following the 12 steps. Gartner grouped these 12 steps into four stages of progress :
1)Establishment Phase
This phase is further divided into three steps :
a) Select a small number of critical business applications.
b) Assess existing skill sets
c) Inventory existing data sources
2)Reactive Phase
This phase is further divided into three steps :
a) Build a semi-structured historical database
c) Implement visualization and natural language access to data
b) Deploy statistical analysis 
3)Proactive Phase
This phase is further divided into three steps :
a) Implement streaming data ingestion
b) Use predictive analytics to anticipate incidents
c) Engage in root cause analysis of complex problems
4)Expansion Phase
This phase is further divided into three steps :
a) Expand functionality to 20 or so business applications
b) Share data and analysis with IT processes outside of IT operations
c) Share data and analysis with business processes
Now, let's look more closely at each stage as follows: 
1)Establishment Phase
Enterprises frequently start their AIOps efforts with a project centered on ingesting vast amounts of data from an IT environment with the hope of quickly collecting an end-to-end perspective on system behavior. Unfortunately, understanding which datasets are required to support an end-to-end view is a skill that requires practice and effort to master. Most IT operations teams still need to gain the abilities necessary to comprehend and work with huge datasets. Therefore, the IT operation team tasked with implementing AIOps should follow the three steps that make up the Establishment Phase:
a) By choosing one to three operational business applications as test cases, choose a starting point for achieving AIOps excellence.
b) Examine the educational background and professional experiences of operations professionals to determine their current skill levels, paying particular attention to any indications of knowledge of or aptitude for statistical analysis and complicated pattern identification.
c) List all the tooling that interacts with the selected applications during operations and development processes and any relevant log files, APIs, and other current data sources that can provide information about them.
2)Reactive Phase
AIOps platforms deal with distinct data types: streaming data, which is presented to the observer in real-time, and stored, historical data. Gartner has noted that starting with historical data rather than streaming data is easier when learning how to manage huge, variable data volumes because dealing with the former is very similar to working with a traditional relational database. The majority of the data gathered during this stage will be retrospective. Therefore, the IT operation team tasked with implementing AIOps should follow the three steps that make up the Reactive Phase :
a) By implementing either a general key-object database management platform (like Cassandra) or a log management platform adapted to store metrics and alphanumeric text strings, you can create a semi-structured AIOps historical database.
b) Give everyone access to the data in the AIOps historical database by combining its native access techniques with powerful natural language and visualization tools.
c) Using statistical analysis or technical computer tools with data taken from the historical database provides the ability to cluster, aggregate, and display trends in the historical data.
3)Proactive Phase
An IT operations team should now be able to move on to both streaming data and implementing true AI capability, having mastered historical data and human-driven analytics. It is important to even though many vendors highlight how their products may predict future events, for many businesses, the automation of anomaly detection and root cause analysis provides even more value. The lowering of MTTR for issues that might impede the smooth operation of digital business operations is most closely correlated with these two competencies. Therefore, the IT operation team tasked with implementing AIOps should follow the three steps that make up the Proactive Phase:
a) Wherever possible, ensure access, visualization, and analysis can be applied to both historical and streaming data simultaneously.
b) Apply correlation-oriented machine learning software to historical and streaming data sources to forecast application performance incidents and other events and behaviors.
c) Apply causal path extraction systems to the patterns previously found with the aid of access, visualization, analysis, and automated correlation determination software to engage in root cause analysis of application performance issues or behavioral anomalies.
4)Expansion Phase
AIOps capabilities provide a crucial bridge between its operations and other components like development and security. As business processes become more digital, business events collide with IT systems more frequently, leaving traces of the same data flowing into and being stored in AIOps platforms. As a result, IT operations teams will be considerably better able to serve and even predict digital business needs by better understanding the patterns that govern AIOps data. Exploiting that understanding assumes that the processes necessary for interaction and communication across IT operations, the rest of IT, and the digital business are in place. Therefore, the IT operation team tasked with implementing AIOps should follow the three phases that make up the Expansion Phase:
a) By feeding data into the AIOps platform from roughly 20 critical business applications now in production, the AIOps platform's coverage will be expanded.
b) By implementing two-way interfaces between the platform and its related processes, including development, security, configuration, change, problem, and incident management technologies and processes, you may transform AIOps capability into a cross-IT service.
c) By implementing two-way interfaces between the platform and its related processes and the digital business process analytics technologies and processes, AIOps capability will be transformed into a cross-digital business service.
Conclusion
The 12 steps will aid an IT organization in approaching an integrated end-to-end view of their shifting IT environment and provide insightful information about expected upcoming occurrences and incidents. To make the most of artificial intelligence for IT operations (AIOps) platforms, the IT operation team must implement these measures, which require significant changes to IT operations activities and procedures. To learn more about Algomox AIOps, please visit our Algomox AIOps Platform page.