Internet of Things: how the data management cycle works

5 min

Since data has become the lifeblood of decision-making and operations, companies are increasingly looking for devices that can connect and provide information for processing. Internet of Things technologies enables a connection of devices to the network by speeding up and increasing the data collection, management, and analysis process. Let’s look at how IoT works and what the main steps are.

The connection of IoT devices

We have already covered the discussion regarding what IoT is and what the benefits are for businesses, but wanting to take up the process that pushes the data toward its processing in the cloud, what are the crucial steps? The first step is technical and involves connecting IoT devices to the network.

We can connect to the network any machinery from which we want to take process or operational data through sensors embedded in IoT devices. The moment a connection to a cloud platform or Edge device is activated and we are able to visualize the data, we can call that machinery or device connected and smart.

What data can I collect through IoT devices? Depending on the choice of IoT devices, we can get complex data, such as video formats, or simple data such as the count of parts produced by a machine. IoT sensors, in fact, can be temperature, level, accelerometer, humidity, or GPS trackers.

This generates 3 macro categories of data:

  • Equipment data: this is data useful for process monitoring and predictive maintenance and allows energy savings, increased productivity and increased machine longevity.
  • Smart meter data: allow for advanced metering useful especially for energy monitoring. Such data reduce measurement costs and errors.
  • Environmental data: IoT sensors can be used to measure and monitor, for example, humidity, temperature, motion, and air quality.

The protocols and gateway for data transmission

Next, we turn to data transmission, which is done through gateways and IP protocols. Gateways are bridges that allow information to pass from the IoT device to the cloud. If there are perimeter proximity devices in the enterprise, such as Edge Computing, some of the data produced by the IoT will be processed locally. While only those that need further processing will be sent to the cloud. In the latter case, the data will not all arrive in the cloud. This aspect is crucial to make an initial skimming and reduce, thus, network traffic and latency times.

There are a variety of ways in which we can connect devices to the cloud, depending on the infrastructure available in the enterprise: cellular, satellite, WiFi, low-power networks (LP WAN) or direct connection via Ethernet.

IoT protocols, chosen according to the reference system architecture, can also be different: AMQP (Advanced Message Queuing Protocol); DDS (Data Distribution Service); Bluetooth; ZigBee; LoRa; MQTT (Message Queue Telemetry Transport).

IoT working

Platforms and data processing: data processing in the cloud

In data processing, data will need to be fed into a processing cycle to be transformed into information useful for the production process or decision making. During the processing cycle, raw data from IoT devices undergo initial manipulation (classification, sorting, or computation) and then, through software, is transformed into information that the end-user can read.

These steps are typical of cloud-to-device data communication and occur on the chosen IoT platform that connects the entire IoT device architecture with applications-a kind of operating system for the connected system. Cameras, self-driving vehicles, smart machinery, sensors, and smart robots will send different values to the platform that may already be complete if it is raw data to be entered into a table or to be worked on in the case of numbers that need to be interpreted or entered into other applications.

For example, if the smart camera I have in the factory detects the entry of an unidentified vehicle into the company fleet, it could be an intrusion or an unregistered but harmless entry. The data, in this case, is an alert that means nothing in itself but needs human oversight.


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The IoT platform, which includes the software needed for the analysis phase (Analytics IoT), can be installed locally (on-premises) or be embedded in Cloud services (IoT Platform as a Service). The choice depends on the amount and quality of data to be processed.

If the data are contained and are needed to make a given plant’s production process smart, an on-premises solution might be convenient. Otherwise, in the case of a large amount of data and the need to manage it in different and distant parts, the cloud is the most versatile solution. Another variable to consider is the response time of data processing: if I need to act quickly, I would be better off with an on-premise solution. That way, in the case of changes, the work of the machinery will not be slowed down, and the data reprocessed locally can be reused immediately.

Before choosing an IoT platform, it is essential to:

  • analyze the business environment and your business model;
  • assess the investment you want to make (licensing costs, implementation costs, and integration to existing IT systems; in-house consulting and training);
  • assess the degree of security in terms of guaranteed protection systems.

Data visualization and analysis

Once data has been collected through connected devices, processed by the cloud platform, and transformed into useful information, it can be visualized. Resources will analyze these values through software and applications chosen to read the IoT platform. The user interface of the software will provide as output the information that you have chosen to monitor. This could be data that is used to develop predictions useful for decision making or monitoring and analysis data that will optimize operational processes.

Through the historical data of a given activity or process, for example, I will be able to outline future trends. This step will allow me to evaluate possible investments based on forecast changes. Or I will be able to use them to understand what happened in the case of a faulty process.

Here is an example. If I want to evaluate the consumption forecast of the production of a given product, I will go to the IoT platform and read the historical data. By calculating the average consumption and comparing the current cost of energy with the future cost of energy, I can predict when I will consume. I need this data not only to evaluate the economic aspect but also, for example, to decide whether to change machinery and get a more efficient one. The greater the amount of data to feed to the software’s Artificial Intelligence, the more reliable the predictive values will be for decision making.

Predictive data are not the only interesting values to analyze. Equally important is the information that is used to improve operations and related flows on a day-to-day basis. Monitoring real-time data about a machine, for example, will allow me to take immediate action on a drifting process. That way, I will not have to fix the problem after the fact, that is, when the costs have already been incurred and, in some cases, the damage cannot be remedied.

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