**1. Introduction**

Although its definition continues to change as the technological ecosystem changes, big data can be referred to as "data that is so large, fast or complex that it's difficult or impossible to process using traditional methods" [1] (para. 1). Famed data analytics guru Doug Laney conceptualized big data into three Vs: volume, velocity, and variety. Volume refers to how organizations gather data from a variety of sources, including computers, smart devices, cameras, social media platforms, and many others. In education settings, educators often gather data from student interactions with curricular materials across many of these sources, not to mention the data gathered by educational leaders at the school, district, region, or national level. The second V, velocity, refers to the growth of the Internet's integration with everyday devices and processes, such as e-books with embedded Internet resources. The velocity of big data requires organizations to be nimble and flexible, as data can be captured—or lost—at unprecedented speed, if the organization has adequate data collection and storage capacity. Finally, the third V—variety—implies that data can come in many different formats, from traditional databases of information in columns and rows to highly disorganized and unstructured data [2], such as multimedia, global positioning system (GPS) information, or Microsoft PowerPoint files. This variety places educational organizations in difficult positions, as educational organizations are resistant to change given their bureaucratic nature, with many organizations only able to analyze more traditional data in traditional ways.

Despite the traditional nature of education, the era of big data has arrived in the field of education on a global scale. As the Internet became widely available to educational organizations in the 1990s and online education has exploded in growth and popularity since 2000, many educational leaders and policymakers now have access to more data than ever before [3]. As a result, both governments and educational organizations have made considerable efforts to use large datasets to make educational decisions, including those

**Citation:** Taylor, Z.W.; Charran, C.; Childs, J. Using Big Data for Educational Decisions: Lessons from the Literature for Developing Nations. *Educ. Sci.* **2023**, *13*, 439. https://doi.org/10.3390/ educsci13050439

Academic Editor: Han Reichgelt

Received: 20 February 2023 Revised: 17 April 2023 Accepted: 22 April 2023 Published: 25 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

related to curriculum and instruction, program development, policy advocating, resource allocation, and countless other educational decisions [4–7].

However, as data is continuously created, collected, and analyzed by educational researchers, those collecting it may reach a point of diminishing returns: How much data is too much? And in an era where nearly everything can be observed and digitally documented, when do educators reach a point of data exhaustion and overload? In a discussion of big data in business circles, data analysts often say you cannot manage what you do not measure [8], but surely the inverse is also true: You cannot measure what you cannot manage. Educational leaders and policymakers face this challenge in many aspects of their operations, as many educational systems in developing nations may be in their nascent stages of conceptualizing data collection, much less engaging with big data analytics.

Moreover, the utility of big data requires both a technical expertise and a level of communication that many bureaucratic educational organizations simply may not possess, especially in under-resourced developing nations. Here, educational organizations in developing nations are placed in an interesting position, as data is now more available than ever from a wide variety of sources and stakeholders, and this data could prove transformative in the efforts that organizations make to become more efficient and effective for their students. However, developing nations with limited human and financial capital, complex bureaucratic organizations, and limited technical capacity may need to catch up to the speed in which big data has advanced and will continue to do so.

Additionally, the arrival of neoliberal policies and agendas in many developing nations has placed educational organizations in difficult positions regarding the country- and local-level allocation of resources, both human and financial. Core tenets of neoliberalism including the privatization of public functions, the deregulation of industry, and reductions in spending on public initiatives—has been felt in educational contexts within developed and developing nations. As a result, many developing nations may have limited human and financial resources than their peers, with this stratification and inequity exacerbated by neoliberal policies enacted by national or local governments.

From here, this discourse provides an overview of how educational organizations have strategically utilized and benefited from data-driven decision-making using large educational data sets. Additionally, this review will outline several drawbacks and ethical concerns of using big data sets in education, including how uncertainties in human and financial capacity as well as limited technological capability may hinder developing nations who desire big data to make decisions but are not in the position to do so. Furthermore, accountability systems at the national, state, and local levels within developed nations have become more technologically advanced as data continues to become increasingly available and abundant. Subsequently, educational leaders and policymakers from developing nations must understand how big data sets have been used in the past and how these leaders can develop the organizational capacity to use the data to improve the lives of students and the communities to which they belong.
