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Recently, as the use of the Internet has grown, so too has an interest in the use of data gathered and analyzed by arriving at a more digitized and technologically connected society (George et al., 2014; Lokshina et al., 2017). In the 1998 film “Enemy of the State”, a rogue government agency is shown as having unlimited access to private data from a variety of data sources. At that point, the scenario was so disturbing to the Federal Bureau of Investigation (FBI) that a public relations campaign was launched to assure society that the plot was pure fiction (Miller, 2013).
Nevertheless, as recent exposures about data privacy have made clear, the surveillance shown in the film is very real at this time (Jennex, 2017; Nunan & Di Domenico, 2017). What has changed is the role of governments as collectors and users of data, and the increasing importance of commercial entities as the drivers of both data gathering and analysis, i.e. data analytics (Committee on Commerce, Science, and Transportation, 2020; Marr, 2015; Molok et al., 2012; Sedayao & Bhardwaj, 2014; Zaslavsky et al., 2012).
The COVID-19 emergency has taken place in an already digital society. The amount of pandemic-related data gathered and processed globally has been enormous.
Additionally, advanced computational models, some based on machine learning, have shown tremendous potential in tracing sources or predicting the future spread of COVID-19, necessary for the planning of resources. Without correct data and models, the risk to public health cannot be assessed correctly, causing a problem that the authorities cannot allocate time, assets, and resources appropriately, leading to both penury, and oversupply, and waste.
Therefore, it is essential to leverage Big Data technology and intelligent analytics and put them to effective use for the benefit of public health. Reliance on digital data sources has been of great value in outbreaks caused by new pathogens significantly improving data collection, however, precise data is still rare and hence forecasts are still less reliable and effective (Scarpino & Petri, 2019).
The needs and conditions for responsible data gathering and analysis at a global scale must be clear as Big Data technology has become critical for managing the COVID-19 pandemic in a digital society. The use of data that has been collected from digital sources for prediction and surveillance is very important in the fight against the COVID-19 pandemic, but it is equally important to use this data in compliance with data protection regulations and with due respect for privacy and confidentiality and recognizing its possibly limited validity or bias.
Previous generations of information technology were dominated by technology companies with commercial strategies based on their expertise in hardware or software. Currently, many leading Internet companies have commercial strategies built around the collection and analysis of data.
For datacentric business organizations like Google, Facebook, and others, the collection of data has become a target instead of a way to achieve any additional business goals. This comes at a point when there is a growing interest from researchers in wide-ranging ethical implications of the increasing use of data in different areas, including data privacy (Jennex, 2017; Hong & Thong, 2013; Lokshina et al., 2019a; Lu et al., 2014; Nunan & Di Domenico, 2017); cyber-hacking (Bambauer, 2014; Jain et al., 2016; Perera et al., 2015; Zaslavsky et al., 2012); government regulation (Committee on Commerce, Science, and Transportation, 2020; Fink et al., 2012; Lokshina et al., 2019b); and intellectual property (Bateman et al., 2013; Jennex, 2017; Marr, 2015).
Besides, there are concerns about the level of government surveillance of commercial social networks, for instance, revealed due to leaks by Edward Snowden (Witte, 2013). In these circumstances, the term “Big Data” has obtained popularity in both business and public policy circles as summarizing industrial and commercial aspects of these state-of-the-art data gathering and analysis processes which involve also private data collection (Marr, 2015; Nunan & Di Domenico, 2017; Perera et al., 2015; Sedayao & Bhardwaj, 2014; Zaslavsky et al., 2012).
Until now, there has been much discussion of Big Data technology focused on its potential positive effects for both business and society (George et al., 2014). This extends beyond improvements to commercial efficiency and expands to claims about its transformational effect on such areas as healthcare and delivery of public services (Manyika et al., 2011; Lokshina et al., 2017).