The current business environment consists of applications, systems, servers, networks, devices, and sensors that generate massive data incessantly. The complex machine data generated from disparate sources contain valuable details, such as records of customer transactions, user behavior, system activities, security threats, and fraudulent activities. It is challenging for businesses to ingest, process, and analyze this data using traditional data management methods, which are not suitable for high-volume unstructured and dynamic data.
To derive insights from machine-generated data.
Analyzing critical data about organizational operations to improve planning and performance Data Analytics with Splunk is used to evaluate a business’s overall performance or specific key performance indicators (KPIs) critical to a business unity, process, project, or product.
A business environment is characterized by event floods that are rampant and unmanageable. Interpreting machine data using traditional approaches is complicated and time-consuming. Businesses lack a way to understand which events to prioritize, resulting in troubleshooting delays and lost time and money. At the same time, some enterprises have created custom and messy integrations to ingest and analyze data. However, such solutions lack a single place to monitor and understand big data.
Businesses feed machine data to Splunk that does the data processing and analysis to produce insights.
Businesses can identify and resolve issues up to 70 percent faster using Splunk.
The solution reduces costly escalation by up to 90 percent.
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