The complexity of IT systems has increased significantly in recent years, creating a greater urgency for IT teams to stay on top of the health of operations. An increase in devices connecting to individual applications, the rise of cloud computing and the development of new products have led companies to invest in digital services to meet customer needs.
For example, 99% of organizations surveyed by McKinsey said they have pursued a large-scale technology transformation since 2020. And yet, CIOs say their executives believe 59% of digital initiatives take too long to complete and 52% take too long to realize value, according to a 2023 Gartner survey.
The rise in complexity has created a need for a systematic approach to ensuring the health and optimization of any organization’s IT services. This has led to an increase in the importance of IT operations analytics (ITOA), the data-driven process by which organizations collect, store and analyze data produced by their IT services.
ITOA turns operational data into real-time insights. It is often a part of AIOps, which uses artificial intelligence (AI) and machine learning to improve the overall DevOps of an organization so the organization can provide better service. The use of automation and machine learning capabilities expedites operational workflows, creating insights immediately and removing potential human error from the equation.
ITOA helps ITOps streamline their decision-making process by using technology to analyze large data sets and identify the right IT strategy.
The increasing complexity of IT systems has created a need for organizations to monitor and analyze data better to make more informed decisions. Each organization has a unique tech stack, which is typically made up of native software and cloud platforms. The IT infrastructure of modern organizations is comprised of a large, interdependent ecosystem where an issue with one incident or error could jeopardize the entire system.
An organization’s tech stack of software, infrastructure and network services enable businesses to provide more services to their customers, yet the increased complexity means more things can go wrong, and those errors can have an exponential impact. Organizations strive to minimize downtime as it interrupts their services and jeopardizes their reputation with customers and partners. IT departments need to know how to allocate their resources best to address any emerging issues, increase uptime and keep the organization’s IT operations management (ITOM) running smoothly.
Thankfully, IT systems produce their own data and collect even more in aggregate from customers, partners and employees. Organizations can use all this data to understand the overall health of their system through IT operations analytics.
IT operations analytics (ITOA) vs. observability
ITOA and observability share a common goal of using IT operations data to track and analyze how a system is performing to improve operational efficiency and effectiveness. They both aid business intelligence by enabling organizations to resolve IT operations issues more quickly, inform triage strategies for future issues and assist in the deployment of new technologies.
Observability is concerned with understanding the internal state or condition of a complex system based only on knowledge of its external outputs. It tracks four important pillars: metrics, events, logs and traces (MELT) to understand the behavior, performance, and other aspects of cloud infrastructure and apps. It aims to understand what’s happening within a system by studying external data. ITOA uses data mining and big data principles to analyze noisy data sets within the system and creates a framework that uses those meaningful insights to make the entire system run smoother. It is concerned with root cause analysis of incidents in IT operations, so IT teams can fix problems that could occur again. The goal is to address the underlying issue while determining if other software or systems are at risk of failure, as well.
IT operations analytics technologies
IT operations analytics (ITOA) contains several key tools, processes and technologies, all of which work together to produce value within the organization. Here are some of the most common technologies and use cases:
- Application performance management (APM): Application performance management is a significant component of ITOA that McKinsey estimates to be a $11.8 billion business. It involves using telemetry data and monitoring tools to track software application performance metrics, identifying resource allocation and program usage and helping to solve bottlenecks and detect anomalies. Examples of APM include identifying slow-loading web pages, transaction processing times and latency issues.
- Incident management: Organizations must identify incidents and have a streamlined approach to addressing them. Incident management enables DevOps teams to address unplanned events like server crashes or other service quality issues as quickly as possible.
- Workflow automation: Workflow automation involves the coordination of tasks performed by humans and tasks that are automated, such as email notifications and automating data entry and archiving.
- Predictive analytics: A predictive analytics solution uses historical and real-time data to predict if software and IT services may encounter future issues, providing organizations with the ability to make improvements or fix bugs before they occur. Predictive analytics helps to optimize IT operations by intervening before an incident happens. Predictive analytics can help identify server issues or traffic surges, helping the organization prepare a defense or proactively fix the issue.
- Event correlation and alerting: This analyzes application or host log data to detect patterns, better understand how one application or system affects the other, and alert DevOps engineers about potential issues that could affect multiple systems. Event correlation is especially valuable to detect whether issues like unusual traffic patterns or multiple failed logins are part of a larger security concern.
- Cloud monitoring and maintenance: Organizations need to know the dependability of their data centers, whether they use the public cloud, multicloud environments or on-premises approaches. If the cloud goes down, organizations need to understand how that impacts their ability to provide services.
Stages of IT operations analytics
IT operations analytics (ITOA) helps organizations parse large amounts of structured and unstructured operational data across systems through three key stages:
- Search: IT operations systems capture and store big data generated by business operations, customer interactions and log files that an organization can use to understand and manage the overall health of its system better. ITOA involves searching through the data to assess the current status, identify any existing or potential future problems, and alert the IT operations team about any issues.
- Visualize: This aids the organization’s business decisions by providing a single-pane-of-glass view of how a system is operating. IT operations analytics consumes big data and turns it into usable graphs, charts and spreadsheets. Visualization can occur through interactive dashboards or other administration panels. It helps organizations understand where they need to invest, such as licensing, security applications or purchasing new equipment or software.
- Analyze: The organization can use the visualized data analytics to identify system performance and detection any unusual activity in IT environments and recommend actions to solve those problems.
IT operations analytics KPIs
Organizations can judge successful IT operations analytics (ITOA) programs by several key performance indicators (KPIs):
- Mean time to repair (MTTR): IT operations analytics can help IT teams repair issues that the discipline discovers, thereby improving MTTR. Organizations with a seamless ITOA and incident management program can resolve issues quickly.
- False positive rates: ITOA, which increasingly relies on automation, can sometimes produce false positives, which can lead to unnecessary triage and fatigue site reliability engineers and other IT employees. An increasing number of false positives potentially demonstrates that the ITOA process or IT operations are not working as intended.
- Service availability: This is the percentage of service uptime (i.e., the amount of time that services are running as expected and are accessible to end users). It is crucial that organizations track service availability to ensure they are meeting customer expectations and are in good standing related to their service level agreements (SLAs).
- Capacity utilization: ITOA can also help organizations know if their IT systems are running at capacity or are underutilized. Knowing the latter is increasingly important for organizations using the cloud to baseline their usage to eliminate unnecessary costs.
Key IT operations analytics benefits
There are several benefits for any organization that has a strong IT operations analytics (ITOA) practice:
- Cost savings: Organizations that use ITOA experience several cost benefits, including operational efficiency, reduced downtime and outages, and minimized costly data breaches and other external threats.
- Enhanced customer experience: Customers have high expectations that the services and products they purchase work when they want them. Organizations that plan to deliver excellent customer service depend on ITOA to avoid unnecessary disruptions so customers can access those organizations’ products and solutions on demand.
- Enhanced security and compliance: ITOA plays a crucial role in detecting potential security issues caused by vulnerable endpoints and end devices. ITOA also can detect compliance concerns, such as non-compliant system configurations and non-working audit logs.
- Data-driven decision-making: ITOA is often part of a larger organizational focus on data and analytics tools. ITOA helps organizations make smarter IT investments, better allocate resources and prepare for any future challenges.
Embrace IT automation
IBM’s IT automation tools— including IBM Cloud Pak for AIOps, IBM Turbonomic and IBM Instana—help keep all your systems up and running by giving you the observability and resource management capabilities to predict, detect and remediate incidents faster and cheaper. They can also help automate for innovation and management within and across IT teams.