How to collect, process and analyze IoT data.
Over the past few years, the Internet of Things (IoT) has become a trendy topic for both consumers and businesses. People are keen to follow the latest developments in the area of connected devices. As of today, we have been surrounded by devices that can detect and measure data in real time, which the data can help save time, energy, and money.
For many businesses, the IoT is gradually becoming a significant part of their data-driven strategies. The companies have benefited from IoT for things like improved operational performances or enhanced equipment maintenance. Yet, the value of IoT goes far beyond simple device connection. In order to gain true business insights and competitive advantage, companies should have the ability to collect, process and analyze their IoT data.
The data process contains three stages. First, a device creates data and sends it to the internet; secondly, the central system receives and organises the data; finally, what the data will be used for.
How are the IoT data collected
IoT data collected by connected devices, such as smart appliances, health wearables can be transmitted, saved, and retrieved at any time.
Depending on the complexity of the sensors, there are different types of data:
● Status data: This is the most basic and prevalent IoT data, which most of the IoT devices can generate. It tells the status of the device, for example, whether an appliance is on or off, whether there are available spaces in a parking lot, etc. This type of data is great for planning or maintenance.
● Location data: The location data offer similar functionality like the GPS trackers but better. The IoT provides high data processing speed and precision compared to GPS. Instead of a simple destination, location data allows you to track your item in real time. Common applications of location data are the motion sensors used for fleet
management, asset tracking, etc.
● Automation data: Compared the two types of data above, this type of data is a much higher level data in terms of processing. The automation data helps IoT systems control devices. Whether the connected devices are inside or outside, still or moving. This level of automation therefore requires complex processing capability and minimum room for errors.
● Actionable data: As an extension of status data, the actionable data not only capture bare insight, it enables the system to process and transform the information into easy-to- follow instructions. This type of data is normally used in forecasting, energy consumption optimisation, and workplace efficiency enhancement.
How is IoT data being processed?
Once the connected devices collects the data, the next step is to process them before they can be put into use. With the massive amount of data that comes from a great variety of IoT sensors in different
formats, it is essential to do four things before processing:
1. Standardize or transform the data into a single format that is compatible with the application.
2. Store or create a backup of the newly created format.
3. Remove any outdated or repetitive data for accuracy.
4. Combine additional data from other applications or sources to enrich the current database.
If you use a white label IoT company like myma.io that offers a fully customised solution, then compatibility would not be a problem in this case. You can rely on the IoT service company to take care of all your data collection and processing needs and be sure to check they are GDPR compliant.
How to analyze the IoT data?
This is the most valuable part of IoT data collection as it brings endless possibilities and opportunities for businesses. With well-designed data analysis tools, this valuable information can be extracted from massive data collections and be used to improve business processes, applications, procedures, and production. The common data analytics used are:
● Prescriptive analytics: Often described as a combination of descriptive and predictive analytics, prescriptive analytics is used to analyze which step to take under a specific situation. It functions as a tool to shortlist or scale down information and obtain precise conclusions.
● Spatial analytics: This is an ideal method used to analyze location-based IoT data and devices. Spatial analytics can decipher numerous geographical patterns and determine the spatial relationships between different physical objects. The types of IoT data that benefit from spatial analytics include parking applications, crop planning, and smart vehicles.
● Streaming analytics: With the ability to facilitate massive “in-motion” data, streaming analytics can detect emergency or urgent situations, and also give an immediate response. Traffic analysis and financial transactions tracking are great examples of streaming analytics applications.
● Time series analytics: As the name suggests, the time series analytics is used to analyze time-based data. It’s often used to reveal patterns, trends, and anomalies. The health-monitoring and weather-monitoring systems are two great examples that benefit from time series analytics.
By collecting, processing, and analyzing IoT data, businesses can gain valuable insights to make informed decisions. To learn more on how IoT data can benefit your business, click here or visit myma.io for more information.