Big Data for Business Model Renovation, Current Machine Learning Regression Model and Ethical Issue in the Big Data Industry

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Information and Technology’s role and digital transformation challenges: a systematic literature review

With the emergence and maturity of digital technologies (e.g., social networks, mobile telephony, big data, artificial intelligence), companies in virtually every segment are pursuing a range of initiatives to leverage their benefits. Given the increased competition from globalization and greater importance of customer focus, companies are being pressured to become digital ahead of others to survive and gain a competitive advantage. Going through such a change requires transforming the way the company sees its value proposition, its processes, the profile of its clients and its economic sustainability. Moreover, the functions of the information technology department and their leadership are being questioned. From a systematic examination of the literature, this paper presents an overview about Bimodal IT, the participation of IT in digital transformation and the hurdles of such a change

Exploring the Digital Transformation Based on Big Data with Ubiquitous Internet of Everything

Digital technologies present both game-changing opportunities for and existential threats to companies. Digital services in consumer-facing organizations offer novelty value propositions, closer consumer relationships and higher automation of consumer-facing processes. Facing big digital data streams generated by ubiquitous Internet of Everything(IoE) and savvy customers with mobile computing and social media, this paper focuses on digital transformation journeys seeking digital capabilities and digital leadership to upgrade organizational performance, one is discovering big data value, the other is dual methods with agile. The finding provides practical implications that can help guide practitioners in digital transformation

The adoption of big data technologies - A challenge for national statistics offices

National Statistics Offices (NSOs) integrate the global statistical network system. As it happens with other organizations, NSOs need to innovate in their technological structure to keep offering timely and high-quality official data. Big Data technologies are among the most relevant to improve the performance of NSOs. However, on the one hand, there is considerable variation among NSOs regarding the adoption of these technologies, which is a matter of concern. On the other hand, the phenomenon is not being addressed in the research literature. This study outlines research that aims to contribute to the understanding of how NSO organizations adopt and disseminate Big Data technologies in their main processes, including collecting, analyzing, and providing public statistics.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scopes: UIDB/00319/2020

Towards Bridging the Gap Between BDA Challenges and BDA Capability: A Conceptual Synthesis Based on a Systematic Literature Review

Big data analytics (BDA) and strategies for implementing BDA have received attention among researchers and practitioners alike. However, success stories pertaining to the implementation of BDA remain scarce. The notion of the BDA deployment gap describes the chasm between the attributed value potential of BDA and its actual value realization in organizational practice. Several research articles indicate challenges encountered in implementing BDA but lack a comprehensive systematization of BDA implementation-related challenges. This research article aims to systematize those challenges through a systematic literature review. As a result, we derived five overarching challenge dimensions related to the BDA implementation. Based on this systematization, we adopt the lens of a big data analytics capability and delineate future research avenues through the derivation of propositions on how to overcome the BDA implementation-related challenges, while enhancing our understanding about how to solve the BDA deployment gap

Big data: the path to data monetization

The path to monetizing data is a challenging task for many organizations. Not only the technical complexity and the most often needed organizational changes, but also high initial investment costs without certain outcome make this endeavor highly risky. As a result, the number of organizations that have successfully walked the path to monetizing data remains scarce. To address this issue, this paper aims to shed light on the limited understanding of how organizations can unlock the value of data and monetize it by conducting an in-depth systematic literature review

Patterns of Resource Integration in the Self-service Approach to Business Analytics

The main premise of Self-Service Business Analytics (SSBA) is to make business users autonomous during the data analytical process. To empower business employees, organisations are decentralising their analytical capabilities therefore adopting an SSBA approach. Yet, little is known about how employees integrate resources, such as personal competencies, environment resources including technology, and other employees’ competencies, to generate insights in SSBA. Based on the empirical data of a major Norwegian online marketplace and drawing on service-dominant logic as an analytical framework, we identify and explain two types of resource integration in an SSBA environment: direct and clustered recourse integration (including 1st tier and 2nd tier) enabled and controlled by three types of institutions. We finally discuss some organisational implications and the meaning of each sub-type of clustered resource integration

Artificial Intelligence and Big Data in Entrepreneurship: A New Era Has Begun

While the disruptive potential of artificial intelligence (AI) and Big Data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. Here we present some reflections and a collection of papers on the role of AI and Big Data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other Big Data technologies for contemporary structures in entrepreneurship research, we put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and Big Data contribute to a productive transformation of the research field and the real-world phenomena (e.g., 'smart entrepreneurship')? We also discuss, however, ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that we will encounter unforeseeable and currently inexplicable developments in the field soon. We call on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios

Investigating the Role of Enterprise Architecture in Big Data Analytics Implementation: A Case Study in a Large Public Sector Organization

Big Data Analytics (BDA) offers capabilities that can support a wide range of business areas across an organization. Organizations are increasingly turning to Enterprise Architecture (EA) to manage BDA implementation complexities. Through a case study in a large public sector organization, how EA supports various stages of BDA implementation is examined. The findings show that EA can address BDA challenges through 18 specific roles, which are categorised into four domains: Strategy (6 roles), Technology (4 roles), Collaboration (3 roles) and Governance (5 roles). While EA appears to have the most prominent role in strategy planning process, our study also identifies factors that can lead to the ineffectiveness of EA roles, such as frequent changes in business strategy. This study offers important implications to research and practice in EA and BDA implementation

DATA MONETIZATION CHALLENGES IN ESTABLISHED ORGANIZATIONS: A SYSTEMATIC LITERATURE REVIEW

Over the last decades, researchers and practitioners have looked at data as a valuable asset for improving business processes in organizations. However, nowadays, they see data more as a tradable asset that can be monetized. Data monetization here refers to generating revenue from selling data and data-based products and services. Despite providing opportunities for generating new revenue streams, data monetization is not without challenges, especially in established organizations. Previous research shows that an organization’s data monetization capability is constrained by its existing business model, infrastructure, and organizational culture. Although Information Systems (IS) research and practice have shown an increasing interest in data monetization, we lack a thorough understanding of its challenges. As a first step in addressing this gap, we set out to identify challenges that established organizations face in monetizing their data. To that end, we conducted a systematic literature review and identified 21 challenges reported in the extant literature. Based on their nature, we divided these challenges into five categories, including business model, legal & regulatory, security & privacy, organizational, and data management challenges. Our study has several implications for IS research and practice