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IoT & Digital Transformation

Discover how digital transformation and the Internet of Things (IoT) are revolutionising business models. Explore the convergence with big data and artificial intelligence (AI) for advanced solutions and predictive management. Harness the potential of IoT and its analytics for business innovation and growth.


New digital business models are revolutionising established companies and creating new ways of doing and understanding business.
As a result, companies are forced to identify new business models to ensure future growth and development, and digital transformation and digital technologies may be the key.
Digital transformation affects the innovation of business models and the understanding of the role of individual digital technologies (e.g. Internet-of-Things, IoT) in the development of new business models.
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Digital transformation and the application of IoT provide leverage for digital business models.

What is the Internet of Things and what is it useful for

Learn what the Internet of Things (IoT) is, how it is transforming the way we live and work, and its applications in the Smart Cities, health, industry and energy sectors.
Our insight

What is the Internet of Things and what is it useful for

Learn what the Internet of Things (IoT) is, how it is transforming the way we live and work, and its applications in the Smart Cities, health, industry and energy sectors.
Our insight

IoT & Big Data

The convergence of the two technologies is a fundamental part of Industry 4.0. Through data analysis, numerous activities that improve production and enable smart manufacturing are possible

There is an almost natural correlation between the Internet of things (IoT) and big data technologies. The former are in fact producers of large amounts of raw data, the latter provide the tools to filter, sort and analyse this data and extract value from it. The convergence of IoT and big data thus constitutes a powerful tool that can be made available to companies for a variety of purposes. For example, in smart cities, where data collected by sensors distributed across the territory constitute a valuable asset from which information of all kinds, from traffic to parking usage, can be extracted. The convergence of IoT and Big data is obviously an integral part of Industry 4.0: connected objects installed in factories send data about their surroundings and their own operation, enabling a range of possibilities, from monitoring workers to controlling machines themselves.

There are different domains to which the continuous circular process generated by the convergence of IoT and big data can be applied, in a crescendo that goes from the individual production process to involving not only the manufacturing company but also its customers (in a servitisation for example). Applied to a production process, it allows it to be monitored in order to improve it and adapt it to changing external conditions. Or, the process can be adopted by different departments of the same company in order to achieve the best integration and improve the results of the entire company. If, as mentioned above, the IoT big data convergence and the continuous circular process are also extended to the customers of the company adopting these technologies, the information sent by the products will provide valuable insights into their operation and the need for maintenance.

In this respect, it should be emphasised that IoT - big data convergence enables predictivemaintenance, i.e. it allows machines and devices to be serviced before they malfunction or fail, whether they are in the factory or installed at the customer's premises.

Finally, the interconnection between objects, devices and machinery, in addition to obtaining information from them, has the advantage of being able to control or configure them remotely. A possibility that can prove extremely useful when the object to be monitored, controlled or programmed is located in an inaccessible place to reach or is placed in a dangerous or hostile environment for the worker.

Thanks to the ever-increasing miniaturisation of sensors and circuits for wireless communication, it is now possible to embed a device in almost every object to collect information about the environment and transmit it over the network.

These devices are referred to as 'embedded systems'. Added to this is the availability of multiple wireless networks to connect objects and machines. The data that IoT devices generate are different depending on the type of object or machine being considered. They can be:

  • data from sensors concerning the external environment (temperature, humidity, presence of smoke, pollutants or hazardous substances, etc.);
  • data on the monitoring of workers (presence in a given area, compliance with obligations regarding personal protective equipment, compliance with safety rules, etc.).
  • data relating to its operation (which can be used by the system to make real-time diagnoses and predict maintenance activities)
  • data relating to information of various kinds, which is collected and then processed by big data analytics systems (e.g. smart city, smart mobility, etc.)
  • data from wearable devices relating to a person's vital parameters, which can be used in the context of telemedicine or sports practice

IoT & Artificial Intelligence

The combination of IoT and AI makes it possible to develop increasingly innovative and advanced technological solutions, so much so that we can speak of 'AIoT'. But how can these two realities be integrated? Artificial intelligence exploits a very valuable resource to be able to function and constantly improve: Big Data! But for one to really be able to speak of machine intelligence, similar to human intelligence, these resources must be reliable and always available.

