Leveraging Splunk as a log management tool
Splunk is the market’s leading log management software for good reason. Based upon over 1,000 studies of actual Splunk customers, companies realized these significant improvements after implementing Splunk as their log management tool for IT Operations:
- 15% to 45% reduction in high priority incidents
- 70% to 90% reduction in incident investigation time
- 67% to 82% reduction in business impact (downtime and other negative business impacts)
- 5% to 20% increase in infrastructure capacity utilization
Source: 1,000 documented case studies by Splunk’s Business Value Consulting team
Legacy log monitoring tools typically operate in silos, and as a result these systems can struggle to collect and correlate information across multiple technologies. The result is difficulty within or across IT infrastructure teams to rapidly troubleshoot problems, impacting customers or business operations.
As a central log management tool (CLM), Splunk changes this all-too-familiar scenario. Splunk enables you to examine your data in depth and in real time from across your entire IT environment, through what Splunk refers to as a “single pane of glass”. As the market’s leading centralizing log monitoring software, Splunk allows IT practitioners to eliminate the bridge calls and finger pointing that occur when logs and troubleshooting are siloed. Splunk’s log management software collects and correlates the machine data across your technology stacks.
From a single location, Splunk’s log monitoring software allows correlation and analysis of:
- Servers and Operating Systems
- Network traffic
- Identity Systems
- Application, Database and Mobile
The result? Reduced Mean Time To Resolution (MTTR), lowered monitoring costs, improved system uptime resulting in less negative impacts to customers and business operations, and support of strategic initiatives such as datacenter optimization and tool consolidation.
Splunk’s advanced, premium IT Service Intelligence (ITSI) app takes infrastructure monitoring to a new level by predicting and alerting on events before they happen. This is accomplished through the use of machine learning and Splunk’s Machine Learning Toolkit, which baselines normal operational patterns and use statistical measurements to determine threshold variability patterns.