In today’s dynamic and highly componentized IT landscape, predictive analytics offers the much-needed ability to proactively predict future outages and automate fixes before they bring down the entire infrastructure.
Owing to the rapid digitization of business operations, IT teams need to constantly monitor and analyze large volumes of data, resulting in extended delays in identifying and solving issues. On top of that, a single IT issue can trigger thousands of alerts, logs, and events, and with the ITOps team working in disconnected silos, it becomes extremely difficult to diagnose the root cause and solve issues.
Predictive analytics, powered by big data, artificial intelligence (AI), and machine learning, overcomes these obstacles to improve application performance, network uptime, and IT infrastructure resiliency by predicting and mitigating outages, and reducing maintenance and operations expenditure in the process.
Gartner predicts that the number of large enterprises that use artificial intelligence in IT operations to combine big data and machine learning functionality to enhance or optimize IT operations and automate processes and tasks to grow by 40% by 2023.
Let’s understand how predictive analytics is transforming ITOps.
Predictive Analytics: An Evolution In IT
Today’s IT operations monitoring and management systems leverage predictive analytics for collecting and integrating data, normalizing it, and analyzing it in real-time.
Machine learning algorithms analyze past incident data to predict and resolve potential incidents in the future.
Here are some ways in which predictive analytics is transforming IT operations.
1. Dynamic Thresholding And Anomaly Detection
Consequently, event patterns are analyzed in real-time and compared against expected behavior, and the IT team is alerted when a series of events showcase anomalous activity.
Moreover, fuelled by artificial intelligence, the system also accounts for false alert suppression and seasonality, i.e. alerts are sent only when a critical application displays abnormal behavior during an unexpected time period. For example, a 90% system utilization is normal during peak business hours, but indicates an issue when the same metric is hit on a Sunday morning.
Anomalous group of events are helpful in –
- Alerting the team regarding an unplanned activity, for example a cyber attack
- Making IT operations more agile by improving planning for significant events, for example, Amazon increasing capacity to ensure infrastructure and applications perform well during the ‘Big Billion Sale’.
2. Predictive Maintenance Of Application Health In Real-Time
Available data generated by the application, including configuration data, network logs, application logs, performance logs, and error logs, is compiled. Multivariate machine learning techniques analyze this data, across different dimensions, to learn the application’s normal behavior.
As new data enters the application, the model identifies unusual patterns and sends it to the IT personnel to follow up before a business-critical outage takes place.
3. Predict Network Downtime Using Sequential Pattern Analysis And Time-Series Event Correlation
A survey by an AIOps Exchange states, “45% of businesses are using AIOps for better root cause analysis and to help predict potential problems.”
Thousands of events across applications and infrastructure are correlated in a time series and analyzed by an ML-powered algorithm to identify patterns.
These patterns proactively alert the team regarding any potential outages and network downtime, reduce the quantum of alerts and pinpoint the root cause, and understand the future impact of similar patterns being repeated.
4. Predict And Prevent Common Capacity Issues
Predictive analytics ensures more bandwidth can be added via automation or manual intervention, so no resource becomes non-operational due to capacity issues.
Moreover, organizations may purchase additional capacity and reserve instances in advance, to cope with the additional demand, leading to large cost savings.
5. Ensuring Cybersecurity By Detecting Fraud
Continuous monitoring of IT systems leads to early detection of any potential fraudulent activity.
https://acuvate.com/blog/predictive-analytics-it-operations/
AIOps tools and solutions are rapidly being adopted by enterprises across the world.
Here are 9 KPIs that can help you measure the effectiveness and impact of AIOps solutions in your company
Research Nester published a report titled “AIOps Market: Global Demand Analysis & Opportunity Outlook 2030” which delivers detailed overview of the global AIOps market in terms of market segmentation by components, deployment type, application, vertical, and by region.Further, for the in-depth analysis, the report encompasses the industry growth indicators, restraints, supply and demand risk, along with detailed discussion on current and future market trends that are associated with the growth of the market.The global AIOps market is estimated to grow at a CAGR of ~30% over the forecast period, i.e., 2022–2030.
The market is bifurcated on the basis of deployment type into on-premise, and cloud-based, out of which the cloud-based segment in estimated to gain the largest market share during the forecast period, owing to the increasing penetration of cloud-computing technology amongst the various end-user sectors on a global level.
On the basis of vertical, the market is segmented into telecom & IT, BFSI, healthcare and others.
Among these, the telecom & IT segment is anticipated to hold the largest share throughout the forecast period, on the back of early adoption of advanced technology in the IT sector, along with the increasing share of IT sector in the global GDP.
Moreover, the advantages offered by AIOps over DevOps, such as higher accuracy, and reduction of false positives, are estimated to boost the market growth over the years.
Furthermore, frequent launches of new technologies, and product pipeline is another major factor projected to fuel the market growth.Regionally, the global AIOps market is segmented into five major regions including North America, Europe, Asia Pacific, Latin America and Middle East & Africa region.
The global AIOps market size is expected to reach USD 23.7 billion by 2025.
The report also throws light on various aspects of the global AIOps market including end user industries by assessing value chain analysis.
The report covers several qualitative aspects of the artificial intelligence for IT operations industry in market drivers, market restraints and key industry trends.Request sample copy of this report at: https://www.adroitmarketresearch.com/contacts/request-sample/1344The report on AIOps market, documents a comprehensive study of different aspects of the artificial intelligence for IT operations market.
Every market intelligence report covers certain important parameters that can help analysts define the market situation.
It includes a thorough analysis of market trends, market shares and revenue growth patterns and the volume and value of the market.
It also covers methodical researches.This report on AIOps market is also based on a meticulously structured methodology.