Digital Transformation and Business Intelligence (BI) in the Industry 4.0 (I 4.0) Age

Introduction

New ideas for Industry 4.0 and Society 5.0 emerged due to the need to discover fresh approaches to the exponentially growing amount of data being generated by IoTs (Internet of Things). Digital solutions become essential in order to offer immediate answers that improve analysis and uninterrupted process maintenance.

Through the use of smart technologies, civilization quickly advanced from I 3.0 (Industry 3.0) to I 5.0 (Industry 5.0), becoming an extremely intelligent society. Later on, further technologies were added to Information Technology (IT) and Information Systems (IS) to keep relevant data and aid in decision-making. The center of this ecosystem is BI. It is acknowledged that Business Intelligence (BI) is the use of technology for data analysis (Howson, 2003). IT&IS is now a more dynamic system with BI than it was before, offering more immediate answers. The key here is using smart technology to extract relevant information from huge data to provide a real-time solution. This clever process is known as business intelligence (BI), and it serves as the primary tool for gathering, evaluating, reporting, and disseminating important data for decision support systems.

Although BI is commonly misinterpreted as Business Analytics (BA), it is not. BA is a subset of BI in the broadest sense. As part of this ecosystem, BA employs express analytical tools and procedures, whereas BI is composed of strategies, tools, and technology (Ng & Nagalingham, 2023). Stated differently, business analytics (BA) uses data interpretation methodologies to prepare the firm for action.

BI has a layered architecture including data, logic, and access/interface. In the data layer, data from many sources (external and internal) are collected and sorted for subsequent analysis. All stakeholders want to access data because businesses implement open-source data policies due to their transparent management approach. Besides, digitalization efforts, where objects and people are connected from everywhere via the internet, cause the production of a lot of mass data: big data. To access this mass of data, businesses save it in their databases to prevent data from being lost in the data layer. The second layer of BI is logic. In this part of BI, analytical processes for data such as extracting from the main data warehouse, loading to internal data warehouse or datamarts, transforming to meaningful and useful information, and inferring to achieve knowledge are carried out. The main elements are data warehouses, datamarts, ETL, OLAP tools, etc. for this layer. The last layer, access/interface, stands for a visual representation of the results. In this layer, there exist tools such as reports, dashboards, graphs, and tables that will allow the user to easily read the results.

Web 3.0 technologies create an immersive, 3D, and End-to-end (E2E) environment for businesses and customers. In this connected internet ecosystem, the internet service quality such as speed, and ubiquitousness become important. Real-time data analysis and instant solutions are the main facilitators of this ecosystem. Real-time data and solutions are also the main handicap within the BI context. ETL is a tool that allows you to extract, clean, and analyze data by dividing it into smaller, more meaningful pieces. The ability to perform all these ETL operations instantly and at a speed close to real-time plays an important role in the success of BI. The scholars are mainly focused on real-time solutions for BI.

Artificial Intelligence (AI) is the main facilitator for instantaneous solutions on BI. It is possible to make meaningful inferences from data thanks to deep learning, machine learning, and heuristic algorithms of artificial intelligence that solve problems in a very short time. Due to the ability to learn & and understand from the past, extract meaningful knowledge, and quickly respond AI is the main assistance in the decision-making process within the context of BI.