What is Data Logging?

Arfan Sharif - December 21, 2022

Data logging is the process of capturing, storing and displaying one or more datasets to analyze activity, identify trends and help predict future events. Data logging can be completed manually, though most processes are automated through intelligent applications like artificial intelligence (AI), machine learning (ML) or robotic process automation (RPA).

Data loggers can serve many purposes across various industries, including tracking supply chain and transportation activity; measuring temperature and humidity levels in various locations; monitoring growing conditions and environmental conditions in greenhouses or farms; and reviewing network performance and CPU usage.

How does data logging work?

The data logging process consists of four main steps:

  1. A sensor gathers and records the data from one or more sources.
  2. A microprocessor then performs basic measurement and logic tasks, such as adding, subtracting, transferring and comparing numbers.
  3. Data stored in the memory unit of the data logger is then transferred to a computer or other electronic device for analysis.
  4. Once analyzed, the data is visualized through a knowledge graph or chart.

Four types of data loggers

Data loggers fall into four basic categories:

  1. Standalone data logger
  2. Wireless data logger
  3. Computer-based data logger
  4. Web-based data logger

Standalone data loggers

Standalone data loggers, or standalone sensors, are small, portable devices typically equipped with a USB port. These devices can either have an internal or external sensor which allows the device to track data from an on-site or remote location, respectively.

Wireless data loggers

Wireless loggers, or wireless sensors, are a type of standalone data logger that accesses data via wireless technology (such as a mobile app or Bluetooth) and transfers it via cloud technology. This eliminates the need for manually retrieving and compiling data from various systems.

The main benefit of using a wireless data logger as compared to a standalone sensor is speed. Cloud-based services can enable the system to automate the transfer of data at constant or regular intervals. The actual process is significantly faster than the manual downloading of data from a sensor.

Computer-based data loggers

As the name implies, computer-based data loggers, or computer-based sensors, are data loggers that are tethered to a computer. A computer-based logger supports real-time visibility into sensor data, while software applications on the computer enable real-time analysis. The main drawback of a computer-based logger is that it is limited by the system the sensor can run on.

Web-based data loggers

Web-based data loggers, or web-based sensors, are the most advanced type of data logger. This system is connected to the internet, typically through a wireless network; though, in some cases, an ethernet connection may still be used. Collected data is transferred and stored on a remote server and accessed on demand.

Like a computer-based logger, web-based sensors can enable real-time monitoring and analysis. However, a computer-based sensor can also provide real-time alerts based on logging levels set by the IT team. While this capability can be helpful to the business, it requires significantly more energy from the logger, which means it either needs its own power source or may be prone to draining the battery of the endpoint it is associated with. However, the web-based logger is not limited by the system the sensor can run on, as is the case with computer-based loggers.

How to retrieve data from the data logger

The method for retrieving data from the data logger depends on the type of data logger in use. As noted above, for standalone devices, data must be transferred or downloaded manually; for wireless or web-based data loggers, the transfer process can be automated via the cloud.

Data loggers vs chart recorders vs data acquisition systems

Data loggers are one of the most popular data management solutions given that they offer organizations a great deal of flexibility in when and how data will be collected and stored. They can also accommodate sizable datasets from one or more inputs.

Other data management solutions include:

Chart recorder

A chart recorder is a traditional data management tool used to record various inputs. While most chart recorders record data on paper, digital models that display log data on a computer or other device have been introduced. However, this capability significantly increases the cost of the chart recorder, which prevents them from being cost-competitive with data loggers.

In addition, data loggers generally offer far more functionality, speed and ease of use compared to chart recorders. They typically can also support a wider variety of input types, which can be changed over time to meet the organization’s evolving needs.

Data acquisition system (DAQ)

A data acquisition system (DAQ) is a collection of hardware and software components that support data collection, measurement, storage, analysis and alerting.

The main difference between a data acquisition system and a data logger has to do with independence. A data logger is a standalone device that can typically function with or without a computer. A data acquisition system must remain tethered to a computer system to function.

Beyond that, the use cases for a data logger and DAQ differ widely. A data acquisition system is designed to process sensor data very quickly for a relatively short period of time. This makes a DAQ an ideal solution for advanced use cases such as military ballistics testing, automotive combustion analysis, vibration analysis and aerospace telemetry recording.

Data loggers, on the other hand, are designed to record data that has less variability over a longer period of time. This can often include factors like temperature, humidity, current, voltage or CPU usage.

Customer Story: Lunar Bank

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Benefits of using a data logger

Using a data logger — as opposed to manual data recording or casual observation — allows the user to better understand the inputs being tracked, as well as how and why they may change over time. This allows the business to respond to issues proactively, potentially helping to reduce costs and waste while boosting efficiency. For example:

  • Using a data logger to consistently track temperature data or relative humidity levels in a storage facility can ensure that goods, such as food or medications, do not spoil or degrade
  • Installing a sensor to measure moisture in soil can help farmers adapt watering schedules, thus conserving resources and improving crop output
  • Applying sensors on transport vehicles allows the manufacturer to track movement of goods or even reroute shipments based on traffic delays or weather

Benefits of data logging for cybersecurity

Within the context of cybersecurity, data logging enables the IT function to identify suspicious behavior or anomalous activity that may indicate a potential compromise or cyberattack. Data logging can be used to track:

  • Interactions and events within the IT environment to establish a baseline of “normal” network activity
  • Access to and use of applications, data, devices and other assets
  • When and how files, data or other assets are downloaded, modified or exported

Using a data logger improves visibility and enables real-time insights into system health and operations. A data logger can help organizations:

  • Enable real-time monitoring and alerting, which improves detection and response times in the event of a breach or other security event
  • Improve observability and visibility across the enterprise, helping the team better manage and monitor the attack surface
  • Support faster and more precise troubleshooting capabilities through advanced network analytics
  • Prioritize activity based on alert parameters set in the system

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Arfan Sharif is a product marketing lead for the Observability portfolio at CrowdStrike. He has over 15 years experience driving Log Management, ITOps, Observability, Security and CX solutions for companies such as Splunk, Genesys and Quest Software. Arfan graduated in Computer Science at Bucks and Chilterns University and has a career spanning across Product Marketing and Sales Engineering.