Big Data and Decision-Making: Applications and Uses in the Public and Private Sector
Table of contents(15 chapters)
Big data is a buzzword of our times, and yet the awareness of what big data is, how it permeates our daily lives, and how it is applied either in the policy-making process or in the business sector remains relatively low. From a different perspective, while specialists, that is, practitioners and researchers, dealing with the technical facets of big data successfully uncover new features, new domains, and new opportunities related to big data, there is a need of evaluating and examining these findings through the lens of social sciences and management. This chapter offers an insight into key issues and developments that shape the broad and multi-directional big data debate. To this end, the content of the book is elaborated and the key findings are highlighted. In this way, this chapter serves as a very useful guide into the question of how big data is applied across issues and domains and how it is valid and relevant to all of us today.
Part 1: Conceptualizing Big Data, Its Value Added and Relevance in the Modern World
In today’s world, the possibility to collect and analyze unprecedented quantities of data pertaining not only to the operation of private and public sectors, but also to the lives of individuals, bears the potential to employ the data as invaluable source of information, and possibly knowledge. The latter, in turn, may lead to innovation, and innovation-led sustainable growth and development of our societies. Inasmuch as the promise inherent in – what tends to be termed – big data is huge, so are also the caveats related to the prospect of exploiting the benefits of big data. In this chapter, it is argued that four challenges beset the possibility of effective and efficient use of big data today. These include: a substantial degree of ignorance as to what big data actually is; the challenge of obtaining quality data; the challenge of the utilization of big data in the decision-making process; and, finally, the twin ethical and legal challenge pertaining to the acquisition and the use of big data. It is also argued that the awareness of the existence of these caveats enables a critical insight into the promise big data brings about for our societies today. This chapter dwells on these issues.
This chapter focuses on performance measurement and management systems (PMMS) in the inter-municipal cooperation context by considering the development of new capabilities required to exploit the digital governance potentialities in which data integration is essential. The analysis relied on the advent of digital governance, the Italian public informative systems reform, as well as on local governments (LG) renewals through the Union of Municipalities (UMs) – one of the most widespread structured forms of inter-municipal cooperation – based on the sustainability of local service delivery. Through a review of the literature and the conceptual outcomes resulting from the analysis of the dynamic capabilities (DCs) theory applied to digital governance, this chapter aims at suggesting a useful contribution for an effective improvement of PMMS in the public sector networks, with the consequent improvement of resilience in policy management. Thus, the broad information required by the UMs and the complexity of its administration together with the constraints regarding the need to share a common vision and strategy, plan objectives, targets, measurement, and evaluation processes are considered. In particular, three propositions have been developed as guidelines for achieving coordination, coherence, and integration of measuring and managing performances in public networks. This evidence will offer insights allowing scholars and practitioners a practical understanding of whether and how DCs – applied to digital governance – address PMMS challenges within an inter-municipal cooperative context.
This chapter explores young individuals’ attitude toward protection of personal data. Specifically, the discussion focuses on the General Data Protection Regulation (GDPR) and the introduction of a property right over personal data. To this end, a total of 10 in-depth interviews were conducted with both Albanian and Italian college students. Transcribed data from the interviews were analyzed through thematic analysis. Four main themes emerged from the interviews: (i) distrust toward the way personal data are used online, (ii) no change in behavior after the introduction of GDPR, (iii) limited knowledge regarding terms and conditions of websites, and (iv) individual desire for more control over his/her data. In light of the research findings, the present contribution highlights that, despite the GDPR enhances individuals’ data protection and rights, there is an intention–behavior gap that has been labeled in the literature as “privacy paradox.” Furthermore, findings reveal that treating personal data as a property right has pros and cons.
Nowadays, there are billions interconnected devices forming Cyber-Physical Systems (CPS), Internet of Things (IoT) and Industrial Internet of Things (IIoT) ecosystems. With an increasing number of devices and systems in use, amount and the value of data, the risks of security breaches increase. One of these risks is posed by open data sources, which are databases that are not properly protected. These poorly protected databases are accessible to external actors, which poses a serious risk to the data holder and the results of data-related activities such as analysis, forecasting, monitoring, decision-making, policy development, and the whole contemporary society. This chapter aims at examining the state of the security of open data databases representing both relational databases and NoSQL, with a particular focus on a later category.
