Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Preemptive Security Through Information Analytics

ABSTRACT

Business security and threat actors continue to play a dangerous cat-and-mouse game with businesses intellectual property, customer data, and business reputations at stake. Businesses need to delve into a new way of doing business security to break out of this game. Businesses are sitting on repositories full of security-relevant data that is not being capitalized upon with the current information security and physical security organizations within businesses. This article poses the introduction of a data scientist role and a new supporting central data correlation technology platform based on big data predictive analytics into business security functions. The goal is to intelligently and autonomously identify, correlate and pinpoint normally innocuous or unnoticed security event attributes to allow security personnel to preemptively remediate physical and information risks before exploitation or loss of intellectual property occurs.     

Big Data analytics for sustainability: Insight through technological innovation

How do information systems and big data analytics help to enable a sustainable future? This question is investigated in nine papers in this special issue that examine the issue of big data analytics for sustainability from a variety of perspectives. Broadly, these papers can be considered in four main areas: health, online behavior and consumption, safety and the environment, and methods to improve understanding of sustainability issues. Recent advances in data-driven decision-making analytics research focusing on different aspects of sustainability are discussed in these papers, including air pollution management, online health consultation services, gamification of exercise and health, sustainable urban mobility, the sustainable use of resources in hospitals, the design of anticrime information support systems, the interdependence effects among mobile social apps, networks of sustainable development goals, and the spillover effect of sustainable consumption.     

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

基于MOOC的大数据分析技术

MOOC近几年发展迅猛,在使用过程中,大规模的学习者和海量的教学资源积累了庞大的学习行为数据。因此,基于MOOC的大数据分析成为了一个新兴的研究热点,其分析框架中涉及的四大核心是：大数据从哪里获取（Where）、MOOC大数据的类型（What）、如何进行大数据分析（How）和大数据分析应用（Do）。本文通过对MOOC现状的分析、特征及分类的梳理,提出一种Where-What-How-Do大数据分析框架,并对上述的四大核心进行阐述和回答。最后,结合Canvas Network数据集进行聚类分析和多元回归分析,得出关于MOOC数据的一些启示和应用。      

An integrated big data analytics-enabled transformation model: Application to health care

A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data analytics capabilities, IT-enabled transformation practices, benefit dimensions, and business values. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.     

Data analytics and firm performance: An empirical study in an online B2C platform

Data analytics has become an increasingly eye-catching practice in both the academic and the business communities. The importance of data analytics has also prompted growing literature to focus on the design of data analytics. However, the boundary conditions for data analytics in creating value have been largely overlooked in the literature. The objective of this article therefore is to examine the business value of data analytics usage and explore how such value differs in different market conditions. We rely on an online B2C platform as our empirical setting and obtain several important insights. First, both demand-side and supply-side data analytics usage has a positive effect on the performance of merchants. Second, when merchants’ product variety is high, the influence of usage toward demand-side data on performance is strengthened, whereas such impact is weakened for supply-side data analytics. Third, when competitive intensity is high, the performance implication of demand-side data analytics usage is strengthened, whereas such impact is not strengthened for supply-side data analytics. This study contributes by advancing the overall understanding of business value of data analytics.     

Optimizing the energy efficiency of chiller systems in the semiconductor industry through big data analytics and an empirical study

Big data analytics is playing a more and more prominent role in the manufacturing industry as corporations attempt to utilize vast amounts of data to optimize the operation of plants and factories to gain a competitive advantage. Since the advent of Industry 4.0, also known as smart manufacturing, big data analytics, combined with expert domain knowledge, is facilitating ever-greater levels of speed and automaticity in manufacturing processes. The semiconductor industry is a fundamental driver of this transformation; moreover, due to the highly complex and energy-consuming nature of the semiconductor manufacturing process, semiconductor fabrication facilities (fabs) can also benefit greatly from incorporating big data analytics to improve production and energy efficiency. This paper developed a big data analytics framework, along with an empirical study conducted in collaboration with a semiconductor manufacturer in Taiwan, to optimize the energy efficiency of chiller systems in semiconductor fabs. Chiller systems are one of the most energy-consuming systems within a typical modern fab. The developed big data analytics framework allows production managers to ensure that chiller systems operate at an optimized level of energy efficiency under dynamically changing conditions, while fulfilling the chilling demands. Compared to the commonly-used heuristics previously employed at the fab to tune chiller system parameters, by the utilization of big data analytics, it is shown that fabs can achieve substantial energy savings, greater than 12%. The developed framework and the lessons learned from the empirical study are not only generalizable but also useful for practitioners who are interested in applying big data analytics to optimize the performance of other equipment systems in fabs.     

Enhancing enterprise agility by deploying agile drivers, capabilities and providers

Agility is perceived as the dominant competitive vehicle for all organizations in an uncertain and ever-changing business environment. When embracing agility, important questions must be asked. What precisely is agility and how can it be measured? How can one adopt the appropriate agile enablers to develop agility? How can one effectively assist in enhancing agility?For an enterprise to achieve agility, it is critical to create an effective integrated procedure within the business that coordinates and ensures that the agility providers can satisfy the agility capabilities and cope with drivers, ultimately transforming all of these attributes into strategic competitive edges. However, the existing literature on enterprise agility has failed to sufficiently address the relevant perspectives in such analyzes. The relationship matrix in the quality function deployment (QFD) method provides an excellent tool for deploying important concepts and linking processes. This report suggests a new agility development method for dealing with the interface and alignment issues among the agility drivers, capabilities and providers using the QFD relationship matrix and fuzzy logic. A fuzzy agility index (FAI) for an enterprise composed of agility capability ratings and a total relation-weight with agility drivers was developed to measure the agility level of an enterprise. This report also describes how this robust approach has been applied to develop agility in a Taiwanese information technology (IT) product and service enterprise. This development project revealed that the proposed framework and procedures can enhance the agility of an enterprise as well as ensure a competitive edge.     

