How To Do Log Analysis
Due to the large amounts of data from various sources, log analysis can require a complex strategy for maximum efficiency and productivity. The three core components of effective log analysis involve cleansing, structuring, and analyzing the information contained within data sets.
When working with large and varied data sets, it's important that the data stored is usable and accurate. Data can become corrupted if:
the data's storage disk crashes
applications are improperly or abnormally terminated
the system has been infected with a virus
there are issues related to the input/output configuration
Data cleansing is a process that involves the detection and replacement or removal of inaccurate, incomplete, or irrelevant information.
Since log data is collected from a variety of sources, data sets often use different naming conventions for similar informational elements.
The ability to correlate the data from different sources is a crucial aspect of log analysis. Using normalization to assign the same terminology to similar aspects can help reduce confusion and error during analysis.
Once the data is collected, cleaned, and organized, it is ready to be reviewed and evaluated. Depending on processes in place, intended use, and the size of the data sets, there are various methods of analysis. Best practices include:
Pattern recognition: Filtering messages based on a detected pattern can help you recognize data patterns that may facilitate the identification of anomalies.
Classification: Labeling log elements with keyword tags organizes them into different categories that can make it easier to filter and adjust your display of data.
Correlation analysis: Collecting information from a range of sources such as servers, network devices, and operating systems is ineffective without a way to compare and contrast that data when investigating a single system-wide event. Correlation analysis sorts relevant messages from all components that relate to a certain event.
Artificial ignorance: Routine log messages can increase the density of data in a way that makes it more difficult to sift through when trying to identify the root cause of a problem. Artificial ignorance is a machine learning process that learns to ignore routine updates unless they failed to occur, which indicates an anomaly worth investigation.