Part 2: Big Big Data and Its Application Across Policy Fields
The purpose of this chapter is to investigate how big data influences the diffusion of knowledge in firms and how it influences the firms’ innovation process. The chapter identifies gaps that exist in the current literature on the topic. This study is conducted by using a qualitative methodology. PRISMA methodology is used to carry out a systematic literature review. The results shown in this study indicate the positive influence that technology has on knowledge sharing and innovation and on the other hand how good management of knowledge can benefit small and medium enterprises (SMEs). The limitation of this study is related to the methodology, whereas it is considered that a quantitative methodology can be incorporated into the other research resources. The chapter’s aim is to fill a gap in the current literature and to be a new starting point for scholars and managers. Through this work, managers can better understand how to improve the innovation process through knowledge sharing in their organizations. The originality of the present study represents a first step in understanding the mechanisms of knowledge sharing within firms, how it supports the innovation process, and what role big data plays in this context.
This conceptual chapter aims to understand the role of artificial intelligence (AI) in value co-creation phenomena in a healthcare service ecosystem, through a literature review and the definition of a conceptual framework. AI, as an operant resource, can stimulate a completely patient-centered, adaptive and resilient healthcare system, and governance models in healthcare based on data-driven decision-making (DDDM), ensuring faster choices, more timely diagnosis and more personalized treatment paths. However, the full implementation of AI in healthcare is inhibited by some frictions, mainly related to the risk that the AI black box may generate an inadequate automatic decision, also due to the quality of data used, often partial and unstructured given the reluctance to share them by patients concerned by privacy threats. The co-design (multi-part and multi-level) of a predictive decision model based on the functional transparency of the AI algorithm would allow for augmented decision as result of an effective human–machine interaction. Healthcare actors could thus make decisions using the information detected by the software (based on clear cause-and-effect correlations and modifiable variables in case of mistakes), integrated with their professional knowledge. This would also help to strengthen the patient’s perception of the decision’s reliability and accuracy and the safety of the tool (factors that can affect his/her trust). AI may be considered as a driver for value co-creation in healthcare, thanks to transparency. It would allow the promotion of collaborative behaviors involving actors by generating new institutions and new resource integration practices among them.
The progressive increase in the size of datasets has given life to the so-called big data that provides researchers with the opportunity to extract a greater amount of useful information in many sectors, especially in the tourism industry.
The chapter aims to demonstrate that sustainable tourism (ST) could be particularly favored by using big data and a data-driven approach. Furthermore, as ST appears in line with a new type of responsible entrepreneurship, called Humane Entrepreneurship (HumEnt), this chapter investigates the link between ST and HumEnt and the impact of big data and data-oriented approaches on ST and HumEnt.
The research adopts a qualitative approach, applying the case study technique. The authors conducted ten semi-structured interviews with key informants from a specific form of hospitality: Albergo Diffuso. Findings show the advantages of the data-driven approach to tourism and entrepreneurship highlighting how using data creates new opportunities for decision making in ST and HumEnt.
The improvement of the agri-food supply chain sustainability plays pivotal role in the planet’s survival and in overcoming of climate disasters. Digital technologies that support the collection of Big Data produced along the agri-food supply chain (SC) emerge as powerful tools to accelerate the ecological transition of the sector. Digital technologies can support the implementation of circular business models by sharing data across the SC, monitoring in real time the materials flow, automatizing some agricultural practices and improving the decision-making through the development of decision support systems. Despite the relevance of these arguments, there is a lack of shared frameworks and guidelines for the effective development of a “data-driven circular economy” in the agri-food SC. In this scenario, this chapter examines how scholars investigate data-oriented strategies to accelerate the ecological transition and the adoption of circular economy (CE) models in the agri-food sector (AFS). To this end, a systematic literature review (SLR) was performed. Twenty-nine papers were selected following a rigorous sampling process. Both bibliometric and descriptive results are provided in the first part of this chapter. According to the analytical framework developed, the selected papers were examined in light of the “reduce, reuse and recycle” (3R) paradigm. Moreover, an additional R was retrieved from the systematic review (i.e., redesign), broadening the analytical perspective. The results indicate that scholars have predominantly provided theoretical contributions concerning the role of digital technologies and big data for the agri-food circular transition from a macro-perspective. The findings are useful for policy-makers and managers, who can promote and implement the big data-oriented approach to facilitate the circular transition. Limitations and future research directions are also provided.
