세계의 IT 운영 분석 시장 : 유형별, 용도별, 전개 유형별, 최종 용도별, 지역별 - 예측 분석(2022-2028년)
Global IT Operations Analytics Market By type, By application, By deployment type, By end use, & By region-Forecast Analysis 2022-2028
세계의 IT 운영 분석 시장 규모는 2021년 51억 달러에서 2028년에는 148억 달러에 이를 것으로 예상되며, 2022년부터 2028년까지 예측 기간 중 연평균 복합 성장률(CAGR)은 16.5%를 나타낼 것으로 예측됩니다.
세계의 IT 운영 분석(IT Operations Analytics) 시장에 대해 조사분석했으며, 시장 개요, 시장 규모, 시장 역학, 기업 개요 등의 정보를 제공합니다.
Global IT Operations Analytics Market Size was valued at USD 5.1 Billion in 2021, and it is expected to reach a value of USD 14.8 Billion by 2028, at a CAGR of 16.5% over the forecast period (2022 - 2028).
The primary reasons propelling the market's expansion are the rising reliance on software for operational needs, the consumption of big data, and the digitalization of industries supported by cloud and mobility. Analytics tools in operations are becoming more accessible for general-purpose IT and analysts due to an increase in data sources, data quantities, and use cases as big data and analytics technologies mature. Additionally, the long-term market growth is projected to be aided by the IoT-led data explosion and the application of AI and ML for operational analytics.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global IT Operations Analytics market and various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined by using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Global IT Operations Analytics Market is segmented based on Type, Application, Deployment, and End Use, and Region. Based on component, market is segmented into solution and servicesSolution has further been segmented into log management, application performance management (APM), network and security management, root cause analysis, anomaly detection and others. Others have further been sub-segmented into cost management and capacity management and business process monitoring. Services have further been segmented into professional services and managed services. Professional services have further been sub-segmented into consulting, system integration and implementation and support and maintenance. Based on Type the market is further sub segmented into Visual, Predictive, Behavior, Root Cause. Based on Application the market is further sub segmented into Asset Performance Management, Network Management, Security Management, Log Management, Based on Deployment the market is further sub segmented into On-premise, Cloud. Based on End Use the market is further sub segmented into BFSI, Healthcare, Retail, Manufacturing, Government, IT & Telecom, Others. Based on Region the market is aggregated into North America, Europe, Asia Pacific, Latin America, Middle East & Africa.
In order to help users make decisions and recognize potential hazards, IT Operations Analytics (ITOA) is used to monitor systems and collect, process, analyze, and infer data from various sources of IT operations. Due to the digitalization initiatives made by incumbents across several business sectors, operational analytics has elevated to the status of a strategic priority for organizations. According to a recent industry report, approximately 600 executives from the US, China, Germany, France, and the UK claim that about 70% of the firms are focusing their analytics activities more on operations than consumer-focused procedures. The respondents also concurred that employing operation analytics solutions helps businesses boost earnings and obtain a competitive edge.
In order to boost the flexibility and dynamic nature of IT infrastructure, organizations are integrating modern IT architecture using virtualization technologies like the cloud, containers, and virtual machines. Most firms are migrating their apps to the cloud as a result of the increasing use of containers, which has increased the popularity of cloud-based solutions. Due to the adaptability of cloud-based and analytics technology, businesses can now adjust to shifting resource needs.
Organizations have become less visible as a result of workflows and workloads being isolated from the firms' current physical IT infrastructure. It becomes challenging to identify possible issues as a result. The integration of multiple systems with ITOA is one of the most crucial steps for helping various IT teams deploy solutions in accordance with their needs. As the application of AI and ML technologies increases, this integration is anticipated to advance. In order to attain and maintain performance and availability, ITOA can be used to control the workflows and workloads in complex IT environments.
An example of the rising popularity and demand for ITOA solutions can be found in the use of ITOA by a well-known Asian steel manufacturer to improve the competitiveness and effectiveness of 30-year-old business procedures. It identified the most important quality problems and investigated their underlying causes using data relevant to process innovation. The organization was able to spot issues early on and re-engineer procedures as necessary by closely monitoring the process data.
It was able to cut the lead time for producing hot coils by 50% and reduce inventory by about 60% as a result. Similar to British Petroleum, the petroleum industry was able to save around USD 200 million in CapEX through reduced non-productive asset time thanks to operations analytics in asset maintenance.