Effect of organizational characteristics and technical competence on Big Data Implementation in Organizations

Importance, Key Contribution and Theoretical Base “We’re really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government.... [ view full abstract ]

Importance, Key Contribution and Theoretical Base
“We’re really just getting under way. But the march of quantification, made possible by enormous new sources of data, will sweep through academia, business and government. There is no area that is going to be untouched.” Gary King, Director Harvard Institute for Quantitative Social Science

Miller (2014) argues that data is quickly becoming a strategic business asset. He further argues that data will impact the full business spectrum and is not just about IT and technology. “Job spanning the entire business spectrum, including legal, sales, marketing, finance, product development, manufacturing, and operations, will be impacted by the big data phenomenon” (Miller 2014). Leaning on literature, Laster 2010; Miller 2010; Parry 2010; Vaidhyanathan, 2010, all argue that every profession, whether business or technical, will be impacted by Big Data. Galbraith (2014) also maintains the legitimacy of Big Data by citing reports from the World Economic Forum, the McKinsey Global Institute, and The Economist Intelligence Unit. In his article, Galbraith (2014), presents the case of how Nike has used Big Data to create a completely new business unit and summarizes the impact that Big Data has on organization design by using the Star Model ™ Framework with Big Data. Furthermore, Gartner Inc. through its research mentions “Through 2015, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage.” Big data, McAfee and Brynjolfsson (2012) write, is far more powerful than the analytics of the past. Executives can measure and therefore manage more precisely than ever before. They can make better predictions and smarter decisions. They can target more-effective interventions in areas that so far have been dominated by gut and intuition rather than by data and rigor. More data now crosses the Internet every second than was stored in its entirety 20 years ago. Nearly real-time information makes it possible for a company to be much more dynamic and responsive than its competitors.

Why the sudden hype and focus on Big Data? Well, for a few reasons. First, and as Galbraith (2014) notes, this data is unstructured and is different from the usual structured datasets that we have shared across and within organizations. It is different than the columns and rows we are used to visualizing and sharing. Second, and also as Galbraith (2014) notes, this data is available in real time. Third, the first two aspects of Big Data open up business opportunities that did not exist before – as the case of Nike’s new service offerings or the tweaking and decision-making by gathering sensor feedback from car and driver performance in Formula 1 races (Munford 2014). Lastly, current analytical or database management systems cannot manage and provide insights with the variety and the volume of data gathered.

This is where I believe OD has a role to play. OD is concerned with organizational effectiveness/value. We are trying to get the organization from one instance to another through research, data collection, diagnosis and action due to changes as they occur in the world. Our goal is always with improving effectiveness or value of the organization. The arguments in our favor include:
(a) Our methods, tools and models require us collect data both structured and non-structured from multiple sources and vantage points. This is similar to how Big Data is formed from multiple sources. Since the beginning, dating back to Fredrick Taylor’s work with organizations, the effort revolved around collecting quantitative (structured) and qualitative (non-structured) data. Weisbord (2012) talks about how Frederick Taylor in the early 1900s did consulting engagements with organizations helping to provide efficiency by studying not only the solid quantitative evidence for improvement but also the human interaction. Church and Dutta (2013) capture this piece perfectly noting the singular role that data (and feedback) play in creating energy for change. The use of data, (whether quantitative or qualitative) as a catalyst for action is one of the most unique contributions that OD makes to organizational transformation. Furthermore, the evidences to partake in a change was derived in early OD was as much based on structured data, such as error rates, cost, profit as unstructured data that referenced human interaction, behavioral knowledge and organizational culture (qualitative). Similarly, Pentland (2014) mentions “the use of large-scale data to predict human behavior is gaining currency in business and government policy practice, as well as in scientific domains where the physical and social sciences converge”.
(b) The second argument in OD’s favor deals with the insights, analytics and meaning that is derived from such data that exists and/or can change in near real-time when working on a change effort from the current state to a future ideal state. The current issue is that tools and expertise is not available to utilize meaningful data insights.
(c) Finally, the last argument in favor, as Church and Dutta (2013) mention, deals with the action that is tied once data and realization is made. The action is derived from what we gathered, analyzed and given as feedback.

