Artificial Intelligence for IT Operations
Artificial Intelligence for IT Operations (AIOps) describes the combination of big data and machine learning (ML) to automate IT operation processes, including event correlation, anomaly detection and root cause analysis.
AIOps stand for Artificial Intelligence for IT Operations, which is using Artificial Intelligence (AI) technology to operate an IT operation. The word “AIOps” was established by Gartner which is the global well-known organization. They discuss about AIOps platform that using Artificial Intelligence & ML technology to manage all the big data generated by IT system and also explain that AIOps is improving IT operation performances.
How AIOps relate with IT Operations
Nowadays, IT Operations cover many different areas such as
The 3 main challenges which can be observed for digitally powered IT operations are:
- Huge amounts of IT infrastructure generated events, metric, traces, network flow data and telemetry data often exceeds manageability. The lack of proper tools to analyze those massive data lakes can lead to overlook the really important information.
- Having too many tools to manage the IT systems including ITIM, ITSM, NPMD, SIEM, APM, DEM raises not only complexity but also increases both, Capex and Opex expenses.
- IT Operations often focus only on the IT department itself and misses out on valuable insights which can affect business growth, competitiveness and users or client digital experience.
IT Operation Management can make the difference between success and failure for your Digital Transformation journey. In order to maximize the efficiency of existing IT Operation Management, it is highly recommended to utilize an Artificial Intelligence or AIOps Platform to create the best possible user experience and business results.
Artificial Intelligence or AIOps
enhance existing monitoring systems, service management and task automation. AIOps is covering 3 main areas which are:
Huge amounts of IT infrastructure generated events, metric, traces, network flow data and telemetry data often exceeds
The AIOps receives incidents, dependencies and changes related data to introduce task automation, change risk analysis, SD agent performance analysis and knowledge management.
The AIOps platform analyzes and run playbook processes: Self-Diagnostic for analyzing, Self-Healing” for issue solving, “Self-Recovery” for recovering and “Self-Prevention” for preventing future problems in an automatic manner.
The benefits of Artificial Intelligence
The benefits of Artificial Intelligence in IT Operation or AIOps is that it enables your IT operations to identify, target, and resolve slowness and outages faster. Here are some several benefits:
The AIOps Platform can also help manage IT services or IT Service Management (ITSM) tasks, including:
- Assisting staff responsible for dispensing various incident services
- Automating tasks such as software installation, password resets, or email validation to open a service request.
- Helping to analyze past incident data to support and increase service provider productivity.
- Helping to find insights at strategic levels within change management, including predicting whether a Change Request will be successful, finding conflicts in changes, or determining the best time to implement system patches.
- Helping to predict which incidents are unlikely to be resolved within SLA timeframes.
- Using natural language processing (NLP) to aid the functionality of chatbots and virtual support assistants in order to reduce the basic work required by employees.
The main AIOps Platform functionality we discussed are:
- Data Ingestion is bringing in information from many sources entry to the system
- Data Analytics is the analysis of data by using machine learning technology
- Prescription is the things what to do from the analysis of the data in item 2.
- Action is an automate operation.
How can I&O Leader improve IT operations efficiency and shorten the time of impact due to this variety of monitoring tools
According to Gartner (Gartner, Inc.)’s customer surveys, having multiple monitoring tools in the organization may lead to the delay in responsiveness and longer solving time. To use AIOps may be the answer as the following:
Improve collaboration by using the AIOps platform to collect data from monitoring tools
Such as telemetry data, logs, and virtual them on the central dashboard, which corresponds to what the operation team wants to monitor.
- To create centralized visibility for events from various IT systems, which impact to the business.
- To find and correlate information from various systems reduce vague and duplication of information
- To improve teams’ collaboration by using a central data set for making decisions and problems solving
Deliver the insights gained from analysis to stakeholders by integrating raw data from
Various tools and confident that the outcome data or results are more valuable by using various techniques as follows:
- To manage active events data using Event Correlation Analysis (ECA), the challenge of this technique is that we expect manually adjust the rule periodically.
- To handle of active data and archived events data by using pattern recognition and machine learning to optimize event correlation and reduce manually rule updating.
- To Manage events and metrics by holding the data on the same time series axis. This will make it easier for us to find the root cause of the problem.
- To handle of metrics by hold the data on the same time series axis for anomaly.
Netka AIOps Director
Netka AIOps Director or N-AIOps is AIOps Platform which provides data ingestion, data analytics by using AI technologies and intelligently drive automation.
N-AIOps have workflow designer which is tool for creating automation process that can flexibly design workflow with complex conditions. N-AIOps is platform which require data from IT management systems e.g. ITIM, ITSM, NPMD, SIEM, APM, DEM for cross-domain analysis and drive automation. N-AIOps supports data for processing as follow:
- Log data e.g. Syslog, SNMP Trap, Windows event
- Telemetry data e.g. metrics, traces
- Network data e.g. packet analysis data, flow analysis data, topology, inventory
- ITSM data e.g. incidents, changes, problems, Cis
- IoT data or sensor values e.g. temperature, humidity, AC/DC voltage, current, watt, relay, contact, access door’s status
How Netka AIOps works?
The ultimate AIOps solution
N-AIOps can work with 3rd party application which send data with Syslog, SNMP Trap or JSON format and work seamlessly with Netka products including NetkaView Network Manager or NNM, NetkaQuartz Service Desk or NSD, NetkaView Logger or NLG, NetkaView IoT or NIoT