This article highlights a group of concepts combined and traced over the past decades: decision support systems, business intelligence, big data, cloud computing, data science, web services, and many other aspects of Industry 4.0. The main objective of this work is to perform a theoretical analysis and discussion about these elements, thus providing an understanding of why and how they work together, giving us greater ability to come up with new ideas that contribute to Industry 4.0. It begins with the problem of data silos then displays the industrial revolutions. Sections 4 and 5 focus on integration shift within the IS discipline and DSS integral position within the IS discipline, and it finally ends with Section 6, “Dialectic of Technology and Business,” which shows the dialectic of technology and business, explaining the thesis of technology-driven and its antithesis of business-driven. It then synthesizes a more holistic paradigm in Industry 4.0.
TopIntroduction
Major evolution of information systems taken place during the second half of the twentieth century during the shift from Industry 3.0 to Industry 4.0, accompanied with transitional period from 20th century to 21st century, jargon and metaphoric words emerged (Laudon and Laudon, 2022). The fields of data science and big data as an example of one of the most important sciences emerging from these transformations are full of jargon and metaphoric words. For example, data lakes represent a single repository of data that includes raw copies of source system data, sensor data through IoT (Internet of Things), social data, and converted data used for reporting, visualization, advanced analytics, and machine learning (Sharda et al., 2021). This is great, but things aren’t as beautifully simple as the metaphor of the Lake. If we don’t solve the problem of data silos, we may find ourselves facing a data swamp. However, to understand these metaphors we need to know the historical contexts behind these terms. For example, the concept of data silos is information dating back to 1988 (Trkman et al., 2015). The period in which enterprise application emerged, a new era of computing is appearing with more integration and holistic paradigm due to the invention of web in 1990s. This is known as Industry 4.0 technologies, which comes decades after Industry 3.0, in which the big data technologies thrived to integrate more data sources using NoSQL and Hadoop. Data lakes is a good example for of jargon that appeared in this era as an emerging approach to cloud-based big data, in which Enterprises across industries are starting to collect all the different types of data sources through a common ingestion framework like Hadoop-based repository (Stein & Morrison, 2014). This was considered to be 10 to 100 times less expensive to deploy than conventional data warehousing.
The problem of diverse concepts and ambiguous metaphors may be misleading if it is not understood in its proper context. In this chapter, it is proposed that the main reason of this problem is due to isolation between business and technical issues. The good news is that the concepts of DSS still work, and it can bring together all the contradictions. DSS evolved to include what became known as Analytics. The broad and diverse nature of DSS field makes it with high potential to consolidate and provide suitable interpretation for these diverse concepts that evolved through a long period of time.
This methodological suggestion came from the combining and adaptation of commonly used models and procedures in information system research, such as the socio-technical model for information systems. Socio-technical model for information systems views DSS as socio-technical systems with several viewpoints and potential conceptual framework (Hirschheim, Klein, & Lyytinen, 1995). For control or emancipation of effective design (Shim et al., 2002) requires knowledge of both the application domain and the solution domain. The methodology that is used to tackle this dual problem is based on dialectic theory, which was used several times in IS and DSS literature (Elgarah, Haynes, & Courtney, 2002; Haynes, 2001). Further, it was used in similar and related fields, as Boehm did in his Hegelian dialectical (related the German philosopher Hegel) view of the history of the field of software engineering (SE). Boehm (2006) traced evolution of software development methodologies from the classic process models such as waterfall into a spiral model, prototyping and, to agile development methodologies. The latter models seem to have worked well for DSS (Gharaibeh & Soud, 2008; Gharaibeh et al., 2009). In this chapter DSS will be considered as a software system with unique characteristics, from both SE and DSS literature it is found that they have similar beginning, on the one hand power (Shim et al., 2002) in his historical review of DSS field [stated that SAGE (Semi-Automatic Ground Environment- air defense system for North America) is the first computerized DSS, on the other hand, Boehm stated that SAGE was the most ambitious information processing project of the 1950’s. By tracing the evolution of concepts, we mitigate the Epistemological rupture (or epistemological break) according to the French philosopher Gaston Bachelard.