The monitoring of data analytics is as important as data analytics itself. Especially in fields like the digital economy, where one small internal glitch can result in a shutdown of services causing massive amounts of loss.
FREMONT, CA: The digital economy focuses on conducting personal and business transactions directly via the internet with the help of applications. These applications make the process of fund transfer fast, cost-effective, and efficient. Activities like personal banking, payments processing, stock purchase, ordering food online, paying utility bills, and calling a cab are carried out through digital wallet applications. The sheer volume of transfers has created an impact that makes it difficult for large technology organizations to manage business applications. With one minor glitch, the entire system could be brought down, resulting in a significant loss in the revenue.
In order to avoid such hazardous instances, it is crucial to enable supervision and monitoring of every process and components of the application stack all the time. With disastrous problems leading to market downs previously, it is clear that the traditional monitoring systems are flawed.
Instead of a holistic view, a fragmented approach has been incorporated to conduct application monitoring. The traditional method uses a host or a SaaS platform based solution to design and monitor specific applications; by doing so, it is neither effective nor efficient. An optimized hosted or SaaS platform creates a centralized solution that monitors the application as a whole by visualizing the systems. A unified monitoring system provides infrastructure and application monitoring by sending alerts when an adjusted metric breaches the threshold.
Often, separate IT teams are hired to monitor KPIs for the application. It should be avoided as the application develops to accommodate new data sets; the monitoring process of KPIs should be developed as well. Even though many monitoring solutions divulge in providing extra plug-ins and customizable features, the IT team constantly find it taxing to monitor the KPIs, which are critical drivers for business decision making processes.
Large enterprise applications generate a massive amount of data, both necessary and irrelevant. The system will be subjected to a clog down with irrelevant monitoring of data, which results in missing of key KPIs.