Modern Information Technology (IT) firms use a popular saying: “If you can’t measure it, you can’t manage it”. This saying indicates that companies must continually measure their operational performance in order to identify and remedy potential issues. This can apply to a wide range of business processes and infrastructures, including for example application development and deployment infrastructures. By measuring the right operational metrics and sources of data, modern enterprises can derive insights about their infrastructures and their services. The above listed metrics include for example metrics of computational resources, networking infrastructure metrics, as well as business operations metrics. When properly analyzed, these metrics can enable the so-called observability intelligence.
Nowadays, observability intelligence can greatly benefit from technological advances in data analytics, machine learning and business intelligence (BI). These technologies facilitate the collection and analysis of structured and unstructured data at scale. Moreover, they also help bringing value from these data beyond simple data collection and analytics. For instance, they can suggest how companies can optimize the use of their development infrastructure and of their business operations.
The Notion of Observability Intelligence
Observability is directly linked to enterprise resilience, as well as to the availability of enterprise infrastructures. In a time where the demand for applications that feature fault tolerance, resiliency and rapid availability is increasing, it is imperative for enterprises to practice observability. Observability provides information about the development and functioning of systems and processes execution. When it comes to software development and deployment, observability can be considered as a key component of DevOps (Development and Operations). It allows organizations to gain insight into their applications, systems, and servers. In some cases, it also provides insights into business users, customers, and processes. Through the extraction of such insights, observability enables businesses to make smart decisions about how to improve their services, products, and internal operations.
In practice, observability systems collect data and insights from various tools to improve the overall performance and reliability of some software system. Specifically, it collects, analyzes, and interprets data from various sources within the systems (e.g., logs, metrics, traces), towards gaining a comprehensive understanding of the systems’ behavior. One of the main goals of applied observability is to help teams troubleshoot problems and make data-driven decisions that improve the overall health and performance of their business and software systems.
Observability Intelligence Technologies
Observability intelligence is the ability to derive insights from the large amount of data. Big data helps enterprises in explaining and eventually predicting phenomena associated with their products and services. In this direction, analytics tools provide a single, high-level view of all data that helps enterprises to identify any deviations from normal patterns. There are various types of analytics tools available (e.g., batch analytics, real-time analytics), which are designed to provide data observability at different processing speeds. Batch analytics process the data up to hours or days, whereas real-time analytics process the data instantly. Beyond these analytics tools, observability intelligence is empowered by various technologies, including:
- Log Management: These technologies collect, store, and analyze log data from various sources to identify issues and track events.
- Metrics Monitoring: Metrics related technologies are focused on the collection and analysis real-time metrics data towards tracking performance and resource utilization, while at the same time identifying bottlenecks.
- Distributed Tracing: These technologies are used to trace the flow of requests across multiple microservices of a company’s cloud and software infrastructure, in order to understand the performance and dependencies between software components.
- Event Correlation: This family of tools is focused on correlating events from multiple sources to identify patterns, trends, and anomalies.
- Artificial Intelligence (AI): Sophisticated observability intelligence systems use machine learning algorithms to analyze data, automate issue detection and resolution, and to identify potential problems before they occur.
- Visualization Technologies: Every non-trivial observability intelligence solution provides some visual data representation such as dashboards and charts. Visualization makes it much easier to understand and identify issues.
- Interpretation and explainability: Visualization technologies are usually integrated with models and techniques for interpreting the data-driven observability insights. For instance, AI-based observability systems integrate explainable machine learning models in order to interpret why certain issues are identified and why specific behaviors are classified as abnormal.
- Collaboration and Alerting: In many cases, observability intelligence comes with tools for collaboration, communication, and incident management. Such tools enable teams to respond quickly and effectively to issues and alerts.
Main Challenges and Best Practices for Overcoming them
Leveraging the above-listed technologies, enterprises strive to address and mitigate the following challenging parts of observability intelligence implementations:
- Data Complexity: Effective observability solutions must handle very large volumes of data from multiple sources. In several cases the data must be also analyzed in real-time. Thus, it is important to adopt and effectively leverage Big Data technologies.
- Data Integration and Interoperability: Observability is about collecting and consolidating data from diverse sources, including data with different formats and ingestion rates. In such heterogeneous environments, data integration and data interoperability become challenging. Therefore, implementers must design and implement interoperability solutions such as data normalization based on a common standards-based format.
- Data Privacy and Security: Observability must at times integrative sensitive data (e.g., logs of healthcare systems), which creates security and data protection challenges. Thus, it is important to identify vulnerabilities and to deploy effective cybersecurity measures.
- Scalability: Developing observability systems that scale is very challenging. It asks for systems that can handle increasing amounts of data without any essential degradation in their performance and reliability.
- False Positives and False Negatives: One more challenge relates to the design, development, and deployment of effective machine learning systems. The latter must be capable of detecting real issues while avoiding false alarms. This is not always easy given the lack of properly labelled data for training AI algorithms.
- Interpreting Results: Understanding the results of observability data can be challenging, especially for non-technical stakeholders. This is the reason why effective techniques for explaining models coupled with ergonomic visualizations are required.
- Integration and Automation: Automation is a key prerequisite for applied observability intelligence. In this direction, observable systems and tools must be integrated with legacy systems. Moreover, it is crucial to choose a proper set of tools that complement each other and that can be effectively integrated.
- Cultural Adoption: Encouraging a culture of observability and continuous improvement is usually a challenge for organizations without relevant experience. Therefore, it is extremely important to foster a culture of observability by encouraging stakeholders to adopt observability practices and to use relevant data to drive decision-making.
Overall, when used correctly, observability intelligence can produce business insights that allow enterprises to improve their performance and stay ahead of the competition. It enables a totally new way of seeing things, one that gives companies an unprecedented view into how their applications behave in production. Leveraging this knowledge, companies can ensure high performance and business continuity in ways that set them apart from their competitors.
