What is log analysis?

Log analysis helps IT professionals better understand how their systems are functioning, improve system performance and enhance security.

Log files, also known as ‘log data,’ are records of system activity generated by various compute resources, such as devices, applications and software programs. Log files are indispensable to IT operations, providing valuable insight into system performance, optimization and potential security breaches. However, the rise of data-rich technologies like generative AI (gen AI) has exponentially increased the amount of data enterprises need to analyze. According to a recent report, data logs requiring analysis at the enterprise level have grown as much as 250% year-over-year in the last 5 years.¹

As gen AI and other data-intensive technologies continue to expand and flourish, IT leaders are seeking a deeper understanding of how they can use log analysis to keep the systems their organizations rely on performing at peak levels.

There are three kinds of log files that IT professionals focus on when conducting log analysis: access logs, error logs and event logs.

• Access logs: Access logs are logs that record common application server requests (e.g., IP addresses with timestamps) and a user’s requested destination (e.g., a web address). Access logs are important because they help someone monitoring a system track user behavior and identify potential security threats.

• Error logs: Error logs contain data pertaining to a security incident; for example, if a user or application tried to connect to a database and was denied access. Error logs are critical for log management, the processes IT teams rely on to collect, process and store log data. Error logs help teams with troubleshooting efforts when they need restore normal business operations after an interruption. Also, studying error logs after an event can help minimize downtime in the future and improve the user experience for customers.

• Event logs: Event logs help give IT teams a better understanding of what was going on inside a system during a period of time. They record everything that happened on the system, such as when it was booted up or shut down, when a particular user logged on or logged off, and when any changes were made to its configuration. After a security breach, IT teams often study event logs closely to trace unauthorized access attempts and try to better understand the nature of a cyberattack.

To conduct effective log analysis, network administrators, DevOps engineers and other IT professionals typically follow four steps:

• Data collection

• Data processing

• Data analysis

• Data visualization

Log analysis begins with engineers collecting data from the various sources that are relevant to the systems they need to analyze. Usually, these sources of data include a blend of hardware and software systems like network devices, servers, applications and software programs.

Data collection is critical to the overall success of log analysis. If it isn’t done thoroughly, it can result in missing log sources, applications or programs that are not submitting data, resulting in an incomplete picture of how a system is functioning.

During data processing, engineers focus on indexing and normalizing logs, a process known as parsing. Parsing involves categorizing data by timestamp, source, event type and other characteristics to make it easier to understand.

Data processing is critical in turning raw logs composed of unstructured data into organized, actionable data logs that are easier for engineers to extract insights from.

Once data has been processed, it’s ready for analysis, arguably the most important (and time-consuming) step in the process. During data analysis, engineers pore over the actionable data they’ve extracted from logs during data processing, looking for clues as to why a particular system or application isn’t functioning.

Today, data analysis is almost always aided by artificial intelligence (AI) and machine learning (ML) tools that help speed time-to-value and improve the accuracy of log analysis with their advanced pattern recognition capabilities.

Log data is only as valuable as the insights it can generate about the systems’ overall health. Data visualization, the displaying of data and insights via a comprehensive dashboard, helps transform raw information into vivid pictures of real-time system health.

Aided by AI and ML tools, today’s dashboards help IT teams identify performance issues by visualizing key metrics like central processing unit (CPU) usage, network latency and more.

IT teams typically rely on five different kinds of log analysis to detect problems across a wide range of systems:

• Pattern recognition: In pattern recognition, also known as log analytics, analysts try to identify specific patterns or trends in log data that could be evidence of a problem. Pattern recognition algorithms, advanced algorithms capable of spotting patterns in large datasets, are widely used in pattern recognition, helping data scientists identify repeated failures or unusual activity that could be evidence of a broader problem.

• Anomaly detection: Anomaly detection involves the identification of information that deviates from what is usual, standard or expected, making it inconsistent with the rest of the data in a dataset. While pattern recognition concentrates on identifying recurring patterns in data, anomaly detection seeks to spot deviations in those normal patterns. ML algorithms are commonly used in anomaly detection, helping systems engineers spot unusual spikes in site traffic, user behavior or other abnormalities that can be evidence of a broader problem.

• Root cause analysis: Unlike pattern and anomaly detection, root cause analysis is a kind of log analysis that tries to identify the cause or underlying conditions that led to a problem. In root cause analysis, data scientists and engineers trace the sequence of events that led to a system failure or unexpected downtime. Root cause analysis is time-consuming and intense, often dealing with the close examination of large volumes of data.

• Semantic analysis: Semantic analysis involves examining and interpreting log data, looking at the patterns, anomalies, and even root causes, and then trying to understand the broader picture of a system’s overall condition. Natural language processing (NLP), a branch of AI that tries to teach computers to understand language like the human brain, is often used in semantic analysis, helping scientists understand why a system or application has failed.

• Performance analysis: In performance analysis, engineers and data scientists seek to optimize a system or application by looking specifically at log data associated with performance. Performance analysis can help resolve a wide range of performance issues, such as slow response times, CPU usage, and operating system (OS) boot times, by identifying bottlenecks that are preventing systems from running at peak efficiency.

Modern enterprises need to constantly be on the lookout for ways to make their systems and applications function more efficiently, and log analysis plays a crucial role in this ongoing effort. Here’s a look at some of the most popular benefits of log analysis.

Modern DevOps teams rely on log analysis software for observability, which helps improve their awareness of how systems and applications are functioning. Through metrics like usage, web traffic, logins and more, log analysis shows DevOps teams where their code is strong and where it could be improved. It also helps identify opportunities for new features and capabilities. Modern DevOps platforms are often equipped with log analysis tools that aggregate data from various sources and deploy AI and ML to spot patterns that will help them identify issues.

Log analysis plays a crucial role when it comes to cybersecurity and protecting systems, applications and people from cyberthreats. It increases the visibility cybersecurity teams have over the systems and applications they’re responsible for, providing detailed records of logins and user-behavior that may contain evidence of an attack. Advanced cybersecurity log analysis tools can even automate the detection of suspicious activity, alerting IT managers when a certain kind of behavior is taking place.   

Visibility doesn’t just help IT operations teams prevent cyberattacks, it can also help with the day-to-day operations that ensure an organization’s IT systems and applications perform the way they were designed to. IT operations (ITOps) teams rely on effective log analysis tools to access and observe large amounts of data and identify performance issues. Log analysis helps centralize a team’s strategic approach, gaining a full picture of how systems and applications are functioning across an entire enterprise.