Sunday, 17 January 2016
Location: South Pacific 1
This session on big data will address various topics in the telecommunications industry related to how companies can leverage (or be hindered) by this type of data. Oftentimes, corporations are challenged because they are unable to establish capabilities to leverage big data, including the analysis, capture, curation of data, internal and external search capabilities, sharing, storage, transfer, visualization, and information privacy. Research topics in this session will look at the use of predictive analytics and other certain advanced methods to extract value from big data.
Senior EVP & Chief Strategy Officer, Multinet Pakistan Private Limited
Professor, Information Systems, Graziadio School of Business and Management, Pepperdine University
Businesses are taking advantage of the benefits provided by virtualization and cloud computing to build flexible, agile, and cost-effective IT architecture. These elastic capabilities help companies compete by accelerating the delivery of internal and customer facing applications and services. Many companies are building enterprise architecture by combining legacy, multi-cloud and virtual environments. The critical security problem is that these new opportunities to align with business resulted in a co-mingled architecture which effectively eliminates a formal architectural perimeter. Based on interviews with boards of directors and executive leadership teams facing these new environments, we explored the question: How do we secure increasingly dynamic architecture amidst increasingly coordinated and sophisticated threats? Based on this qualitative analysis we describe a number of security, compliance, and risk-based approaches that are effectively being used. Critical approaches identified and presented include, a risk evaluation of new technologies analyzing how they are aligned to emerging threats and remediation. In addition, agile development, user behavioral analytics and automaton must be tied to a big data platform. Importantly, user-behavior analytics must be architected into all future design. In an environment without a perimeter, these solutions and lessons are critical for telecommunications companies, internet service providers, and public cloud hosting providers and decision-makers.
Associate Professor & Department Chair, Department of Interaction Science, Sungkyunkwan University
Republic of Korea
Big data is a data set (or a methodology) whose potential utility is not yet fully known for its size and it is often considered as population itself or huge data whose size is close to population. However, big data itself has vague definition. How 'big' should a data set be a 'big data'? In addition, general public has difficulties accessing big data. Big data are mainly handled and/or owned by large companies or government offices. Therefore, Kim (2011) proposed an alternative concept to big data: 'appropriate data (AD)'.
AD is inspired by 'appropriate technology (AT) which means that engineers/scientists should develop technologies that are both affordable and efficient. For instance, providing an expensive water purifier set to a typical underdeveloped village is useless because the residents cannot pay for the power bill and filter replacement for the device. As an example of AD study, Kim (2007) was able to predict the emergence of Barack Obama using the intersite hyperlink networks data of United States senators, retrieved in as early as 2005. This was possible because Kim understood that web is a relational and topical medium and those traditional ‘mainstream’ politics theories overlooked the importance of inter-senatorial interactions online and offline. The size of U.S. senators websites was just 100, specifically, 100*100 matrix for measuring their on-the-web interactions and content similarity among those websites. The author of this study attempts to evaluate the validity of semantic and relational data from the web of pacific telecommunication companies to evaluate their images formed by both the companies and general users.
Based on social construction theory, the present study examines how the images of pacific telecommunication companies are formed and changed over time and seeks to find theoretical and practical/policy implications from the analysis.
Ph.D. Student, Department of Interaction Science, Sungkyunkwan University
Republic of Korea
Reputation can be defined as the “publics’ cumulative judgments of firms over time” (Fombrun & Shanley, 1990, p. 235), which are perceived “from available information about firms’ activities originating from the firms themselves, from the media, or from other monitors” (Fombrun & Shanley, 1990, p. 234). This implies that it is not only corporations themselves but also other parties that influence a company’s reputation. Furthermore, with the upsurge of social networking sites (SNS) use in the recent years, there has been a drastic increase in content creation by users in online space, which is likely to have an effect on the company’s corporate image. Thus, the present study seeks to examine whether there is a relationship between reputation rankings and the type and sentiment of words that appear about telecommunication companies in social media space.
The sample was derived from the ranking of the ‘2015 Harris Poll Reputation Quotient’, a study that measures reputation of companies. Seven telecommunication corporations were selected: Verizon communications (#66), Sprint Corporation (#72), T-Mobile (#75), AT&T (#76), Time Warner (#85), Charter Communications (#92) and Comcast (#93). In order to examine the semantics about the companies in social media space, the Facebook pages of the aforementioned telecommunication corporations were analyzed. The present study used various social and semantic network analysis tools; NodeXL (Smith et al., 2010) was used for data collection, WORDij (Danowski, 2013) was used for data analysis, NetDraw from the UCINET Software (Borgatti, Everett, & Freeman, 2002) was used for data visualization and LIWC (Pennebaker, Booth, & Francis, 2007) was used for sentiment analysis. Findings from the study will provide both theoretical and practical insights on how telecommunication corporations should effectively use social media for reputation management.