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Many think that promoting "real" innovation is a very satisfying, "cool" job. My friends are astounded when I tell them how long it takes to build a partnership - from six months to a year - and the number of calls needed to get a project started (on average about twenty). They have no idea how many emotional ups and downs you have to manage, both yours and the person’s you’re dealing with.

With data monetization - the monetization of data, which is the process of strategic analysis and practical implementation that allows you to use data as a source of revenue - companies can transform data they have collected into monetizable value just like other assets. In fact, data monetization becomes a strategy that allows companies to virtuously recover the investments made on IoT and IIoT infrastructure, systems and platforms, accelerating their evolutionary path and designing new business scenarios.

Big data monetization - literally, data monetization, that is the process of strategic analysis and practical implementation that allows you to use data as a source of revenue, is one of the most rewarding objectives for any company that wishes to undertake a virtuous data-driven business path. However, its achievement can be hindered not only by a misinterpretation of the data already available but, above all, by a lack of correct investments in emerging technologies.

Edge computing enhanced by AI has countless applications. Let’s take a closer look at its positive impact on data analysis and how businesses can put such data to use.

Engineered with 11th Gen Intel® Core™ vPro®, Intel® Xeon® W-11000E Series, and Intel® Celeron® processors, the SECO LAGOON COM-HPC® Client module Size A helps ensure secure, high-performance computing and interoperability for AI, video, and other data-intensive applications at the edge.

Why should a manufacturing company today, as well as any other organization, embark on a data-driven transformation journey? In 2020, research conducted by Statista measured a 12% increase, compared to 2018, in the adoption of a global data-driven approach for decision-making. Here's what is driving this change.

Edge computing solutions in industrial settings are poised for rapid growth and innovation, driven by the need for real-time insights and localized action. I&O leaders must tread cautiously as vendors begin to collaborate and provide solutions based on temporary and opportunistic alliances.

SECO is named among the Representative Edge Computing Vendors focused on Industrial IoT.

In this paper we propose a retrofitting methodology based on Design Thinking in a steel mill plant, and highlight how the retrofitting activity is important to allow even more companies to migrate to Industry 4.0, reducing the gap between SMEs and Large Industries for participation in the 4th Industrial Revolution.

In the paper we present the design and development of the Machine Learning (ML) modules for two case studies. In both cases we developed a ML model to learn the system’s normal behavior so to identify whichever abnormal condition may arise. Such a framework is usually referred to as Anomaly Detection (also known as Fault Detection or Novelty Detection). Our models succeeded at identifying the injected anomalies. In addition, no anomalies were observed when the model was fed with normal data. The results are discussed considering the trade-off between type of sensors, learning algorithm, training effort, computational demands.

As connectivity and flexibility requirements continue to drive performance demands in embedded edge computers and servers, industries are poised to take advantage. Performance is driving a new wave of creativity and innovation, and both computing modules and processors need to keep pace through enhanced flexibility.