What is machine data, and what is it used for?
Business application data, human-generated content, and machine data represent 3 fundamental types of data. Machine data is consistently generated by various software applications and electronic devices.
What is machine data?
Machine data, also known as machine-generated data, is information created without human interaction, stemming from computer processes or application activities. This type of data spans across various industries.
Where does machine data come from?
Machine data is generated automatically on a schedule or in response to events. It presents increasing opportunities for analysis alongside other data types in big data and machine learning analytics. It encompasses diverse sources such as devices (computers, servers, mobiles), applications, websites, financial transactions, sensors, and logs.
Machine data also includes insights derived from data analysis, predictions, and decisions made by software tools. This category extends to automated tasks, where tools issue commands, generate status logs and even trigger alerts based on analyzed machine data.
Notably, metadata, providing details about events, is a significant subset of machine data, seen in elements like timestamps, GPS locations, and camera settings in photos taken on devices.
Types of machine data
Sensors are crucial to monitor and maintain machinery like compressors and pumps and devices like smart home security systems. They continuously gather machine data, such as movements, temperatures, pressures, and rotational speeds to generate insights and implement action plans.
Computer or system log data
Computers produce log files that contain system operation details and are presented as log lines. These lines document actions like file operations, network connections, software installations, application launches, Bluetooth device attachments, and more. Some log data is shared with manufacturers, while others remain locally confidential.
Geotagging involves adding location-based metadata to media, like photos and videos, created by a device. These geotags, including timestamps, automatically capture details such as latitude, longitude, altitude, bearing, and more, forming machine data
Call log data
Machine data linked to telephone calls is known as a call log or call detail record. Call logging is the automated collection, recording, and analysis of phone call data, including call duration, start and end times, caller and recipient locations, and network used.
Web log data
A weblog is an automated record of a user's online activity, distinct from computer log data that captures system operations. Examples of weblog machine data include clickstream data, IP addresses, timestamps, access requests, bytes transferred, referral URLs, downloads, submissions, and more.
Application log data
An application log is an automatically generated file that records activities within a software application. It includes machine data like the application used, timestamps, issues, downtimes, access requests, user IDs, and file sizes. This data helps assess and prevent errors and track user activity.
What is machine data used for
Machine data serves various purposes across different industries and applications. Exploring and analyzing machine data can unlock a variety of beneficial capabilities, such as:
Ensure essential services operate at expected capacities, meeting customer service level expectations.
Proactively monitor security postures and swiftly detect network intrusions and suspicious activities by capturing machine data.
Understand user interactions with software applications, generate new business intelligence, and make informed, data-driven decisions regarding feature prioritization and bug fixes.
- Machine data, generated automatically, includes sensor, log, geotag, call, web, and application data. It originates from machinery, devices, and computer processes.
- Machine data is crucial for operation, security, and business analytics to provide insights for system performance, security monitoring, and user interactions.
- Despite its importance, machine data often needs to be more utilized. Its notable rise complicates tech infrastructures, but integrating it with big data analytics and machine learning presents valuable opportunities.