Church and Dutta (2013) provide a conceptual framework for how different levels of data analytic methods and capabilities commonly found in OD and I-O Psychology can be linked together with Big Data applications. The role of Big Data as an approach to data- driven change within the broader context of OD is clearly indicated as being at the highest (and most expansive) level of the framework.

Contribution to literature & Gap
The fact of the matter is that organizations have been slow to get on the bandwagon and start utilizing Big Data. There are some really good reasons for the slow start, namely: (a) this is going to be change in how organizations conduct current business and operations – their current value proposition may come at stake, (b) the resources and expertise are not readily available to embrace this change and (c) there is no framework, governance or method to go about implementing such a program. Though “big data” has now become commonplace as a business term, there is very little published management scholarship that tackles the challenges of using such tools—or, better yet, that explores the promise and opportunities for new theories and practices that big data might bring about (Gerrard, Haas, Pentland 2014). Church and Dutta (2013) further note, “Despite the potential inherent in Big Data driven OD applications to deliver entirely new types of insights for organizations, there are some potential barriers to using this approach as well. These consist of the three different issues: capability, mindset, and ethics.” Moreover, the realm of big data-sharing agreements remains informal, poorly structured, manually enforced, and linked to isolated transactions (Koutroumpis & Leiponen, 2013). This acts as a significant barrier to the market in data—especially for social science and management research (Gerrard, Haas, Pentland 2014). Church and Dutta (2013) reference “there is no singular Big Data theory, methodology, or value structure” and “just about anyone can enter the consulting space and engage in Big Data mining activity”. It is imperative as scholar-practitioners that we engage in and discuss/debate the implications of such with governance and oversight.

Research Questions & method
Given the above, the purpose of this paper, therefore, is to define, look at criteria and establish relationship (organizational characteristics, technical characteristics) for Organizations to realize value from Big Data implementations/governance.

I will be pursuing a purely quantitative methodology. I will explore organizational characteristics such as: size, information intensity, leadership attitudes, willingness to experiment, risk capacity, organizational structure, adaptability, competitiveness of environment, industry and collaboration. I will explore technical competence defined as the culmination of the ability to implement Big Data projects/programs which include: technical expertise and formal knowledge base

Implications

Once I have the data collected, I should be able to shed light on how businesses can be re-imagined and the role of ethics will evolve with the integration of Big Data in organizations and our current economic climate.

References
Boyd, D. & Crawford, K. (2012). “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon.”Information, Communication, & Society 15:5, p. 662-679.
Church, A., Dutta S. (2013). The Promise of Big Data for OD. OD Practitioner. 45(4).
Galbraith, J., (2014). Organization Design Challenges resulting from Big Data. Journal of Organization Design. 3(1). 2-13.
Galbraith, J., Downey, D., Kates, A. (2002). Designing Dynamic Organizations: A Hands-On Guide for Leaders at All Levels. New York: AMACOM.
George, G., Haas M. R., Pentland, A. (2014). Big Data and Management. Academy of Management Journal. 57(1).
Koutroumpis, P., & Leiponen, A. 2013. Understanding the value of (big) data. In Proceedings of 2013 IEEE international conference on big data. 38–42. Silicon Valley, CA, October 6–9, 2013. Los Alamitos, CA: IEEE Computer Society Press
Malik, P. (2013). Governing big data: Principles and practices. IBM Journal of Research and Development, 57(3), 1-13. doi:10.1147/JRD.2013.2241359
McAfee, A., Brynjolfsson, E., (2012). Big Data: The Management Revolution. Harvard Business Review. 90.
Miller, H. G., & Mork, P. (2013). From data to decisions: A value chain for big data. IEEE IT Pro, 15(1), 57-59. doi:10.1109/MITP.2013.11

Big Data Change agent Organizational characteristics Technical competence Organizational Development OD Scholar-Practitioner [ view full abstract ]

Big Data
Change agent
Organizational characteristics
Technical competence
Organizational Development
OD Scholar-Practitioner