I am delighted to present the third Journal of Information Technology Case and Application Research (JITCAR) issue of 2016. This is a special issue on the theme of Big Data Analytics. Its co-editors are Prasad Padmanabhan and Ajaya K. Swain. The contents of this issue are: an Editorial Preface article by Tahir Hameed; a Research Article by Swain; another Research Article co-authored by Shafaq Khan, Mathew Nicho, and Haifa Takruri; an Expert Opinion report by Swain; and a Book Review by Salvatore Parise. Summary information of these items is as follows.
In his Editorial Preface article titled “Impact of big data analytics on individuals and the South Korean big data analytics market,” Tahir Hameed focuses on three areas of big data analytics (BDA) that have an impact on individuals: (a) work productivity and performance, (b) lifestyle management, and (c) learning and development. Personal analytics apps generally provide descriptive analytics (colleting, processing, and presenting information to users). Predictive and prescriptive analytics are in the decision-making domains to help users alter their future strategies and behavior. Issues that hinder the adoption and use of BDA at the individual level (personal analytics) include individual competency level to effectively use smart devices and systems, aesthetics and ease of use of devices, and concern for individual privacy. The second section of this article provides the current status of the BDA market in South Korea. The BDA market is increasingly gathering momentum in South Korea, particularly in the areas of citizen services, transportation, healthcare services, and commerce. Public policy is still an important driver for commercialization of new technologies like BDA and for general market development in South Korea.
The first research article authored by Ajaya K. Swain has been totally managed by me to avoid any conflict of interest, with double blind reviews and recommendations from an associate editor. It is titled “Mining big data to support decision making in healthcare.” This study identifies key demographic and lifestyle characteristics associated with adult obesity by utilizing data mining of available healthcare big data from the Centers for Disease Control and Prevention (CDC) and the SAS Enterprise Miner. Two predictive models are used to identify key factors associated with adult obesity in the United States. In general, the findings of this study confirm existing research findings related to the association between obesity and key demographic variables, marital status, gender, education, age, and other behavioral factors such as smoking and exercise. The findings of this study have useful implications for healthcare professionals, government policy makers, individuals, and academics interested in using big data sets for targeted analysis.
The second research article is titled “IT controls in the public cloud: Success factors for allocation of roles and responsibilities.” This article is not related to the theme of the special issue. This research provides a set of success factors that can assist IT managers to allocate the roles and responsibilities of IT controls appropriately to staff to manage the migrated IT resources migrated to the cloud. A set of success factors generated from behavioral and information systems (IS) literature were verified using in-depth interviews of executives working in the United Arab Emirates (UAE). This research found that the role allocation is driven predominantly by people’s skills, competencies, organizational strategy, structures, and policies. This research also concluded that the most significant competency and skill for a person allocated to IT controls is to be able to evaluate and manage a public cloud service provider in terms of risks, compliance, and security issues. This study offers new insights for practitioners involved in assigning responsibilities. It also provides extensions for IT governance (ITG) framework authorities to align their guidelines to the emerging cloud technology.
The Expert Opinion report documents an interview by Ajaya K. Swain with Bipin Chadha, data scientist for United Services Automobile Association (USAA), a Texas-based Fortune 500 diversified financial services group of companies. Chadha is responsible for applying advanced analytics, machine learning, and operations research techniques to solve business problems. Earlier, he was vice president (solutions development) for NuTech Solutions, where he developed several decision support and war gaming models using system dynamics, agent-based modeling, swarm algorithms, advanced analytics, and optimization techniques. These models were used by Fortune 500 and Department of Defense (DoD) clients to make strategic decisions in the face of uncertain market, geo-political, and regulatory environments. He has published over 50 articles in international journals and conferences. Questions posed to Chadha include (a) This is the age of big data and “big data” has been a popular phrase in recent times. However, there is a feeling that it is too esoteric and there is no single agreed definition of big data. What is big data? Could you please highlight a few key features of big data? (b) What are the major risks and challenges with big data, particularly to individual rights, privacy, identity, and security? (c) Do you feel that “big data” is “really a big deal” as far as businesses are concerned? (d) How do you relate Business Intelligence to big data? (e) USAA has learned to be a data-driven organization. How does big data promote fast and fact-based decision-making in USAA? (f) USAA has done a lot with big data to understand its customers better, to understand the implications and effects of its advertising campaign. Please explain how USAA has been able to extract business value from big data. (g) You mentioned the term “big data analytics.” What does big data analytics refer to? (h) USAA has established a brand identity around superb customer service, affording it some of the highest customer satisfaction ratings on an annual basis, earning accolades from Forrester Research. Could you please explain how much big data analytics has contributed to this success? (i) Earlier we talked about several challenges in dealing with big data. There is a lot of concern about privacy and security of big data. Recent instances of data breeches have substantiated those concerns. The readers would be interested to know about a few specific technology platforms and networks that help USAA maintain the secured identity and privacy of its customers. (j) A recently published article in Harvard Business Review reported that USAA, the nation’s 6th largest consumer property and casualty insurer, has reinvented the process of marketing, finance, and data analytics working together, starting with a first-ever partnership between the chief marketing officer, chief finance officer, and chief data analytics officer. Having been in the area of data analytics, what are your reactions to and impressions on such a partnership and its impact on organizational performance and culture? (k) You mentioned the importance of models to provide value to the customers. Could you please elaborate on a few models/analytics technologies and their vendors that can help companies learn more about their customer preferences and needs so that they can offer relevant products? (l) Could you name a few other companies in your industry that have gained using big data? In your opinion, what other industries could gain most from big data? (m) What major trends and advances do you foresee in the area of big data and big data analytics in general?
The book review report by Salvator Parise provides a detailed critique of the 2014 book titled Big Data: A Revolution That Will Transform How We Live, Work, and Think authored by Vikto Mayer-Schonberger and Kenneth Cukier and published by Eamon Dolan/Mariner Books.
Ten chapters in the book are titled in a novel fashion: Now, More, Messy, Correlation, Datafication, Value, Implications, Risk, Control, and Next. Excerpts from the review are provided below.
The book is aimed at organizational leaders and practitioners who want to understand what big data is and how it is shaping data policies, approaches and analytical decision-making across many organizations. The book is not “technical” as there is very little detail in terms of analytical algorithms and tools. However, it is rich with examples describing big data solutions and their impacts across several sectors.
[It] is a fine book in describing the big data analytics movement and how it is impacting organizational and business decision-makers, including value propositions and risks and limitations. Getting leaders and decision-makers to be more analytical often requires changes to behavior. Therefore, coverage of big data implementation issues and adoption strategies in this book would have been helpful to practitioners. …[T]here could have been more discussion of some of the different big data algorithms and use case examples on when they might be most appropriate. Finally, this field is continuously being updated with new technologies and practices, so future editions of the book might be written that will include emerging big data technologies such as IoT, digital assistants, cognitive learning, and self-analytics.
Taylor & Francis, in line with the ongoing trends, has published this journal online since 2014. The website to find all JITCAR issues is: http://www.tandfonline.com/utca. All authors and reviewers are encouraged to start using the publisher’s online manuscript submission and reviewing system at http://www.editorialmanager.com/jitcar/. I urge all our current and future authors and reviewers to familiarize themselves with this system.
I hope you will enjoy reading all the items in this issue.