The Global Synthetic Monitoring Market accounted for USD 962.7 million in 2016 growing at a CAGR of 19.3% during the forecast period of 2017 to 2024.
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This report consists of below pages:
- No of pages: 350
- No of Figures: 60
- No of Tables: 220
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Synthetic monitoring is a process of monitoring the applications by using robotic agents to simulate the real user’s interaction. It also includes testing of applications before launching it to the end-users.
The synthetic monitoring tools have HTTP, automatic cookie handling, cross platform support, load generation, response verification, an execution and monitoring console, real-time stats, GUI or console modes, custom timers, and others. It is widely applicable in banking, financial services, and insurance, government and defense, IT and telecom, healthcare, retail, automotive, transportation, and logistics, media and entertainment, and others.
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Critical need for pro-active monitoring, and management of increasing complex applications may act as the major driver in the growth of synthetic monitoring market. On the other hand lack of expertise and skill set may hinder the market.
The report for synthetic monitoring market include detailed vendor level analysis for market shares in 2016 for Global, North America, Europe, Asia Pacific, Middle East and Africa and South America specifically. Also impact and development analysis of key vendors is registered in the market and factored on the basis of Vendor Positioning Grid Analysis which measures the vendors strengths and opportunities against present market challenges, measure providers ability to identify or satisfy present market needs, map providers market vision to current and upcoming market dynamics among others.
The report also measures technology life line curve and market time line to analyze and do more affective investments.
Some of the major players operating in Global Synthetic Monitoring Market are:
- IBM Corporation
- BMC Software, Inc.
- Dell Inc.
- Dynatrace LLC
- Hewlett Packard Enterprise Development LP
- New Relic, Inc.
- Riverbed Technology
- Splunk Inc.
- SCIVISUM LIMITED
- Catchpoint Systems, Inc.
- Salesforce and others.
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Segmentation of Global Synthetic Monitoring Market:
The global synthetic monitoring market is segmented on the basis of component into monitoring type, and services.
On the basis of monitoring type, the global synthetic monitoring market is segmented into API monitoring, SaaS application monitoring, mobile application monitoring, and web application monitoring.
On the basis of service, the global synthetic monitoring market is segmented into managed services, and professional services. The professional services segment is further sub segmented into business consulting services, implementation services, and training and support services
On the basis of organization size, the global synthetic monitoring market is segmented into small and medium enterprises (SMES), and large enterprises.
On the basis of deployment model, the global synthetic monitoring market is segmented into hosted, and on-premises.
On the basis of vertical, the global synthetic monitoring market is segmented into banking, financial services, and insurance, government and defense, it and telecom, healthcare, retail, automotive, transportation, and logistics, media and entertainment, and others.
On the basis of geography, the global synthetic monitoring market report covers data points for 28 countries across multiple geographies such as North America, South America, Europe, Asia-Pacific, and Middle East & Africa. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa, and Brazil among others.
In 2017, North America is expected to dominate the market.
Major Market Drivers and Restraints:
- Micro services driving the development of applications
- Critical need for pro-active monitoring
- Management of increasing complex applications
- Importance of DevOps
- SLA parameters evaluation latency issues
- Ease of deployment
- Lack of expertise and skill set
- Lack of real time application
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