Creating value from big data
Topics: Organizational agility Data & Business analytics By Leif Jarle Gressgård
Big data is heralded as the next big thing for organizations to gain a competitive edge [1], and it has become the focus of academic and corporate investigation due to its high operational and strategic potential [2]. However, many big data projects provide disappointing results [3]. It is therefore important to understand the mechanisms and processes through which big data can add value to organizations [4].
Big data on its own is unlikely to be a source of competitive advantage, since all organizations can collect large volumes of data from a variety of sources [1]. Similarly, investments in big data analytics (BDA) alone are unlikely to create superior BDA capabilities. Recent studies have noted that many organizations fail to capture value from their big data investments, and some even argue that big data may hurt rather than help organizations [5]. Yet, analytics continues to be a top investment priority [6]. This has led some researchers to anticipate a “big data productivity paradox” [1], referring to the productivity paradox of information technology – i.e. the failure to establish a positive relationship between IT investments and firm productivity – that has been the focus of many studies the last decades.
Research seeking to resolve the productivity paradox has shown that there is a set of organizational characteristics and resources that help realize the value of IT investments. This can also be expected for realization of the big data value potential. Attention should therefore be paid to organizational changes big data entails and how technological aspects should be leveraged strategically [4]. In this regard, research literature argues that to leverage big data analytics and realize performance gains, organizations must develop strong big data analytics capabilities [5].
Several studies show that strong big data analytics capabilities can improve performance by enhancing organizational agility. Ensuring a high fit between data analytics tools, people, and tasks, is in this regard important [7]. This may have implications for organizations regarding selection of analytic tools, competence development (e.g. training programs), recruitment of personnel, and task design and allocation.
Torres et al. [8] examine the relationship between business intelligence & analytics (BI&A) and firm performance. They find that data quality, system quality and management practices influence organizations’ abilities to identify opportunities for improved efficiency and effectiveness, sense the need to enhance the way the business works and make organizational changes, sense opportunities and threats, identify inefficiencies in existing business processes, and foresee a wide range of actionable options based on its surroundings. Organizations should therefore emphasize management process and practices that encourage and promotes the use of BI&A within the organization, as well as standardization of BI&A development processes and user satisfaction. They should also focus on building BI&A solutions that are characterized by:
- high data quality (i.e. data that are accurate, comprehensive, correct, and consistent)
- high system quality (i.e. solutions that are flexible/adjustable to new demands and conditions, integrate data from systems servicing different functional areas, versatile in addressing data needs as they arise, and effectively integrate data from a variety of data sources within the organization).
Mikalef et al. [9] find that big data and analytics capabilities (BDAC) can strengthen organizations’ abilities to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments. To achieve this, organizations need to recruit people with good technical and managerial understanding of big data and analytics, foster a culture of organizational learning, and embed big-data decision-making into the fabric of the organization. Developing BDAC requires top management commitment and a clear plan for firm-wide big data analytics adoption and diffusion. Regarding development of managerial and technical skills, it is important to create cross-domain understanding. BDA mangers should understand the business need of other functional managers, suppliers, and customers to determine opportunities that big data might bring. Development of abilities of BDA mangers to coordinate big data-related activities in ways that support other functional managers, suppliers, and customers, is also important.
Wamba et al. [10] present similar advice. Their study shows that three BDA capability components (infrastructure flexibility, management capabilities, and personnel expertise) influence the abilities of organizations to develop or acquire required competences to change its existing business processes. The analysis shows that the most important factor is personnel expertise capability, which leads the authors to conclude that an organized effort must be made to build technical knowledge, technological management knowledge, business knowledge and relational knowledge related to BDA.
References / sources
- Toward the development of a big data analytics capability.
Gupta, M. & George, J.F. (2016). Information & Management, 53(8), 1049-1064. - How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study.
Wamba, S.F., Akter, S., Edwards, A., Chopin, G. & Gnanzou, D. (2015). International Journal of Production Economics, 165, 234-246. - Creating strategic business value from big data analytics: A research framework.
Grover, V., Chiang, R.H.L., Liang, T-P. & Zhang, D. (2018). Journal of Management Information Systems, 35(2), 388-423. - Big data analytics capabilities: a systematic literature review and research agenda.
Mikalef, P., Pappas, I.O., Krogstie, J. & Giannakos, M. (2018). Information Systems and e-Business Management, 16, 547-578. - Big data analytics and firm performance: Findings from a mixed-method approach.
Mikalef, P., Boura, M., Lekakos, G. & Krogstie, J. (2019). Journal of Business Research, 98, 261-276. - The 2019 SIM IT issues and trends study.
Kappelman, L., Johnson, V.L., Maurer, C., Guerra, K., McLean, E., Torres, R., Snyder, M. & Kim, K. (2020). MIS Quarterly Executive, 19(1). - Increasing firm agility through the use of data analytics: The role of fit.
Ghasemaghaei, M., Hassanein, K. & Turel, O. (2017). Decision Support Systems, 101, 95-105. - Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective.
Torres, R., Sidorova, A. & Jones, M.C. (2018). Information & Management, 55, 822-839. - Big data analytics capabilities and innovation: The mediating role of dynamic capabilities and moderating effect of the environment.
Mikalef, P., Boura, M., Lekakos, G. & Krogstie, J. (2019). British Journal of Management, 30, 272-298. - Big data analytics and firm performance: Effects of dynamic capabilities.
Wamba, S.F., Gunasekaran, A., Akter, S., Ren, S.J., Dubey, R. & Childe, S.J. (2017). Journal of Business Research, 70, 356-365.
Other relevant external resources
When data creates competitive advantage. Article by Andrei Hagiu and Julian Wright, Harvard Business Review.
The new leadership mindset for data & analytics. MIT Sloan Management Review Executive Guide.
Becoming a data-driven organization. MIT Sloan Management Review, case study series.
Philip Evans talks about the relationship between strategy, data and technology. See also article From deconstruction to big data: How technology is reshaping the corporation from the book Reinventing the Company in the Digital Age.