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The shift from data-informed to data-driven educational policymaking is conceptually framed by institutional and transhumanist perspectives. Examples of the shift to large-scale quantitative data driving educational decision-making suggest that data-driven educational policy will not adjust for context to the degree as done by the data-informed or data-based policymaking. Instead, the algorithmization of educational decision-making is both increasingly realizable and necessary in light of the overwhelmingly big data on education produced annually around the world. Evidence suggests that the isomorphic shift from localized data and individual decision-making about education to large-scale assessment data has changed the nature of educational decision-making and national educational policy. Big data are increasingly legitimized in educational policy communities at national and international levels, which means that algorithms are assumed to be the best way to analyze and make decisions about large volumes of complex data. There is a conceptual concern, however, that decontextualized or de-humanized educational policies may have the effect of increasing student achievement, but not necessarily the translation of knowledge into economically, socially, or politically productive behavior.

Educational intelligence can be considered a prized asset in political actors’ careful calculations in setting policy agendas for radical educational transformations in the age of the Fourth Industrial Revolution characterized by Big Data, Artificial Intelligence (AI), machine learning, and the Internet of Things (IoT). As an agent of globalization, the European Union (EU) is uniquely positioned to steer the direction of this new wave of digital technologies for two cardinal objectives in the EU’s rhetorical discourse: social cohesion and economic prosperity. Conversely, its complex governance architecture, which restricts its role in educational policy, tempers its ability to drive policy reforms in education for the strategic and coordinated deployment of Big Data in educational systems to support those twin objectives. This chapter examines this burgeoning policy arena in the European Union by interrogating the most recent policies on the “data economy” enacted at the EU-level and the positionality of education in this newest wave of policy formulation. A content and discourse analysis of policy documents on Big Data reveals that the EU is launching multiple initiatives to regulate these novel technologies across its socio-economic sectors. However, the amorphous nature and unpredictable impact of these technologies, along with the jurisdictional barriers in the education sector stemming from the delimitation of governance layers in the EU, pose difficulties in generating a coordinated approach to policy implementation to engender tangible results. Hence, the contours of an educational intelligent economy in the EU needs considerable policy attention and technical resources in its transition from the current ideational stage to its concrete manifestation.

Central theme: The present chapter discusses the integration of data science methods in devising economic policies in different countries with special reference to India.

Purpose: It explains how the policy-making process in countries can be transformed from estimate-based policies to evidence-based policies with the help of techniques such as artificial intelligence (AI), big data, and data analytics. It answers the research question of whether the data science techniques can make the economic policy process efficient or not in developing countries like India.

Research methodology: Data are collected from secondary sources such as government websites, journals, corporate reports, and research databases to conduct this descriptive analysis. Research papers from Scopus/Web of Science (WoS) database are extracted, and exclusion/inclusion criteria are applied for extracting papers relevant to this research.

Findings: The chapter found out various opportunities which India can tap by gaining new insights on critical macroeconomic issues such as unemployment, labour markets, and water crises and would be able to resolve the problems with the help of predictive modelling. The findings exhibit the possibility of building models that could explain how to integrate data science techniques into the policy-making process. It also highlights the challenges that Indian economy is facing in incorporating these techniques in its policy-making process. It states the need to design different evaluation schemes based on information and communication technology (ICT) and data science for different policies, since one methodology does not suit all.

The context of this chapter is the use of data and advanced data analytics in a commercial setting. Privacy is considered as protection from vulnerability, whereby vulnerability is understood as the state of being exposed to the possibility of being harmed, either physically or emotionally, or in fundamental rights other than privacy. Therefore, privacy's policy instruments, in particular data protection law, could be seen as a means to reduce the risk of harm resulting from data use. Such harm is probabilistic and often uncertain, which, however, does not exclude analyzing costs and benefits of regulatory data protection policies. When balancing privacy protections and opportunities for knowledge gain, regulatory policy could be viewed as superior, when it expands the range of possible trade-offs between vulnerability protection and gaining socially beneficial knowledge.

The UK Coalition government introduced a raft of welfare reforms between 2010 and 2015. As part of its response to the financial crisis, reforms were designed to cut public expenditure on social security and enhance work incentives. Policy makers are required by legislation to have due regard to the need to eliminate discrimination, advance equality of opportunity and foster good relations between different people. This Public Sector Equality Duty is an evidence-based duty which requires public authorities to assess the likely effects of policy on vulnerable groups. This chapter explores the extent to which the Department for Work and Pensions adequately assessed the equality impacts of key welfare reforms when policy was being formulated. The chapter focuses on the assessment of the impact of reductions to welfare benefits on individuals with protected characteristics – age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion and belief, sex and sexual orientation – including individual and cumulative impacts. It also considers mitigating actions to offset negative impacts and how the collection of evidence on equality impacts was used when formulating policy. The chapter shows that the impacts of the reforms were only systematically assessed by age and gender, and, where data were available, by disability and ethnicity with no attempt to gauge cumulative impacts. There is also evidence of Equality Impact Assessments finding a disproportionate impact on individuals with protected characteristics where no mitigating action was taken.

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.