*3.1. Size and Overwhelm Paralysis*

Even though educators should be able to make better decisions with more data, two of the three vs. of big data—namely velocity and volume—pose challenges for educational organizations, especially in developing nations that do not possess the human and financial capacity to handle the velocity and volume of data. Sagiroglu and Sinanc argued that big data implementations need to be planned carefully and with an eye toward growth, as humans have generated more digital data since 2010 than ever existed in the thousands of years previous [19]. The authors cautioned that the size of the data can be confusing, and the technical expertise of staff can be limiting, leading to a sense of overwhelm paralysis. This results in a wealth of data collection but little analysis, and without any aim towards decision-making and practicable outcomes [19]. Additionally, Sagiroglu and Sinanc asserted that organizations must have the capacity to store data in the first place and the ability to organize that data in a way where multiple stakeholders can access and interpret the data accurately. In developing nations, there may not be the physical or cloud storage capacity to gather and analyze data in a timely manner, positioning these nations in a perpetual deficit state [19].

Nazarenko and Khronusova echoed many of Sagiroglu and Sinanc's concerns, suggesting that educational organizations must prepare years or decades in advance to support the type of data storage that is necessary for big data decision-making [15]. The authors also explained that without clear goals and educational outcomes, many under-resourced schools and educational organizations will struggle with understanding what data to gather, where to gather it from, how much to gather, and when data collection stops and data analysis starts [15]. Moreover, Nazarenko and Khronusova explained that it is increasingly common to be in a perpetual state of data collection without the expertise to analyze it. Educational organizations often experience difficulties when recruiting and retaining high-quality data analysts who are technically trained to analyze millions or billions of data points across many different data types and formats [15]. As a result, organizations may realize a sense of overwhelm paralysis with regard to the volume of data, the velocity of data, and the staff and planning to execute their goals. Additionally, without well-defined goals, many educational organizations may gather data that does not serve the mission or vision of the organization or does not substantially inform how educational leaders can improve the organization [15,19].
