A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature

Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

The Hype of Big Data Analytics and Auditors

In the era of fast-tracking digitization and unconventional big data analytics, business models are being reshaped and they impact auditing amongst auditors. This viewpoint paper takes into account the procedures underlying on big data and its analytics in driving the evolution of business and identifies some of the unresolved issues and concerns on auditors, especially in the context of cognitive tasks. The paper continues to focus on the current spate of discussions on big data and auditing profession. It explains the nature of big data and its characteristics as well as the output types. This paper also tries to find answers for what is new in it, how it assists the auditors along with some unresolved  issues and concerns. Since big data analytics is the future, auditors need to reshape themselves in terms of skills and competencies to meet the emerging technological challenges.

A Proposed Architecture for Big Data Driven Supply Chain Analytics

Advancement in information and communication technology (ICT) has given rise to explosion of data in every field of operations. Working with the enormous volume of data (or Big Data, as it is popularly known as) for extraction of useful information to support decision making is one of the sources of competitive advantage for organizations today. Enterprises are leveraging the power of analytics in formulating business strategy in every facet of their operations to mitigate business risk. Volatile global market scenario has compelled the organizations to redefine their supply chain management (SCM). In this paper, we have delineated the relevance of Big Data and its importance in managing end to end supply chains for achieving business excellence. A Big Data-centric architecture for SCM has been proposed that exploits the current state of the art technology of data management, analytics and visualization. The security and privacy requirements of a Big Data system have also been highlighted and several mechanisms have been discussed to implement these features in a real world Big Data system deployment in the context of SCM. Some future scope of work has also been pointed out. Keyword: Big Data, Analytics, Cloud, Architecture, Protocols, Supply Chain Management, Security, Privacy.Comment: 24 pages, 4 figures, 3 table

DEVELOPMENT OF CONCEPTUAL MODEL FOR SOCIAL COMMERCE RESEARCH THROUGH INTEGRATION WITH BIG DATA ANALYSIS

Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice

Business Open Big Data Analytics to Support Innovative Leadership Decision in Canada

This paper summarizes how social media and other technologies continue to proliferate; the shifting economic landscape will precipitate more adaptive approaches for managers attempting to understand the multidimensional virtual aspects of communication with the artificial intelligence aspect. Also, we discover the different existing support of big data analytics to make the rational business decision. The methodology is the systematization literature sources within this context and approaches for underlining approach to open big data analytics and support innovative leadership decisions in Canada

Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

Business Analytics in the Context of Big Data: A Roadmap for Research

This paper builds on academic and industry discussions from the 2012 and 2013 pre-ICIS events: BI Congress III and the Special Interest Group on Decision Support Systems (SIGDSS) workshop, respectively. Recognizing the potential of “big data” to offer new insights for decision making and innovation, panelists at the two events discussed how organizations can use and manage big data for competitive advantage. In addition, expert panelists helped to identify research gaps. While emerging research in the academic community identifies some of the issues in acquiring, analyzing, and using big data, many of the new developments are occurring in the practitioner community. We bridge the gap between academic and practitioner research by presenting a big data analytics framework that depicts a process view of the components needed for big data analytics in organizations. Using practitioner interviews and literature from both academia and practice, we identify the current state of big data research guided by the framework and propose potential areas for future research to increase the relevance of academic research to practice

Value co-creation and potential benefits through big data analytics: Health Benefit Analysis

Big data analytics in healthcare context is often studied from a technical point of view. In the field of strategic management, researchers have indicated a research gap in how big data analytics create business value. This study examines how big data and advanced analytics generate potential benefits and business value for the healthcare service provider, and value for the individual patients and population health. In addition, the effects of advanced analytics to the value co-creation practices and actors in healthcare ecosystem are studied. The theoretical framework used for the purpose is the big data analytics-enabled transformation model which is adapted to answer the research questions. The study is conducted as a single case study. The studied case is the Health Benefit Analysis (HBA) tool. The empirical data is collected in eight semi-structured interviews with participants of the tool development project. Using the HBA tool reveals several paths-to-value chains. The most evident path shows how using advanced analytics affects the personalized care practice by enabling a more interactive service process between the health professionals and patients. It denotes a business scope redefinition as patients are now being interpreted as essential actors in the value co-creation of their own health outcomes. The benefits that arise from the advanced analytics are of several dimensions; operational, managerial, strategic, and organizational. Using the HBA tool generates strategic business value for the healthcare service provider as a differentiator that contributes to gaining competitive advantage compared to other service providers not using this innovation. Value emerges for the individual patient as improved patient experience and better health outcomes. Population health gains most value from the reduced health inequalities. The evolving value co-creation practices set requirements for the healthcare ecosystem actors as they need to conform to new practices with patients and other professionals from other sectors and levels of the ecosystem. The healthcare work and service culture need to develop and adapt to new tools, related processes, and a more diversified professional base, including health analysts and other new professionals. To conclude, it can be claimed that advanced analytics of healthcare big data contributes to the shift to value-based healthcare.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Revisiting Ralph Sprague’s Framework for Developing Decision Support Systems

Ralph H. Sprague Jr. was a leader in the MIS field and helped develop the conceptual foundation for decision support systems (DSS). In this paper, I pay homage to Sprague and his DSS contributions. I take a personal perspective based on my years of working with Sprague. I explore the history of DSS and its evolution. I also present and discuss Sprague’s DSS development framework with its dialog, data, and models (DDM) paradigm and characteristics. At its core, the development framework remains valid in today’s world of business intelligence and big data analytics. I present and discuss a contemporary reference architecture for business intelligence and analytics (BI/A) in the context of Sprague’s DSS development framework. The practice of decision support continues to evolve and can be described by a maturity model with DSS, enterprise data warehousing, real-time data warehousing, big data analytics, and the emerging cognitive as successive generations. I use a DSS perspective to describe and provide examples of what the forthcoming cognitive generation will bring