Information Technology Service Intelligence (ITSI) is a new type of software tool that uses artificial intelligence and machine learning to help IT managers monitor increasingly complex computing environments.
Today's industry-leading ITSI tools are used to monitor and analyze network events from an ITSM perspective where the primary goal is to predict and proactively avoid service degradation. These tools use advanced algorithms to assess trends in network activity that could result in service degradation or interruptions if they remain unaddressed. Armed with this knowledge, IT managers and administrators can take action to prevent service outages before they occur.
- Like other network monitoring and analytics solutions, ITSI tools aggregate and analyze data in the form of event logs from a range of network endpoints and applications.
- The key value-driving insight of ITSI is that organizations whose IT organizations follow an ITSM paradigm can use predictive algorithms to anticipate when service outages are likely to occur and prevent them before they impact customers.
- In the context of ITSI software, machine learning algorithms are used to detect patterns that could lead to service degradation.
- Sumo Logic is the leading cloud-native machine data analytics platform, enabling DevSecOps teams to better manage security and network performance analytics.
Like other network monitoring and analytics solutions, ITSI tools aggregate and analyze data in the form of event logs from a range of network endpoints and applications. While other software tools might use this information to drive business intelligence or to detect security threats, ITSI tools feed this data through machine learning and artificial intelligence algorithms that are optimized to detect potential causes of service outages that could impact revenue or customer experience.
Before we describe how ITSI tools work, it makes sense to identify three of the most important terms that are central to the definition of ITSI: information technology service management (ITSM), artificial intelligence (AI) and machine learning (ML).
Information technology service management (ITSM)
ITSM is the most popular paradigm for managing Information technology at the enterprise level. ITSM consists of all activities that are involved in strategically choosing what services should be deployed, designing those services, creating or building them, deploying them, operating them and supporting them through to the end of the service life cycle.
IT services are any and all services whose delivery depends on the actions of the IT department - anything from configuring technology like computers for users to resetting a password to making sure that an application is available. The ITSM paradigm encourages IT departments to get away from the "break-fix" model of IT support and move towards the proactive management of services that align with the needs of the business.
Artificial intelligence (AI)
AI is the simulation of behaviors that characterize human intelligence such as learning, reasoning or self-correction. Successful applications of AI often require large data sets that the AI processes using special algorithms. AI is used in industries such as finance, security, and healthcare to automate processes, find patterns in large data sets, reveal hidden information and insights and discover solutions to problems that are too complex for human analysis.
Machine learning (ML)
Machine learning is one of the most important applications of AI technology. Through machine learning, a computer program can learn from experience by processing a large amount of data without being explicitly programmed. In the context of ITSI software, machine learning algorithms are used to detect patterns that could lead to service degradation. Without machine learning, these patterns would have to be known a priori by the user and the software would have to be programmed to detect those specific patterns. With machine learning, the process of finding these patterns is effectively automated.
The key value-driving insight of ITSI is that organizations whose IT organizations follow an ITSM paradigm can use predictive algorithms (with machine learning, artificial intelligence, etc.) to anticipate when service outages are likely to occur and prevent them before they manifest in a way that impacts customer satisfaction.
The functioning of ITSI tools can be described as a four-step process:
- Data collection and aggregation - ITSI tools are configured to pull data from a variety of sources across the network. Data sources can include log files, network events, text, wire, metrics, API sources and even data from social media. Bringing data together from a variety of network sources creates the opportunity for IT managers to gain high-level insight into network events.
- Trend tracking and analysis - ITSI tools sift through the massive amount of data using complex machine learning and pattern recognition algorithm. Trends and patterns are identified and tracked for each data source and data can be correlated and analyzed.
- Insight development and predictions - Over time, artificial intelligence algorithms process a significant amount of data that comes from the network. As a result, these algorithms get better at predicting KPI performance for network endpoints on a given metric and identifying the potential causes of service deficiencies before they manifest.
- Action - Armed with insights derived from a massive volume of data, IT management teams and their staff can take action to ensure that agreed service levels are achieved and avoid the negative impacts of a service availability issue.
The DIKW hierarchy is a model for the development of insights through data collection. DIKW stands for Data, Information, Knowledge, and Wisdom.
Data represents the lowest level of the DIKW hierarchy. A data point is a result of doing some measurements. It describes something that happened in the simplest possible terms.
To turn data into information, it must be processed and contextualized. Turning data into information means finding relationships in the data and understanding how factors represented in the data might be interrelated. The purpose of data analysis is to turn data into information, a process that answers who the data was collected by, what the data is, when the data was collected, and where the data was collected.
Knowledge development is all about looking at the information you have and understanding the meaning behind that. For complex systems with a lot of data, artificial intelligence algorithms may be better than humans at picking out the most important information and correlations and turning those into genuine insights into how the system is functioning.
Finally, Wisdom is all about understanding how to use knowledge to drive action.
The DKIW model is reflected and mirrored by the process flow for today's industry-leading ITSI tools. With IT service intelligence, IT managers gain access to powerful automation tools that turn large volumes of collected data into actionable insights. Once a computer has generated useful insight, such as predicting poor KPI performance for critical service, the job of IT managers is to enact a solution that prevents the service from deteriorating and restores the KPI value to an acceptable level.
Splunk ITSI users can import raw data from Splunk into Sumo Logic, leveraging the data collection tools of this IT service intelligence tool to drive Sumo Logic's machine analytics functionality.
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