Sustainability and Industry 4.0 are probably the most important topics in the manufacturing field, both in academic and industrial environments. In the context of sustainability within Industry 4.0, as regards the triple bottom line, profit (economic) and planet (environmental) are being studied extensively by the research community, while people (social) are not receiving the same attention. The wider perspective of the social aspects of Industry 4.0 is neglected by the academic community [1]. Industry 5.0 emerged to overcome problems with the techno-centered approach of Industry 4.0 [2], namely the absence of the human being as the key player in an industrial digital environment and the influence on sustainability as it relates to the social pillar. Nowadays, the challenges are related to the way I4.0 is implemented and managed to achieve the desired outcomes—economic, environmental, and social [3].

Within the industrial digital environment, data act as an enabler or creator of value in Industry 4.0 [4], and most industrial organizations should have integrated low-cost analytical systems to collect and measure results [5] in order to stay competitive, especially small to medium-sized enterprises (SMEs). Industry 4.0 projects are perceived by SMEs as cost-driven initiatives [6], and lack of capacity to invest in advanced technologies and the need for specialist expertise [7] to implement them are constraints SMEs face in the adoption of Industry 4.0 and, probably as a consequence, sustainability practices.

Besides these limitations, those SMEs that are capable of incorporating technologies are unable to access sufficiently complex data due to limitations of scale. Leveraging data access, treatment, and analysis and creating intelligence are elements of a company’s preparedness to improve its decision-making processes. For example, the relevance of big data and analytics to competitive intelligence has been demonstrated [8], and for this reason, data science is gaining relevance in contemporary society.

2. Data Science and Open Data

In a world that constantly produces and consumes data, it is essential to understand the value that can be extracted from it. Mikalef et al. [9] consider data science and big-data domains as the next frontier for both practitioners and researchers as they embody significant potential in exploiting data to sustain competitive advantage. Data science is an interdisciplinary field that supports and guides the extraction of useful patterns from raw data by exploring advanced technologies, algorithms, and processes [10]. The actual extraction of knowledge from data is defined as data mining, and it can be applied to a broad set of business areas, such as marketing, customer relationship management, supply chain management, or product optimization [11]. Data science should be seen as the domain that originates from the merging of big-data technologies with data-management skills and behavioral disciplines [12]. Data science and big data can be combined with co-creation and data-sharing technologies to enable organizations to leverage creativity outside their organizational boundaries [13]. The development and operation of software have become increasingly dependent on data [14], and this data can be made more accessible to organizations and individuals through data-sharing and open-source technologies. Runeson [15] highlights the need for the adoption of co-creation and collaboration principles to harness innovation potential and manage costs in the age of data. Today, data volumes are exploding, and not only is the rate of data generated per individual increasing but so also is the rate at which we share information. Lawmakers and organizations worldwide are trying to envision the future of data ownership. Information remains largely centralized, but the trend is shifting toward a distributed and open model of data sharing [16].

3. Open Data for Industry 4.0

As described by Tim Hall [17], one of the key drivers for the adoption of Industry 4.0 across the globe is the ability to use the power of data to revolutionize manufacturing. Open-data platforms provide innovative services with high impact on innovation [18], and data sources based on open data allow for the evaluation of Industry 4.0 readiness by regions [19]. However, the manufacturing sector has been slow to benefit from Industry 4.0 drivers evenly across different industries, enterprise sizes, and geographies. Since most Industry 4.0 technologies require substantial investment to be successfully implemented, the economic factor is undeniably crucial if they are to be adopted. Nevertheless, while differences in the economic situations of enterprises and countries have an obvious bearing on the speed and rate of success of Industry 4.0 adoption, they cannot be considered the only factor involved. Smart factories and smart cities are another relevant study theme, as technological advancements and digitalization are changing how companies operate their business and organizations reshape communities. All those changes and advancements require big R&D investments and qualified researchers and workers. Since there are many economic challenges as well as difficulties in recruiting the most qualified workers, the adoption of those technologies might be slow and unoptimized for SMEs, which need to adapt to technological changes in order to grow and compete. Besides, the integration of open data is still oriented to applications, websites, and platforms [20], whereas it is necessary for it to be oriented toward product development. Only recently, a case-study project based on open data from academia and companies was applied in the development of Industry 4.0 technologies in additive manufacturing [21].

4. Sustainability in Industry 4.0

Wee et al. [22] reiterate that there is a need for deeper research on sustainability as it relates to Industry 4.0, since it has received little attention from academics and researchers. In Kamble et al.’s [23] framework for Industry 4.0 sustainability, the three sustainable outcomes that ideally should be accomplished from Industry 4.0 technologies and process integration are economic benefit, process automation, and safety and environmental protection. The sustainability pillars in manufacturing companies have a strong relationship with Industry 4.0 [24], and competitiveness and social and environmental advantages are potentialized in manufacturing companies that adopt Industry 4.0 [25]. Other models include open innovation and collaboration as guiding principles for sustainability in industry. In this research, analyzing the progress toward accomplishing sustainability goals using the open data available is considered an overall evaluation of sustainability across the three pillars. Since these are broad goals established not only in countries but also in organizations and companies, successful progress toward accomplishing them is also progress toward accomplishing sustainability in Industry 4.0. UN members need to collaborate across all established goals, even more so because Goal 17 itself—Partnerships for the Goals—focuses on evaluating members’ progress toward economic, social, and environmental collaboration between them. For that reason, it is reasonable to assume that progressing in Goal 17 is essential to accomplish successful collaboration in the remaining goals. Social indicators are neglected within manufacturing companies [26], and Industry 4.0 still needs to improve intra-organization mechanisms for achieving social sustainability [1]. Within the social pillar of sustainability, one of the main social issues relating to the digitalization and automation of industry is how employment and skills requirements will be affected. The common understanding regarding this issue is that automation eliminates the need for human workers, which will bring unemployment and social dissatisfaction. However, researchers such as Shet and Pereira [27] believe that Industry 4.0 generates new job prospects in the emerging domains of science, technology, and engineering. Those domains usually require a higher level of skills and specialization than traditional jobs, leaving unskilled workers more vulnerable to the gradual increase in demand for qualified workers.