The Main Ingredient Of The Internet Of Things (IoT) Is Data

by Tomaž Lukančič | Oct 15, 2019 | Blog

New technologies and new business models enable the development of the Internet of Things (IoT), digitalization, industry 4.0 and other new approaches that have already begun to change our work and living environments. We see their impacts in our daily lives, in traffic, in the shopping experience and elsewhere, but there are still many opportunities and challenges to be tackled by the development of IoT.

IoT should not be implemented for the sake of the company following new technology trends. Such an approach is wrong. Business executives need to understand in particular what they can gain from IoT and what competitive advantages it will bring. Therefore, they need to know the business requirements and identify the impact factors, expected results, and key success indicators. In many cases, these requirements are already set by customers or actors in the supply chain, many examples can be found in the automotive and pharmaceutical industries. IoT also evolves in business environments such as financial institutions, communications services, commerce and logistics, and manufacturing.

There is, however, one common denominator to it – data. Data should be captured from different systems in real-time, properly processed and passed on to the end-user as useful information. The amount of data captured can be very large and its composition varies greatly (structured, unstructured, files…). To successfully tackle the data challenges, companies need new data platforms. Our experts can be consulted about which ones are best suited to their business.

IoT development stage depends mainly on the industry
Many manufacturing companies have already implemented robots and other advanced machines, which also have advanced management and control systems. Their role in a manufacturing process or line is controlled by different sensors, PLC and/or SCADA systems. Most often these are closed systems that are excellent for their narrow field of operation but have limited connectivity and data integration capabilities with other solutions, information systems included. As a result, businesses are facing a new challenge of combining traditional business ICT environment with the industrial environment (OT) which has been so far separated.

At CRMT we note that some Slovenian companies are very advanced when it comes to the use and management of IoT solutions while some are still in the initial steps. Many companies are also using older machines in their manufacturing environment – they can be 20 years old or even older – that are still relevant to the process and mechanically relevant. However, they often have electronic and sensory equipment that, from today's point of view, might be outdated and incompatible with new solutions, especially information systems and environments. So there is a problem of connecting these devices to the IoT environment.

How well companies are adopting the IoT world depends mostly on the type of industry, product complexity and maturity of the industry in general.

IoT development has several phases and stages of maturity

The phases are mainly divided into:

  • primary data capture (sensors, PLC, SCADA, other sources);
  • real-time streaming of data;
  • processing the captured data, such as aggregation, cleaning, anomaly correction, and normalization;
  • processing, aggregation, storage, analytical processing and visualization, sharing and integration of data with other information systems;
  • performing advanced analytical processes such as artificial intelligence (AI), machine learning (ML) and others.

IoT requirements and challenges
When it comes to IoT, Slovenian companies primarily face the following requirements and challenges:

  • product tracking (operational insight into the manufacturing process, digital product information);
  • product and quality control (quality assurance at intermediate stages);
  • predictive maintenance and interventions in the production process (changing tools, setting up machines);
  • real-time quality monitoring and action (sharing AI and ML);
  • servitization and user experience (monitoring and managing performance and trend forecasting).

IoT requires new skills and a lot of collaboration
The approach to IoT solutions and projects is specific from the data point of view, as it is not the same as the process of implementing an IT solution, such as ERP, where most of the requirements and parameters of the deployment are known. The acquisition of data from sensor systems requires adequate knowledge. Also, IoT projects require a lot of experimental and data engineering work as well as intensive end-user collaboration. At the data level, therefore, specific knowledge and experience are needed, which companies usually do not already possess and so cooperation between different teams and even companies is even more important.

This is followed by more advanced solutions, integrations, analytical solutions, artificial intelligence (AI) and machine learning (ML). Properly prepared data is very important, while mastering all the processes requires new approaches, especially in understanding content and information technology environments. Usually, a need to process large amounts of structured and unstructured data in real-time appears, for which some (older) information technologies are inadequate and new data platforms need to be implemented.

Modern companies are striving to have an insight into the overall state of the enterprise, and the introduction of IoT solutions is the future of business. Namely, while ERP systems mainly collect all business data, including certain production data (work orders, machine occupancy...), companies with IoT solutions get a detailed view of the state of the production process, down to the individual machine in real-time. By combining data from business and manufacturing information systems, companies can use analytical approaches and tools to obtain relevant information and indicators that can be used as a basis for improving manufacturing processes, creating new business models, increasing efficiency and profitability, and better managing and decision making.