The Industrial Internet of Things is becoming increasingly popular, not only as a buzzword, but also in terms of functional deployment. An increasing number of companies, in fact, are relying on data collection systems to monitor and control business processes, with a focus on managing production processes. But data collection brings with it an important question: how to exploit the IoT data collected by industrial sensors? In fact, collecting data is only the first step in a supply chain that, if put into practice, can bring important benefits.
Valuing the data collected by IoT platforms is a process in its own right
It is often said that data is the new oil. A saying that has been circulating for quite some time and that Forbes seized on nearly a decade ago. But the comparison does not stop at intrinsic value. In fact, the analogy runs deeper: to make a profit from oil, it must not only be harvested, but processed and refined. The data follow a similar principle: harvesting is thus only the first step.
Data once collected must be processed, synthesized and made usable, thanks to a series of analytical processes. And with the advent of big data, or at any rate massive collection, companies often find themselves in the position of having great untapped potential. Valuing data from the Industrial IoT, in fact, leads to a number of benefits, some direct, some indirect. Of which, for example, theprocess optimization is only the starting point.
However, in order to reap these benefits, it is essential that companies equip themselves with appropriate tools and procedures for analyzing the data they collect.
Let us now look at some indispensable ones for enhancing the value of data from the IIoT.
We prepare an infrastructure adequate for the present and ready for the future
Once a company has defined the need to analyze IoT data, it is necessary to identify the actors involved and verify their competencies. A project along these lines may require acquiring new skills, either through training, acquiring appropriate resources or outsourcing.
In some cases, it may be necessary to create a dedicated department, managed by a specialized figure such as a chief data officer (CDO) to support IoT data analysis efforts and manage data governance.
Given the need for analytics, moreover, infrastructure efforts may also be required. However, in some cases, if the company already has an architecture for data collection, it could also be adapted for analysis, either real-time or deferred. Indeed, the use of a common infrastructure in many cases promotes the movement of data within the organization, as well as allowing for greater flexibility.
Flexibility that is increasingly necessary: data from the IIoT, in addition to growing steadily in quantitative terms, has a tendency to vary in qualitative terms as well.
Most IoT data today is telemetry, but some endpoints also initiate the collection of other types of data, for example, images and audio. An infrastructure today should be flexible enough to adapt to any changes in the type of collection and analysis. Coping quickly with change is precisely one of the prerogatives of Industry 4.0.
Organizations often fail to effectively manage IoT data due to the lack of a sufficiently flexible and elastic data architecture. By providing the right level of flexibility, the right balance between cost, efficiency and competitiveness can be achieved. Great help in this regard can come from cloud-based infrastructures.
Leveraging Artificial Intelligence.
Big data analysis is one of the fields where Artificial Intelligence shows the best potential.
From video surveillance to intelligent control and data acquisition (SCADA) systems, the areas where Artificial Intelligence and Machine Learning show benefits are many. For example, many green companies are using IoT data to build intelligent control systems that maintain required levels of sustainability.
We provide analytics and processing throughout the supply chain
The principle of information sharing is one of the cornerstones of data-driven strategies. But, to really work, sharing must also cover the outputs and results generated by analytics systems, which will need to be returned to different departments (and/or locations) in an equally efficient and timely manner.
Infrastructure aggregation and decentralization capabilities play a key role in this context, particularly for large-scale companies.
We leverage IIoT data to identify new opportunities
The data generated by IoT platforms can be leveraged both within the company and externally.
Inwardly, the process is fairly well established thanks to activities such as analytics and predictive maintenance. But extended data sharing throughout the supply chain, in addition to being a prerogative of Industry 4.0, also brings important economic benefits.
Interfacing with suppliers of raw materials and machinery is already well known, while for example sharing data with customers or contractors is still a partially unexplored field, particularly in the manufacturing sector. But providing real-time analytics, for example on the progress of a supply, is an important competitive advantage in a growing number of supply chains.