Part 3: Business and Policy-making Process Empowered by Big Data
This chapter uses multiple research methods, including quantitative science mapping analysis (SciMat) and a qualitative literature review, to provide insight into the academic debate unfolding at the intersection of big data and business processes. SciMat analysis based on keyword co-occurrence enabled identifying 12 of the most productive research themes, as reflected in a poll of 301 articles about big data and business processes. The three most important themes are: firm performance, Industry 4.0, and innovation. The traditional literature review on firm performance indicated that big data analytics (BDA) positively influence business process performance and have a beneficial impact on a firm’s performance, that is, the role of big data is viewed as critical in the context of Industry 4.0 because it enhances productivity and improves business processes. The benefits of BDA can be achieved only if the organizational obstacles related to planning, workforce attitude, and alignment with strategy are overcome. Moreover, big data is perceived as a significant source of innovation in an organization and can be conceptualized with the use of a resource-based view (RBV) of the firm. BDA positively influence business processes, which is strengthened by adequate implementation and openness to innovation.
The circular economy (CE) model is seen as an alternative model to the linear economy models, which seem to be reaching their physical limits. The CE business model aims to reuse materials and decrease the need for virgin materials. This requires the implementation of a reverse supply chain, close collaboration between actors, as well as well-organized logistics. For this reason, the CE companies have typically high demand for digitalized processes and the utilization of data on both operational and business development dimensions. Also the utilization of big data collected from the companies’ business environment can provide new opportunities for business development in CE. Despite the fact that utilization of data collected from the business environment and operations enables data-driven approaches for various decision-making functions in companies, many companies still struggle to figure out how to use analytics to take advantage of their data. In the small- and medium-sized enterprises (SMEs), in particular, the managers are facing difficulties with ever-increasing amounts of data and sophisticated analytics. Indeed, prior research identified several kinds of barriers to the effective utilization of data in SMEs. Still, research on data-driven decision-making remains scarce in CE context. This chapter presents a case study consisting of seven cases, all representing SMEs operating in the field of CE in Finland. In the case study, the barriers and practical challenges for data-driven decision-making in CE SMEs are investigated. Based on the case study results, this chapter proposes that utilization of data, lack of resources, lack of capabilities, and regulation are the main barriers to data-driven decision-making in CE SMEs.
The growing turbulence of the external environment has progressively led to the necessity by organizations of exploiting new opportunities provided by data-driven approaches for supporting the even more complex decision-making processes. The new digital environment has led to the development and adoption of innovative approaches; also in the urban context which has always been characterized by different, interconnected, and dynamic dimensions. Urban governance models have been enhanced by smart technologies, which act as enablers of advanced services and foster connections between citizens, public and private organizations, and decision-makers. In this context, the objective of this chapter is to examine the role of data-driven approaches in the urban context during the chaotic and high variable circumstances related to the diffusion of the Coronavirus disease 2019 (Covid-19). Thanks to the adoption of the co-evolutionary perspective, a cycle in urban governance decision-making approach based on digital technologies is depicted and its contribution for managing the ongoing Covid-19 is traced. The results of the analysis highlight how the data-driven approach supports urban decision-making process and shed light on the co-evolutionary perspective as heuristic device to map the interactions settled in the networks between local governments, data-driven technologies, and citizens. In this sense, this chapter offers interesting insights, potentially capable of generating useful implications for both researchers and professionals in the public sector.
Our world is changing. Cities of the twenty-first century are becoming independent actors on the international scene. Due to the decentralization, gradually cities gain more power to independently identify their policies, strategies and related priorities, as well as ways of their implementation. As a result, several initiatives can be implemented at the city level at a much faster pace and more efficiently than ever before. The United Nations’ (UN) “Transforming Our World: The 2030 Agenda for Sustainable Development” focuses on issues related to sustainable development. In relation to cities, it aims to make them “inclusive, safe, resilient and sustainable.” This chapter investigates the conceptual foundations of the term smart sustainable cities and summarizes strategies that have been undertaken in cities, as a means of making them smart and sustainable. Against this backdrop, by focusing on Ukraine, an implementation algorithm based on selected examples drawn from Europe is proposed.
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