For an organization, adopting a data-driven approach means making decisions based on its own data and information. In other words, it is about using the vast amount of data that companies possess as a basis for improving decision-making processes, productivity, and efficiency of the industrial plant. It also means implementing increasingly effective business strategies, and gaining a competitive advantage through the processing and enrichment of data through sophisticated Big Data Analysis techniques.
So-called data-driven companies make data management a pillar of their business strategy. Although the benefits of a data-driven approach are now evident to most companies, its adoption is still a challenge for many. Acquiring appropriate technology is not enough; it is also necessary to undertake a path that leads to the acquisition of a correct culture and awareness of the value of data at all levels of the company.
A study by the Big Data & Business Analytics Observatory of the School of Management of the Politecnico di Milano revealed that, although the Italian Big Data market will be worth €2.41 billion in 2022, a 20% increase compared to 2021, according to the “Data Strategy Index” – a comprehensive maturity index developed by the Observatory – only 15% of large companies can be considered “advanced” in the data management and analysis process, while 30% are “entrepreneurial”, 22% “cautious”, and 33% “immature” or “just getting started”. As for SMEs, 55% declare that they have carried out investments in Data Management & Analytics or plan to do so by the end of the year. This percentage, although growing compared to 2021, does not show significant accelerations compared to the last three years.
With the Industry 4.0 paradigm, the advent of the IIoT (Industrial Internet of Things), the digitalization of companies, and the production of enormous amounts of data, there is increasingly talk of the advantages of a data-driven approach for businesses.
In practice, being a “data-driven company” means having the ability to acquire data – related to the management of company assets, the product life cycle, and the processes that take place within the company perimeter – and transform them into enriched information, therefore a strategic resource on which to base business decisions for more profitable performance. Properly processed, in fact, data become useful information for various purposes, from cost control to operational and production efficiency, to predictive maintenance or the so-called servitization of products, offered on the market as a service and no longer as end goods in themselves.
If the construction of a data culture is the key to creating a data-driven organization, the technological condition is continuous connection between the physical assets that make up the company’s infrastructure and the IoT system. This one is realized through IoT sensors connected to a wireless network, thanks to which the plant can continuously and accurately transmit data relating to its operation. Data processing and their return in the form of enriched information take place through IoT platforms equipped with artificial intelligence that collect field data, transfer them to the Cloud and make them available by analyzing them in real-time. AI/IoT platforms allow the integration of any type of data generated by on-field devices, structured, semi-structured and unstructured, returning high-value information to users. By combining artificial intelligence and machine learning models with Big Data Analysis, Data Orchestration, and Cloud Computing techniques, these platforms continuously process updated data and are able to predict trends, outline behaviors and future scenarios, and develop reference models (digital twins) of machine performance through which to optimize energy consumption and productivity and perform predictive maintenance interventions.
The advantages of a data-driven approach can be attributed to a general, greater understanding of the performance of the industrial plant. Based on this, areas for production improvement can be identified, the efficiency of the plant can be increased, and the level of product quality can be constantly monitored. Another benefit is the acquisition of predictive and prescriptive analytical capability that, thanks to artificial intelligence (AI) and machine learning (ML) algorithms, allows to identify faults or anomalies before they occur and intervene exactly where and when needed, avoiding unplanned downtime of machinery. Additionally, it also suggests what to do to avoid a particular problem, accelerating and automating the decision-making process. Finally, a data-driven approach leads to cost savings by minimizing waste, thanks to precise and real-time monitoring of production. In conclusion, embarking on a transition path towards a data-driven approach entails numerous benefits that directly impact the business in terms of increased process efficiency, cost reduction, more effective decisions that trigger greater productivity and profitability.
Through continuous research and development, SECO has acquired the necessary expertise to offer its customers solutions to support the adoption of a data-driven approach, by providing connected edge devices and AI software that process the data collected in the field with advanced analysis models. SECO solutions applied to the industrial plant allow for reduced maintenance costs and machinery downtime, and enhanced energy and productive efficiency, for a potential impact of 10-15% on profit margins growth.
