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In recent years, there has been unprecedented growth in globally generated data. The rapid development of information technology, IoT, and data-driven organizations has accelerated the reliance on data for daily business operations (Parviainen et al., 2017; Sawadogo & Darmont, 2021). The advancement of information technology hastens the process of acquiring, storing, organizing, sharing, and visualizing data in diverse formats, which increases its volume, velocity, and variety (Cappa et al., 2021). In fact, data growth worldwide, which reached 33 zettabytes in 2018, is projected to reach 125 zettabytes in 2025, with an estimated 40% growth annually (Tang et al., 2020). Consequently, the increasing demand for systems, tools, capabilities, and storage to manage data has resulted in a projected global market value exceeding US$203 billion. This indicates the substantial growth in data that is reshaping the business landscape (Tao et al., 2019).
The prevalence of data sharing has significantly influenced how data is accessed and used, presenting both benefits and risks for organizations. The integrity and accuracy of data have become increasingly important as businesses rely on data for decision-making processes. However, the ease of large-scale data generation and sharing, primarily through social media and fake news, has raised concerns about its credibility and quality. Several studies highlight the prevalence of misinformation and data manipulation on social media platforms, severely compromising the accuracy of available information (Conroy et al., 2015; Nakov & Da San Martino, 2021). The emergence of the filter bubble effect, wherein information is selectively presented to suit specific objectives, further exacerbates the distortion of reality and the dissemination of biased content (Amrollahi, 2021).
Post-truth society has emerged in political sciences, referring to the widespread concern about disputes over public truth claims (Suiter, 2016). The term even received the Oxford Word of the Year status from Oxford University Press in the context of Brexit and the Trump election in 2016. The implications for data collection and management are profound. Therefore, the goal of this research is to explore the body of knowledge regarding data veracity in this context.
Businesses tend to outsource their data collection processes as a cost-cutting measure (Hamlen & Thuraisingham, 2013). While this approach provides financial benefits, it introduces uncertainty regarding the accuracy and quality of the data obtained from third-party vendors. Cohen (2017) highlighted the risk of inaccurate data when vendors cannot guarantee its accuracy, potentially resulting in flawed decision-making. In addition to accuracy concerns, sharing information with external parties poses significant risks to data security. Potential data breaches and unauthorized access can compromise the confidentiality and integrity of sensitive information (Hamlen & Thuraisingham, 2013; Pandey et al., 2020). These risks are compounded by the rapid spread of fake news and other forms of deceptive information, which distorts understanding and undermines the accuracy of data analytics. Thus, there is an increasing need to develop robust measures to combat data deception and fake news while navigating the complex, data-driven business world.
Recognizing proper data quality and management systems has changed our understanding of data management and its essential characteristics. Big data is characterized by increased data volume, velocity, and variety, necessitating data veracity to yield valuable insights (Geerts & O’Leary, 2022; Ghasemaghaei, 2021; Rubin & Lukoianova, 2014). The goal of veracity is to extract maximum value from data processing by maintaining the accuracy, credibility, and reliability of the data (Rubin & Lukoianova, 2014). Increasing and preserving data veracity can be achieved through different approaches, including fact-checking or veracity assessment tools via data processing, continuous monitoring of data flow, or experts who can assess valuable information from big data (Cappa et al., 2021; García Lozano et al., 2020).