Complex hybrid cloud environments create many challenges — limited visibility, complicated incidents, longer issue resolution and impacted systems — which collectively put pressure on teams and may even hurt customers.
Use predictive analytics powered by machine learning to prevent issues before they impact customers.
Utilize event correlation to prioritize and group alerts and quickly identify probable root cause.
Get full-stack service visibility and drive efficiency with automated incident response.
Use machine learning algorithms and historical service health scores to predict incidents up to 30 minutes in advance.
No tool I have ever used is as easy and malleable as Splunk. It gives everyday users the insights they need to achieve measurable business results.
Better understand your underlying infrastructure with service-level dashboards. Drill down to code level and identify root causes all in one place.
We can now track our member interaction all the way through every system in the service stack. Splunk ITSI has made troubleshooting effortless and collaborative.
Tap into real-time event correlation, automated incident prioritization and integrations with ITSM tools for more intelligent event management.
Any time our systems are down, we can’t complete customer transactions and could potentially be losing millions of dollars. Splunk’s machine learning capabilities enable us to forecast, predict and specifically avoid those potential transaction failures in real time.
See across your environments and get ahead of incidents with predictive analytics and prescriptive resolution empowered by machine learning.
Use all your data for AIOps
Easily onboard, visualize and act on all your data, whether from Splunk or third-party data sources. Use pre-built content to get started fast and customize as needed.
Short for artificial intelligence for IT operations, AIOps is the practice of applying analytics and machine learning to big data to automate and improve IT operations.
With IT operations management becoming increasingly complex and challenging, AIOps brings together data from multiple sources and simplifies data analysis. AI can automatically analyze massive amounts of network and machine data to find patterns, both to identify the cause of existing problems and to predict and prevent future ones.
Applying AIOps to areas such as big data, anomaly detection, event correlation and analysis, and IT service management, often results in outcomes such as:
These, in turn, improve service delivery and overall customer satisfaction, enable more complete data analysis and insight, increase resource and cost savings and encourage strategic collaboration between the IT organization and business peers.
Organizations today increasingly seek AIOps platforms. According to Gartner, an AIOps platform enables the concurrent use of multiple data sources, data collection methods and analytical and presentation technologies. An AIOps platform needs to be able to both analyze stored data and provide real-time analytics at the point of ingestion.
The central functions of an AIOps platform, as defined by Gartner, are: