Implementing big data strategies: A managerial perspective

doi:10.1016/j.bushor.2019.02.001

Business Horizons

Volume 62, Issue 3, May–June 2019, Pages 347-358

https://doi.org/10.1016/j.bushor.2019.02.001 Get rights and content

Abstract

Despite considerable recent advances in big data analytics, there is substantial evidence that many organizations have failed to incorporate them effectively in their own decision-making processes. Advancing the existing understandings, this article lays out the steps necessary to implement big data strategies successfully. To this end, we first explain how the big data analytics cycle can provide useful insights into the characteristics of the environments in which many organizations operate. Next, we review some common challenges faced by many organizations in their uses of big data analytics and offer specific recommendations for mitigating them. Among these recommendations, which are rooted in the findings of strategy implementation research, we emphasize managerial responsibilities in providing continued commitment and support, the effective communication and coordination of efforts, and the development of big data knowledge and expertise. Finally, in order to help managers obtain a fundamental knowledge of big data analytics, we provide an easy-to-understand explanation of important big data algorithms and illustrate their successful applications through a number of real-life examples.

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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Implementing big data strategies: A managerial perspective

Abstract

Section snippets

The big data rush

Big data and big data dreams

High failure rates of the big data strategies: What now?

Big data analytics cycle

Barriers to effective implementation of big data strategies

Big data strategy implementation: Managerial responsibilities

Fundamentals of big data analytics tools and applications

Concluding remarks

Business Horizons

Business Horizons

Information and Management

Organizational Dynamics

Business Horizons

Journal of Business Research

Organizational Dynamics

Journal of Business Research

European Journal of Operational Research

Conceptual model development of big data analytics implementation assessment effect on decision-making

International Journal of Interactive Multimedia and Artificial Intelligence

The case for the chief data officer: Recasting the C-suite to leverage your most valuable asset

Business intelligence and analytics: From big data to big impact

MIS Quarterly

Data scientist

Harvard Business Review

Does decision process matter? A study of strategic decision-making effectiveness

Academy of Management Journal

Big data raises big questions

Government Technology

Where do capabilities come from and how do they matter? A study in the software services industry

Strategic Management Journal

The comprehensiveness of strategic decision processes: Extension, observations, future directions

Academy of Management Journal