Machine Data

Business application data, human-generated content, and machine data are the three types of data that exist. Almost every software application and electronic device generate machine data all the time.

Applications, servers, network devices, sensors, browsers, desktop and laptop computers, mobile devices, and other systems used to support operations generate information about their state and activity on a continuous basis.

We will cover the following:

  1. What is Machine Data?
  2. Why Collecting Machine Data is Important?
  3. Types of Machine Data
  4. Benefits of Machine Data

What is Machine Data?

Machine data, also known as machine-generated data, is information that is created without human interaction as a result of a computer process or application activity. This means that data entered manually by an end-user is not recognized to be machine-generated.

These data affect all industries that use computers in their daily operations, and individuals are increasingly generating this data inadvertently or causing it to be generated by the machine.

Application log files, call detail records, clickstream data associated with user web activities, data files, system configuration files, alerts, and tickets are all examples of machine data.

Both machine-to-machine (M2M) and human-to-machine (H2M) interactions generate machine data. Machine data is generated continuously by every processor-based system (including HVAC controllers, smart electrical metres, GPS devices, and RFID tags), as well as many consumer-oriented systems (mobile devices, automobiles, and medical devices with embedded electronic devices).

It can be either structured or unstructured. In recent years, the increase of machine data has surged. The expansion of mobile devices, virtual servers and desktops, as well as cloud-based services and RFID technologies, is making IT infrastructures more complex.

Humans rarely alter machine data, although it can be collected and analysed. Machine data is generated automatically, either on a regular basis or in reaction to a specific occurrence. Machine data includes call logs, transaction records, and network logs that record any IP address that pings a given server.

For some time, the amount of data generated and stored by humans has been growing exponentially on an annual basis, fuelled by the expansion of telematics technologies like GPS, Wi-Fi, and mobile data networks, as well as Radio Frequency Identification (RFID) and the Internet of Things (IoT).

As more businesses use big data analytics and machine learning, there are more chances to properly analyse machine data alongside other corporate data types to gain fresh ideas and views that can aid them in making better business decisions

Why Collecting Machine Data is Important?

Machine data can provide a wealth of useful information and commercial benefits. But, for the most part, this data source remains mostly unexplored. The application—in other words, how humans can get value from this data—is the most crucial aspect.

If a company wants to stay ahead of the competition, it must first understand its customers' aggregate behaviour. Using the right data products, businesses can obtain insight.

Machine data has enormous potential for enabling more precise models in a variety of applications. These models have the potential to alter the way businesses are run. Machine data, in particular, allows you to hear the voice of each individual customer rather than a group of customers. This provides a level of business information that was previously unimaginable.

Consider how corporations would be able to see their potential customers' purchase histories. It's possible to figure out what their passions are. User habits and product features are used by recommendation engines to anticipate the best match product for enhancing the user experience.

Naturally, you want to make certain that your customers get the service they deserve. As a result, you must guarantee that important processes are operating to their full potential. You and your company can improve how you function on a daily basis by employing real-time machine data delivery.

You won't have to worry about unanticipated difficulties affecting your applications or network if you capture machine data. When you use this data appropriately, you can spot problems early on and solve them before they become complicated.

Types of Machine Data

The most common types of machine data are as follows:

#1 Sensor Data

Sensors are typically put in critical elements of machinery for monitoring and maintenance purposes, such as compressors, conveyors, and pumps. Sensors are also frequently found in devices that require them to function in the first place, such as smart home security systems and automatic thermostats.

Sensors work together to continuously monitor, measure and gather Machine Data (e.g., movements, temperatures, pressures, and rotational speeds). Further review and analysis of this data are possible, allowing for the extraction of insights and the implementation of action plans.

#2 Computer or System Log Data

Computers generate log files that include information about the system's operation. A log file is made up of a series of log lines that show various system actions, such as saving or deleting a file, connecting to a Wi-Fi network, installing new software, opening an application, attaching a Bluetooth device, emptying a recycle bin, and more.

Some types of computer log data are shared with the manufacturers of computers, operating systems, applications, and programs, while others are kept locally and confidentially.

#3 Geotag Data

Geotagging is the process of adding geographical metadata to a media type based on the location of the device that created it. Geotags, which can include timestamps and other contextual information, can be generated automatically for photos, videos, text messages, and other types of media.

Details such as latitude and longitude coordinates, altitude, bearing, and more would be included in this form of Machine Data.

#4 Call Log Data

The Machine Data connected with telephone calls is referred to as a call log or call detail record. The automated process of gathering, recording, and evaluating data regarding phone calls is known as call logging.

The call duration, start and finish times of the call, the caller and recipient's locations, as well as the network utilised, are all recorded in the logs.

#5 Web Log Data

A weblog is an automatic record of a user's online activity, as opposed to computer log data, which records actions that occur during the functioning of a system.

Clickstream data, IP addresses, timestamps, access requests, bytes transferred, referral URLs, downloads, submissions, and other types of Machine Data are all examples of this sort of Machine Data.

#6 Application Log Data

An application log is a file that keeps track of the activities that occur within a software application. Despite the fact that human users initiate the actions, the Machine Data referred to here is generated automatically rather than being manually entered.

The application utilised, timestamps, problems, downtimes, access requests, user IDs, file sizes uploaded or downloaded, and more are all included in this data. These records can be used to assess and prevent recurrences of errors, as well as to follow the activity of various people.

Benefits of Machine Data

Let's look at some of the major benefits of Machine Data:

  • Business Intelligence and Data Analytics
    Companies are always improving their processes, products, and services in order to better serve their customers. Machine Data, in particular, plays a significant role in achieving this edge. Companies can use sensor data to determine which parts of their applications bottleneck due to high traffic or shut down due to errors, brands can track customer journeys on their websites to see which pages or products are most clicked on, and companies can create behavioural profiles on target audiences.
    Predictive Maintenance
    Rather than arranging maintenance on a regular and fixed schedule, predictive maintenance employs data analytics and condition monitoring sensors to forecast when a repair is required for a machine. Sensors in the machine measure, monitor, and generate Machine Data, which is subsequently sent to software for processing and analysis. This data and its findings inform us whether or not there are any errors, as well as what those irregularities are and where they exist.
  • Log Management and Analysis
    Log data is used frequently by many commercial systems. Logs are time-stamped records of actions and decisions taken by applications. They also provide other runtime information. The concepts of log management and analysis are not new. However, the volume of log data generated by corporate processes is always expanding. As a result, the problem of storing, analysing, and presenting data in the most efficient manner increases.
  • Customized Customer Experience
    A trail is left behind every time a user interacts with an application. And for businesses, these trails offer priceless information. Companies can forecast user preferences using log data. They may then customise their products in real-time. And an increase in conversion rates is generally the result. Also, the power behind a very popular feature, most usually employed in streaming services, is log data created from billions of clicks and user behaviour patterns.
  • Improving Cybersecurity
    Fraudulent conduct is a persistent threat to businesses. They employ data analytics to spot fraud patterns and inconsistencies in their systems. Massive amounts of log data can be examined by systems. They use the patterns in this data to detect and prevent probable fraud. This form of log data can be found on a variety of devices, databases, files, and applications.


You can increase revenue, improve operational efficiencies, and gain a competitive advantage by using machine data. However, you must know what you're looking for in your data. You can use and benefit from machine data once you've verified that.

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Janani works for Atatus as a Content Writer. She's devoted to assisting customers in getting the most out of application performance monitoring (APM) tools.

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