A Brief Contextualization of Big Data in the International Business environment: evidence from the Alibaba Group's transition

The present work seeks to understand the impacts of Big Data and its adjacent technologies in the international business environment, with a focus on the financial sector, bringing the recent transition of Alibaba Group as a case study. In this effort, the work, in the first moment, brings a conceptualization of Big Data from the perspective of different authors, in different areas of science. In the second moment, an analysis of the applications of Big Data in the governmental scope, providing an understanding of how the technology can be applied from the perspective of the States and, at the same time, a differentiation is made with respect to the use of the Big Data in the business world, with a special focus on the financial sector of international business. In the last instance, an analysis of the Alibaba Group transition, the transition from an e-commerce company to a company that focuses on the dynamics of data is brought by this work, demonstrating how dynamics based on Big Data technologies can lead to strategic changes in companies, but they can also provoke a dynamism in the international business sector as a whole

Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services

Linking Big Data and Business: Design Parameters of Data-Driven Organizations

Big data analytics is accepted to be an important driver of business value. However, this value does not come without a cost. Becoming a data-driven organization (DDO) necessitates a substantial transformation along the components structure, actors, task, and technology. Moreover, as successfully generating value from big data requires the utilization of data insights in business, attention needs to be assigned to the different actors from the data and business side, and their interrelation and collaboration. By taking a socio-technical systems perspective and utilizing a multi-case research approach, we developed a taxonomy to structure insights about different design parameters of a DDO. Thus, we contribute to the information systems literature by proposing a holistic design framework for DDOs paying tribute to its high collaboration requirements, and offer a compendium for managers with pathways how to design a DDO

Interaction-driven definition of e-business processes

Business-to-business interaction (B2Bi) is the next step for corporate IT [1]. Business relationships become increasingly dynamic, and new requirements emerge for data and process management. Standardisation initiatives are successfully targeting business ontology [4]. Still, business agility mainly depends on the flexibility of the business processes of a company. In the B2B space, traditional approaches to process modelling and management are inadequate. Today more than ever, traditional workflow management is crucial for the internal effectiveness of a company. Internal efficiency is a prerequisite for external agility. From both a technical and a business perspective, internal workflow management relies on specific assumptions in terms of resources involved in the process, as well as the process itself [2]. Level of control, availability, reliability, and cost stability are parameters that traditional process models and technology can almost take for granted. A single authority ruling on the process definition and the total control over process execution are also basic concepts for internal workflows. From a business perspective, a big upfront investment is put in the complete definition of process specifications. A different conceptual framework is required for the definition and management of e-business processes [3, 5]. The intrinsic capability to adapt to rapidly changing business requirements becomes crucial. The line of research explored in this paper derives from an approach to process modelling and management that explicitly targets the peculiarities and dynamics of B2Bi. In the model we propose, the upfront specification of the interaction logic of a company can be limited to partially specified processes and basic interaction rules. Specific information is then gathered from the observation of actual instances of business interaction, and used to refine and extend the initial model. In addition to the enforcement of explicit business requirement, the goal is to capture and leverage implicit operational knowledge. In the following sections, we present an overview of the methodology we are currently experimenting with for the inference of complex processes from business interaction flows. For our initial experiments, we focus on business messages compliant with the RosettaNet standard [4]

Testing Big Data Applications

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications

Estimating the relation of big data on business model innovation: a qualitative research

Gaining interdisciplinary attention across academia, the concept of Big Data also finds application in the business world. Realizing the potential of the trend, this research considers the impact of Big Data with a strategic perspective and by focusing on the following research question: How can data and data-driven decisions lead to business model innovation?Challenging the assumption that Big Data even has the potential to impact business models, this research firstly elaborates on the construct of business modelsandbusiness model patterns. Subsequently, the Big Data concept is defined, by focusing on its unstructured and fast-moving nature. Considering the broad influence Big Data might have on business models, a qualitative research design is esteemed appropriate to answer the research question: The anal yses of semi-structured interviews with experts give insights about complex relations in the field of Big Data.For this research 13 participants contributedtheir opinions on Big Data, among others, they identifycurrent methodsand illustrate data visions for the future.One of the main findings of this research is that Big Data still imposes problems on managers, most of them are of analytical, technical or cultural nature. At the same time, the agents that suffer from insufficient data analytics,are invested to generate a data strategy that will facilitate data management.This research defines that data objects must be prioritized due to their utility,by means of data valuation. Associating a monetary value with data objects helps managersto commit totheirdecisions indata management. Furthermore, this research reveals that Big Data integration improves operations at various levels. In an incremental instance,businesses can reduce costs or differentiate their product and service portfolio through Big Data integration.Furthermore, Big Data finds applications on a strategic level:This research detects that Big Data possesses the proficiency to facilitate all business model dimensions and even to create innovation. Concluding, this master thesiscontributes to the research field of Strategy&Innovationas it increases the theoretical understanding of Big Data and its integration in strategic decision making. It considers several related topics to assess the capability of data,by including the notions of data monetization and experience data. Furthermore, this thesis discloses novel case studies, which give evidence of the status quo of data integration across industries. By deriving propositions, this study serves as a valuable guideline for further research on data management and business model innovation

