Implementing big data strategies: A managerial perspective
Section snippets
The big data rush
After James W. Marshal discovered gold nuggets in the Sacramento Valley in 1848, thousands of prospective gold miners traveled by land and sea to California to seek their fortunes from large quantities of gold hidden in the riverbeds (“California Gold Rush,” 2010). At the time, the process of mining gold (i.e., prospecting activities and extraction of gold from its ore) was laborious and not all of the fortune seekers were successful in their search for a road to quick riches (Rohrbough, 1998).
Big data and big data dreams
Big data refers to the large and complex data assets that require cost-effective management and analysis for extraction of insights from them (Gupta & George, 2016). Four specific features, also known as the 4Vs, characterize big data (Kietzmann, Paschen, & Treen, 2018; Sivarajah, Kamal, Irani, & Weerakkody, 2017):
- 1.
Volume refers to the large scale of big data, which requires innovative tools for their collection, storage, and analysis.
- 2.
Velocity refers to the rate at which the data are generated
High failure rates of the big data strategies: What now?
In spite of the popularity of big data analytics as a game changer in revolutionizing the way organizations make decisions and operate, surveys show that around 80% of businesses have failed to implement their big data strategies successfully (Asay, 2017, Gartner, 2015). More than 65% of organizations have reported below average returns on their data management investments (Baldwin, 2015). While many organizations have jumped on the bandwagon to take advantage of big data opportunities (
Big data analytics cycle
The goal of big data analytics is to enhance organizational decision making and decision execution processes. Informed decision making is one of the building blocks of organizational success, and the importance of comprehensive analysis of information before making operational and strategic decisions has been highlighted in the works of many organizational researchers and practitioners (e.g., Dean and Sharfman, 1996, Fredrickson, 1984). In making important decisions, managers collect data,
Barriers to effective implementation of big data strategies
Academics and practitioners have enumerated a long list of barriers to the full realization of big data benefits in organizations. Here, we summarize our review of the literature by discussing the technological and cultural barriers as two major categories of common constraints faced by many organizations (Alharthi, Krotov, & Bowman, 2017; LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011).
Big data strategy implementation: Managerial responsibilities
Implementation of business strategies is a complicated process and most of the formulated strategies cannot be executed effectively (Hrebiniak, 2006). When it comes to big data strategies, the implementation process is even more complicated due to the aforementioned technological and cultural challenges specific to this area. Key organizational decision makers play a central role in the success or failure of big data initiatives and are responsible for creating a unified vision regarding the
Fundamentals of big data analytics tools and applications
To present a brief and yet comprehensive account of big data analytics techniques, we classify these techniques into two broad categories: descriptive and prescriptive analytics tools (Sivarajah et al., 2017). For each category, we introduce several important artificial intelligence-based algorithms to clarify their applications. Table 3 summarizes the applications for each of the algorithms and provides specific real-world examples for each of them.
Concluding remarks
Recent developments in the field of big data analytics have generated a new gold rush for extracting business value from big data. While success during the California gold rush was mainly a result of luck (e.g., having access to the right parcel of land), the realization of big data dreams is much more a matter of successful implementation. In this regard, after discussing the many challenges faced by big data dreamers, we showed that the road to riches passes through effective implementation
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