Improving Cyber Security Awareness and Training Programs with Data Analytics

ABSTRACT

This article it is a modest attempt to explore the issues facing cyber security awareness and training programs and potential benefits of using learning analytics, an emerging field in data analytics, for combining existing data sources to provide additional value to these programs. This article was written under the assumption that awareness and training are valid preventive controls and therefore the pros and cons of implementing such programs are not being discussed.     

Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights.     

How does business analytics contribute to business value?

This paper presents a model, synthesized from the literature, of factors that explain how business analytics contributes to business value. It also reports results from a preliminary assessment of that model. The model consists of two parts: a process and a variance model. The process model depicts the analyze‐insight‐decision‐action process through which use of an organization's business analytic capabilities is intended to create business value. The variance model proposes that the five factors in Davenport et al.'s DELTA model of business analytics success factors, six from Watson & Wixom and three from Seddon et al.'s model of organizational benefits from enterprise systems, assist a firm to gain business value from business analytics. A preliminary assessment of the model was conducted using data from 100 customer success stories from vendors such as IBM, SAP and Teradata. Our conclusion is that the business analytics success model is likely to be a useful basis for future research.     

Current trends on ICT technologies for enterprise information systems

As business conditions change rapidly, the need for integrating business and technical systems calls for novel ICT frameworks and solutions to remain concurrent in highly competitive markets. A number of problems and issues arise in this regard. In this paper, four big challenges of enterprise information systems (EIS) are defined and discussed: (1) data value chain management; (2) context awareness; (3) usability, interaction and visualization; and (4) human learning and continuous education. Major contributions and research orientations of ICT technologies are elaborated based on selected key issues and lessons learned. First, the semantic mediator is proposed as a key enabler for dealing with semantic interoperability. Second, the context-aware infrastructures are proposed as a main solution for making efficient use of EIS to offer a high level of customization of delivered services and data. Third, the product avatar is proposed as a contribution to an evolutionary social, collaborative and product-centric and interaction metaphor with EIS. Fourth, human learning solutions are considered to develop individual competences in order to cope with new technological advances. The paper ends with a discussion on the impact of the proposed solutions on the economic and social landscape and proposes a set of recommendations as a perspective towards next generation of information systems.     

Analytics Capabilities and the Decision to Invest in Analytics

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.     

Establishment-level occupational safety analytics: Challenges and opportunities

In occupational safety and health, big data and analytics show promise for the prediction and prevention of workplace injuries. Advances in computing power and analytical methods have allowed companies to reveal insights from the “big” data that previously would have gone undetected. Despite the promise, occupational safety has lagged behind other industries, such as supply chain management and healthcare, in terms of exploiting the potential of analytics and much of the data collected by organizations goes unanalyzed. The purpose of the present paper is to argue for the broader application of establishment-level safety analytics. This is accomplished by defining the terms, describing previous research, outlining the necessary components required, and describing knowledge gaps and future directions. The knowledge gaps and future directions for research in establishment-level analytics are categorized into readiness for analytics, analytics methods, technology integration, data culture, and impact of analytics.     

The promising future of healthcare services: When big data analytics meets wearable technology

As policy-makers and business practitioners across the globe expend extraordinary effort toward the field of e-health, the thriving development of healthcare-wearable technology is creating great opportunities and posing a remarkable future for healthcare services. This paper employs a game theory model to investigate the dynamics of wearable device market. We extend the two-dimensional product differentiation model by incorporating consumer diversity, consumer density, and firms’ big data analytics (BDA) investment strategy. Our model reveals that with differentiated consumer densities firms are more likely to engage in quality competition and the firm that invests in BDA can achieve higher profits. Furthermore, the overall quality of biomedical and healthcare services can be improved under various market conditions. Our findings provide practical guidance to wearable device manufacturers on optimizing competition strategies and offer insights to social planners on potential policy-making to promote better healthcare services.     

Deliberate storytelling in big data analytics adoption

The emergence of big data analytics (BDA) has posed opportunities as well as multiple challenges to business practitioners, who have called for research on the behavioural factors underlying BDA adoption at the individual level. The purpose of this study is to extend the information systems (IS) research on storytelling and to explore the role and characteristics of deliberate storytelling in individual‐level BDA adoption. This case study used the grounded theory approach to extract qualitative data from 24 interviews, field notes, and documentary data. The explicit contributions of the study to the literature include (a) increasing our understanding of the facilitating role of deliberate storytelling in individual‐level BDA adoption, (b) identifying four deliberate storytelling patterns and seven underlying corporate stories disseminated by organizations to influence individual behaviour, and (c) defining the core characteristics of effective deliberate storytelling. This study has multiple implications for business practitioners and demonstrates how deliberate storytelling can be used as a facilitating mechanism in daily business practice.     

Self-Service Business Intelligence Resulting in Disruptive Technology

Self-Service Business Intelligence (BI) requires a much greater consideration of the knowledge workers or reporting and analytics users' point of view than in traditional reporting. In order to meet the reporting and analytics users' needs, much greater interaction with the BI users was required because the awareness that those doing the development frequently did not share the reporting and analytics users' perspective or even understand it. The purpose of this Self-Service Business Intelligence effort was to provide customers with a window into available business data, so they can easily manipulate their data to answer business questions. This effort explores some of the disruptive technology available to empower a significantly more information-capable customer. In providing Self-Service Business Intelligence, a significant amount of cost savings can be achieved through better communication between business and information technology (IT) individuals, reducing the required development staff in IT, and increasing agility of the enterprise by using the agreed upon Semantic definition of terms and making the business data more accessible.