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This paper discusses how educational policies have shaped the development of large-scale educational data and reviews current practices on the educational data use in selected states. Our purposes are to: (1) analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads; (2) propose the concept of Data Literacy (DL) for teaching (Mandinach & Gummer, 2013a) and its relevance to researchers and stakeholders in postsecondary education; and (3) provide future implications for practices and research to increase educational DL among administrators, practitioners, and faculty in postsecondary education.

We used two guiding conceptual frameworks to analyze the common practice and use of educational data in postsecondary education institutions and identify challenges as the educational crossroads. First, we used the 4Vs of Big Data by Rajan (2012) to examine the misalignment between the policy mandate and the practices. The elements of the 4Vs of Big Data – volume, velocity, variety, and veracity – help us to depict how Big Data enables educators to organize, store, manage, and manipulate vast amounts of educational data at the right moment and at the right time. Second, we used the conceptual framework for DL proposed by Gummer and Mandinach (in press). They interpret DL “as the collection, examination, analysis, and interpretation of data to inform some sort of decision in an educational setting” (p. 1, in press).

Using the guiding frameworks, we identified four educational data crossroads as follows:

Crossroad 1: Unintended Increase in Workload Volume;

Crossroad 2: Unrealistic Expectations of Data Velocity;

Crossroad 3: Data Variety in Silos; and

Crossroad 4: Data Veracity and Policy Agenda Mismatch.

In this paper, we explain each of these crossroads in more detail with some examples.

Much of the existing body of literature, exemplary practices, as well as federal and state funding has been focused on K-12 education contexts. In this paper, we identify current practices and challenges of educational data in the institutions of higher education. Additionally, this paper presents the application of the exemplary practices of data literacy development in postsecondary education and implications for future practices of data literacy development in postsecondary education.

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.

Significant advances in digital technologies impact both organisations and knowledge workers alike. Organisations are now able to effectively analyse significant amounts of data, while accomplishing actionable insight and data-driven decision-making through knowledge workers that understand and manage greater complexity. For decision-makers to be in a position where sufficient information and data-driven insights enable them to make informed decisions, they need to better understand fundamental constructs that lead to the understanding of deep knowledge and wisdom. In an attempt to guide organisations in such a process of understanding, this research study focuses on the design of an organisational transformation framework for data-driven decision-making (OTxDD) based on the collaboration of human and machine for knowledge work. The OTxDD framework was designed through a design science research approach and consists of 4 major enablers (data analytics, data management, data platform, data-driven organisation ethos) and 12 sub-enablers. The OTxDD framework was evaluated in a real-world scenario, where after, based on the evaluation feedback, the OTxDD framework was improved and an organisational measurement tool developed. By considering such an OTxDD framework and measurement tool, organisations will be able to create a clear transformation path to data-driven decision-making, while applying the insight from both knowledge workers and intelligent machines.

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.

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.

Educational reform today is being ruled by hyper-rationality. Tyrannies of imposed reform, and technologies of individualized online learning, are separating learners and their teachers from their feelings, their fellow teachers and learners, and their future purposes and dreams. Curriculum standards, accountability, performance evaluations, targets and testing – all these assume a rational, linear system of delivery that can be broken down into the granular organization and administration of cognitively managed technical tasks. Nowhere is this more evident than in the domination of reform thinking and practice by data-driven improvement and accountability.

Contemporary organisations are data-driven with sophisticated and strong Information Technology (IT) supporting the Business Intelligence (BI) systems. Due to the Industrial Revolution 4.0, businesses are subjected to volatility, uncertainty, complexity, and ambiguity (VUCA). The accuracy and agility of decision making (DM) play a key role in the success of contemporary organisations. Traditional methods of DM, i.e. based on tacit knowledge, are no longer relevant in the constantly altering business scenarios. Innovations in the IT domain have accomplished systems to gather and process business data at an exponential speed. Context-driven analytics along with computation capability and performance-driven visualisation have become an implicit need for businesses. BI systems offer the capabilities of data-driven DM simultaneously allowing organisations to predict the future business scenarios. Qualitative research is conducted in this chapter. In the research, interviews, questionnaires, and secondary data from previous research are used as data source. Case studies are discussed to clarify the business use cases of BI systems and their impact on managerial DM. Theoretical foundations are stated basis a thorough literature review of the available body of knowledge. The current environment demands data-driven DM in an organisation at all levels, i.e. strategic, tactical, and operational. Heterogeneous data sources add unlimited value to the decision support systems (DSSs). The BI systems have become an integral part of the technology landscape and an essential element in managerial DM. Contemporary businesses have deployed BI systems in all the functions.

Online learning can present challenges and barriers for students, especially when it comes to self-motivation and discipline. Non-traditional learners and those who may be underprepared are often the students most likely to seek virtual learning options. As a result, methods of supporting online learners must be intentional and robust to stay attentive to students’ needs. The American Women’s College (TAWC) at Bay Path University designed its Social Online Universal Learning (SOUL) model to promote degree completion through a constellation of evidence-based practices that cultivate student engagement in a personalized online learning environment. SOUL employs an innovative adaptive technology approach with Universal Design for Learning (UDL) principles to promote accessibility and affordability. Foundational to these frameworks is a commitment to leveraging technology to gather data that drives action-oriented analytics, triggering interventions by faculty and staff and generating predictive models to inform wrap-around support. SOUL’s high-tech, high-touch attributes give students agency over their unique learning paths and provide instructors and administrators the meaningful insights needed to target efforts in a personalized yet scalable way, to promote and positively impact student success. Lessons learned in the process of developing data-driven “high-tech, high-touch” practices are presented.

Open data are characterized by a number of economic, environmental, technological, innovative, and social benefits. They are seen as a significant contributor to the city’s transformation into smart city. This is all the more so when the society is on the border of Society 5.0, that is, shift from the information society to a super smart society or society of imagination takes place. However, the question constantly asked by open data experts is, what are the key factors to be met and satisfied in order to achieve promised benefits? The current trend of openness suggests that the principle of openness should be followed not only by data but also research, education, software, standard, hardware, etc., it should become a philosophy to be followed at different levels, in different domains. This should ensure greater transparency, eliminating inequalities, promoting, and achieving sustainable development goals (SDGs). Therefore, many agendas (sustainable development strategies, action plans) now have openness as a prerequisite. This chapter deals with concepts of open (government) data and Society 5.0 pointing to their common objectives, providing some success stories of open data use in smart cities or transformation of cities toward smart cities, mapping them to the features of the Society 5.0. We believe that this trend develops a new form of society, which we refer to as “open data-driven society.” It forms a bridge from Society 4.0 to Society 5.0. This chapter attempts to identify the role of openness in promoting human-centric smart society, smart city, and smart living.

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.