Article Preview
TopBig data (BD) has surfaced as a frontline in business for determining competitive advantage and utilizing opportunities (Frisk, 2017). Firms are collecting unprecedented amounts of data as they pursue enhanced business strategies to harness BD analytics that aid in marketing and functions like supply chain management (Zhong et al., 2016), operations management (Choi et al., 2018), finance (Fang & Zhang, 2016), human resource management (Zang & Ye, 2015), and the public sector (Desouza & Jacob, 2017). The extant literature recognizes BD as the “next management revolution” (McAfee & Brynjolfsson, 2012), as well as the “new raw material for business” (Cukier, 2010) or the “new science that holds the answers” (Gelsinger, 2012).
BD portrays large volumes of structured and unstructured data that shape the business ecosystem and the systematic processing of the immense amount of data for more robust decision-making and strategic initiatives. Organizations use big data marketing analytics (BDMA) to enhance marketing operations, deliver better customer service, generate tailored marketing campaigns, and perform activities that contribute to increased revenues and profitability (Ji-Fan Ren, 2017; Sharma et al., 2014). It can be claimed that BD, from marketing communication channels, such as social media, can deliver information paired with legacy consumer behaviour methods while offering supplementary benefits to marketers.
Despite these advantages, utilizing BD can be difficult. For example, more than a singular database may be required for marketing. Furthermore, commitment to customers, information privacy, and legal constraints must be considered when adopting BD analytics (Even et al., 2010).
BD is collected from different sources via automated means; therefore, the quality and usefulness of the data cannot be ensured. There are limitless chances of improper, erroneous data collected alongside valuable data during the collation of BD. Sustaining a good quality BD database or data warehouse necessitates extensive effort. Organizing a BD for analysis often requires more time than the analysis itself (Hofacker et al., 2016).
While BD is a valuable knowledge asset in marketing, high-quality data is required for companies to obtain competitive value and enhanced confidence in the output quality from big data analysis (BDA). Additionally, the users of BD will be able to produce more and concentrate on essential activities (Corte-Real et al., 2019). Research has indicated that the level of BD management impacts value creation (Chen et al., 2014). Despite the claims that BDA and data quality enhance business value, there is inadequate knowledge of the impact of quality dimensions on financial and marketing performance from the marketing perspective (Gupta & George, 2016; Wamba & Mishra, 2017).