Collaboration for Big Data Analytics: Investigating the (Troubled) Relationship between Data Science Experts and Functional Managers

The utilization of insights from big data analytics (BDA) in business operations has been identified as a major driver to unlock value from big data. This emphasizes the importance of the involvement of functional business managers in BDA projects and draws attention to their collaboration with BDA experts, such as data scientists. Scholars have identified several challenges that explain why the success rates of BDA projects remain low. However, the relationship between managers and data science experts has not yet been examined as a potential reason for failure. By applying a social capital perspective on the relationship between these groups, we employ a multiple case study to investigate possible obstacles. We find that the relationship is largely troubled due to incongruent cognitive interpretations of BDA applications in the business context, and the absence of structural network ties. These findings suggest a previously under-researched reason why BDA projects still frequently fail

Collaborative mechanisms for big data analytics projects: Building bridges over troubled waters

Big data analytics (BDA) is accepted to be an important driver of business value. Deriving value from big data to improve organizational decision-making requires the collaboration of data science experts and business users. However, recent literature has shown that their relationship is troubled. Tension arises from diverse relational difficulties and change-inherent challenges. The relationship has been theorized to lack social capital, which leads to inferior collaboration and diminishes project success. In this vein, scholars have begun to investigate relational governance mechanisms, but detailed insights on collaborative approaches to foster the relationship remain scarce. By applying multiple-case research, we shed light on collaborative mechanisms and reveal their impact on the relationship between data science and business employees, theorized by means of social capital. Thus, we build theoretical and practical bridges over the troubled waters in BDA collaboration and contribute to BDA success from a social perspective

Big-Data Labs: Merchandising Informatics by Using Hyperlinks and Network Analysis Visualization Approaches

Merchandising informatics, a novel research-related pedagogy, views data analytics from an information management perspective on merchandising practices. More willingly competent merchandising graduates are able to provide analytical support to cross functional projects (e.g., email targeting, consumer recommendations, product loyalty forecasts) and assist in building large data sets from multiple sources in order to predict future data characteristics. A visionary data inventor with a passion for learning new technologies and translating data into business solutions is critical for growth and success in the merchandising industry. Merchandising informatics aims to transform teaching and learning at graduate courses and around the globe by implementing big-data labs. Applying hyperlinks and Network Analysis Visualization (NAV) approaches to big data construal helps graduates grasp contemporarily big data concepts more quickly and fully, connect theory and application more adeptly, and engage in learning more readily, while also improving instructional techniques, and facilitating the widespread sharing of knowledge. Indeed, the information management perspective and practical experiences within merchandising informatics equip graduates with unique and career-oriented capabilities

Towards cloud based big data analytics for smart future cities

© 2015, Khan et al.; licensee Springer. A large amount of land-use, environment, socio-economic, energy and transport data is generated in cities. An integrated perspective of managing and analysing such big data can answer a number of science, policy, planning, governance and business questions and support decision making in enabling a smarter environment. This paper presents a theoretical and experimental perspective on the smart cities focused big data management and analysis by proposing a cloud-based analytics service. A prototype has been designed and developed to demonstrate the effectiveness of the analytics service for big data analysis. The prototype has been implemented using Hadoop and Spark and the results are compared. The service analyses the Bristol Open data by identifying correlations between selected urban environment indicators. Experiments are performed using Hadoop and Spark and results are presented in this paper. The data pertaining to quality of life mainly crime and safety & economy and employment was analysed from the data catalogue to measure the indicators spread over years to assess positive and negative trends