Today, we are facing an increasingly complex world of application- and B2B integration. To deliver on business requirements for digitization and automation, we continuously adopt new applications. These new applications need to be integrated with existing applications and databases to allow for seamless and effortless communication to deliver the services required by the business.
The Internet of Things (IoT) is presenting new challenges to integration. According to the Gartner Group, 4.9 billion connected Things will be in use in 2015, and by 2020 there will be 25 billion connected Things. These Things will need to be integrated into new and existing organizational infrastructure to be useful.
With the increase in real time connected applications we face increasing vulnerability and it becomes absolutely necessary to monitor these applications to ensure they are functioning properly, and thus avoiding any downtime in your system. With such an increasing complexity in applications it becomes virtually impossible to carry out monitoring of these applications manually.
A Forrester study found that 56 percent of IT organizations resolve less than 75 percent of application performance problems in 24 hours, and in some cases, it could take months to resolve the problems.* That statistics translate into significant downtime costs for organizations and the challenges and costs will continue to balloon as the number of integrated or connected Things and/or applications continue to skyrocket.
Hence, the need for application monitoring solutions is only going to increase. To be able to cope with the scope of monitoring in an always-on, always connected world companies need to use machine learning, AI and big data technologies to identify anomalies in their IT environment that pinpoints problems before problems become critical and end-users are affected. Leveraging traditonal solutions and configuring specific alert rules for defined scenario is simply not scalable and maintainable,
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