As organizations adopt multicloud environments, the ability to unify, store, and contextually analyze operational data is paramount. That's why IT operations analytics is key to the cloud journey.
With 99% of organizations using multicloud environments, effectively monitoring cloud operations with AI-driven analytics and automation is critical.
IT operations analytics (ITOA) with artificial intelligence (AI) capabilities supports faster cloud deployment of digital products and services and trusted business insights. Therefore, it is a necessary component of any enterprise’s cloud journey now and in the foreseeable future. In what follows, we cover how ITOA works, why it’s important, tools to consider, and more.
What is IT operations analytics?
IT operations analytics is the process of unifying, storing, and contextually analyzing operational data to understand the health of applications, infrastructure, and environments and streamline everyday operations. This operational data could be gathered from live running infrastructures using software agents, hypervisors, or network logs, for example.
ITOA collects operational data to identify patterns and anomalies for faster incident management and near-real-time insights. This enables AIOps teams to better predict performance and security issues and improve overall IT operations.
These operational insights are used to achieve the following:
- increase the life span of resources;
- automate root-cause analysis at scale;
- identify and assess network security risks;
- improve mean time to detection and mean time to recovery in the cloud;
- inform better decision-making;
- expedite IT service ticket resolution; and
- establish strategies for improved maintenance.
In addition to improved IT operational efficiency at a lower cost, ITOA also enhances digital experience monitoring for increased customer engagement and satisfaction.
How does IT operations analytics work?
ITOA automates repetitive cloud operations tasks and streamlines the flow of analytics into decision-making processes. Additionally, ITOA gathers and processes information from applications, services, networks, operating systems, and cloud infrastructure hardware logs in real time. Then, big data analytics technologies, such as Hadoop, NoSQL, Spark, or Grail, the Dynatrace data lakehouse technology, interpret this information.
Here are the six steps of a typical ITOA process:
- Define the data infrastructure strategy. Choose a repository to collect data and define where to store data.
- Clean data and optimize quality. Fix or remove duplicate, incorrect, corrupted, or incomplete data within a data set.
- Define core metrics. Identify critical key performance indicators (KPIs) specific to the business application.
- Automate analytics tools and processes. Apply automated analytics tools and processes to extract real-time insights for defined KPIs.
- Establish data governance. Identify data use cases and develop a scalable delivery model with documentation.
- Provide data literacy for stakeholders. Sync analytics insights from a data warehouse or data lake into front-end tools for easy-to-use visual dashboards.
Why is ITOA important?
Operational analytics digests and breaks down an unwieldy amount of big data to provide important insights into internal business functions and processes. Without these insights, it is difficult to enact data-driven, proactive decisions such as enhanced security protocols that identify and prevent software supply chain attacks.
Operations analytics ensures IT systems perform as expected. ITOA can evaluate operating system functions and reduce costs with optimized IT resource management. Additionally, ITOA enables organizations to eradicate traditional data silos that were common with earlier monitoring tools and techniques.
ITOA tools to consider for your organization
The following tools and technologies are used to support big data analytics technologies:
- Apache Hadoop. This open source framework stores and processes large sets of structured and unstructured data. Cloud-as-a-service platforms, such as Amazon Web Services, Google, and Microsoft, have made it easier to set up and manage Hadoop clusters in the cloud.
- NoSQL database. Nontabular data management, as opposed to tabular relations used in relational databases, is useful when working with large sets of distributed data.
- Apache Spark. Organizations use this open source, distributed analytics engine for big data workloads.
- Dynatrace Grail. This data lakehouse technology with AI-powered automation unifies data to improve observability, security, and business workflows in multicloud and cloud-native environments. Grail retains the context of this operations data at a massive scale for instant, precise AI-driven insights.
How a data lakehouse approach unifies data and analytics in context
A data lake is a repository of structured and unstructured data used to power analytics across all departments within an organization. Because data is stored in its native format, it is loaded and accessed faster than data in a traditional relational database, such as a data warehouse.
A contextual data lakehouse, on the other hand, combines the ability of a data lake to store large volumes of varied data at a lower cost with the management features and tools of a data warehouse. The data lakehouse stores data from multiple sources in a common format maintaining entity relationships, thus making it easier for a query engine to sift through and generate results quickly.
The single, unified data lakehouse architecture provides fast access to a curated data set for advanced AI analytics capabilities for trusted business intelligence and reporting.
Why use a data lakehouse for causal AI?
A data lakehouse approach is ideal for unifying big data with analytics to improve IT operational performance and efficiency. New data sources are dynamically incorporated into the platform as they become available, mirroring the changing needs of an organization. This gives AIOps teams deep visibility and near-real-time insights into distributed cloud, networks, and system performance.
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