To overcome this problem, IoT technologies come into play. The latter are in fact capable of collecting, aggregating, analysing and providing predictive models by exploiting a very complex system of platforms and devices. Hence, the joint use of IoT and AI increases the mutual value of the two solutions: on the one hand, AI increases the potential of IoT by implementing machine and device learning; on the other hand, IoT increases the value of AI by providing resources in terms of connectivity and data exchange.

The combination of IoT and AI, which we can refer to as AIoT, thus enables even more reliable and accurate data, predictive models and functions, providing a solid basis on which to develop new efficient and effective technological solutions.

Types of analysis used on IoT data

There are several ways to store data generated by IoT devices, including for analysis purposes: on premises (on-premises), in the cloud or hybridising the two options. The choice between the various possibilities depends on the volume of data, but also on the type of connectivity, as well as other factors (e.g. when there are power supply problems for the devices). Another discriminating factor is the intended purpose of the data: it makes a difference whether the data is collected for storage purposes or for real-time analysis. Alternatively, the analysis may be performed in batch.

Let us examine some methodologies of big data analytics.

  • Distributed analyses: are suitable for analysing large-scale data. Data can be distributed over several databases. Hadoop and Spark software can be used for batch processing.
  • Real-time analysis: is used to analyse time-dependent IoT data streams. In this case, batch processing is excluded due to latency.
  • edge analytics: low-latency analysis method. It is used to pre-analyse data in order to filter out any duplication and to reorder and aggregate the data before the actual analysis. This processing generally takes place at the point of data acquisition, i.e. on the IoT devices themselves or on gateways, hence the term 'edge'.
  • hybrid: is considered the most common approach; it involves edge pre-analysis, before sending the data stream to the cloud data centre.
  • descriptive analysis: they derive from big data a kind of 'picture of the situation', which can be visualised through reports and graphical dashboards.
  • Diagnostic analysis: provides information on the causes of a given situation. Diagnostic analysis enables predictive analysis.
  • predictive analysis: they make it possible to predict malfunctions or shutdowns of machines and plants and allow preventive maintenance interventions.
  • prescriptive analysis: it goes beyond predictions and when faced with a problem suggests, and possibly implements, countermeasures.

IoT applications in Smart Citiy

Discover the applications of IoT in Smart Cities: smart lighting, smart parking, urban safety, traffic management and energy resources. Benefits and smart solutions for a more efficient and sustainable city.
Our insight

IoT applications in Smart Citiy

Discover the applications of IoT in Smart Cities: smart lighting, smart parking, urban safety, traffic management and energy resources. Benefits and smart solutions for a more efficient and sustainable city.
Our insight


In conclusion, the convergence of the Internet of Things (IoT) with digital transformation, big data and artificial intelligence (AI) is opening up new frontiers in the corporate world. Digital transformation has become an imperative for companies seeking to remain competitive and innovate their business models.

IoT, together with big data, offers a powerful tool to collect, analyse and derive value from the data generated by connected devices. This convergence is particularly relevant in the context of Industry 4.0, where the combination of IoT and big data enables better management of production processes and the implementation of predictive maintenance solutions.

In addition, the integration of IoT and AI, known as AIoT, is enabling the development of increasingly advanced and reliable technology solutions with the ability to collect accurate data and provide precise predictive models. Finally, IoT data analysis can be performed using different methodologies, such as distributed analytics, real-time analytics and edge analytics, giving companies an in-depth view of their operations and paving the way for preventive interventions and data-driven decisions. In summary, IoT and its interactions with digital transformation, big data and AI are redefining the way companies operate, creating opportunities for innovation and growth.
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