BIG DATA ANALYTICS TOOLS: A BIBLIOMETRIC LITERATURE REVIEW

Analytics as a field is rapidly growing because of businesses need to tackle increasingly large and complex datasets to gain a competitive edge in the business environment. Currently there are two distinct types of analytics technologies: proprietary and open-source. Each has their distinct strengths and weaknesses, but which is relevant in today business environment? Questions like this are important for businesses wanting utilize these technologies in order to become larger and more efficient. This research answered this question and it was found that both proprietary and open-source technologies are equally relevant in current analytics research. Because of this, businesses should be more aware of the analytics field and how both types of technologies could benefit their current operations and should strategically utilize both types to meet their specific data needs

The Role of Big Data Analytics on Innovation: A Study from The Telecom Industry

Telecom companies face a fierce competition from innovation based start-up companies, particularly those that are using internet networks to offer communication services through the voice and video over internet protocol (VoIP & PVoIP) technologies. More than 10 years have passed from the time the internet and VoIP were widely used, but still, telecom companies are having a great deal of business success through offering a wide spectrum of services, products, deals, and packages to consumers using both B2C and B2B models. The future landscape of how telecom companies will evolve in the market is still not clear, particularly with the increase of aggressive competition from companies that are technology-innovative and starting to deliver new forms of ubiquitous communication technology and services. Understanding why and how telecom companies innovate in the market is very crucial in order to predict the future of this business sector. In this paper, we argue that telecom companies are utilising their capabilities that have a significantly important role in fostering innovation, namely information technology (IT) capability and knowledge management (KM) capability. IT capabilities have changed dramatically in the last few years with the introduction of intelligent systems, big data analytics, the Internet of Things and the wide use of mobile apps and sensors. It is not clear how these technologies play a role in telecom companies’ innovation and it is not clear whether IT impacts innovation directly or if KM capability has a mediation role in utilising technology to support innovation. This paper is a position paper to establish grounds for understanding how telecom companies innovate, and in particular how IT and KM capabilities influence innovation. We outline the methodology of this investigation as a qualitative study with stakeholders from multiple telecom companies and we expect at the end of the study to be able to offer a holistic view on the way these companies innovate in regard to their products and services. We aim at providing a cross case studies comparison towards a prediction of the future of the telecom business sector

DevOps Continuous Integration: Moving Germany’s Federal Employment Agency Test System into Embedded In-Memory Technology

This paper describes the development of a continuous integration database test architecture for a highly important and large software application in the public sector in Germany. We apply action design research and draw from two emerging areas of research – DevOps continuous integration practices and in-memory database development – to define the problem, design, build and implement the solution, analyze challenges encountered, and make adjustments. The result is the transformation of a large test environment originally based on Oracle databases into a flexible and fast embedded in-memory architecture. The main challenges involved overcoming the differences between the SQL specifications supported by the development and production systems and optimizing the test runtime performance. The paper contributes to theory and practice by presenting one of the first studies showing a real-world implementation of a successful database test architecture that enables continuous integration, and identifying technical design principles for database test architectures in general

An evidence-based management framework for business analytics

It is said that knowledge is power, yet often, decision makers ignore information that ought to be considered. The phenomenon known as Semmelweis reflex occurs when new knowledge is rejected because it contradicts established norms. The goal of evidence-based management (EBMgt) is to help overcome Semmelweis reflex by integrating evaluated external evidence with stakeholder preference, practitioner experiences, and context. This evaluated external evidence is the product of scientific research. In this paper, we demonstrate an EBMgt business analytics model that uses computer simulation to provide scientific evidence to help decision makers evaluate equipment replacement problems, specifically the parallel machine replacement problem. The business analytics application is demonstrated in the form of a fleet management problem for a state transportation agency. The resulting analysis uses real-world data allowing decision makers to unfreeze their current system, move to a new state, and re-freeze a new system

The Role of Big Data Analytics in Innovation: A Study from The Telecom Industry

Organisations are looking for new definitions and guidelines for innovation direction due to the changing nature of technology, user behaviour, competition and market trends. Data sources, types and analysis mechanisms have changed dramatically in the last few years, and there are pieces of evidence that these are influencing the level of innovation in a firm. We found that it is very important to explore how telecom companies capture, analyse and make innovation insights from big data. Our review shows a clear scarcity of research on this topic. The study aims to use qualitative methods of both interviews and documents review in three telecom companies in Jordan, with an opportunity to extend the study to different regions and countries. The understanding of how big data and its analysis are carried out by companies will support our effort in building more systematic procedures and guidelines for companies who wish to utilise big data for different types of innovation with different levels of maturity indicators

THE ADOPTION OF BIG DATA SERVICES BY MANUFACTURING FIRMS: AN EMPIRICAL INVESTIGATION IN INDIA

Although some leading companies are actively adopting Big data services (BDS) to strengthen market competition , many manufacturing firms are still in the early stage of the adoption curve due to lack of understanding of and experience with BDS. Hence, it is interesting and timely to understand issues relevant to BDS adoption. The empirical investigation reveals that a firm’s intention to adopt BDS can be positively affected by the quality and benefits of BDS. Surprisingly, a firm’s absorptive capacity in utilizing big data and risks and costs associated with implementation and maintenance does not impact the adoption intention of BDS

Data supply chain (DSC): Research synthesis and future directions

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the preexisting SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies

Big data and supply chain management: A marriage of convenience?

Big data is the new “guy about town.” Indeed, the buzz about Big Data and business intelligence (BI) as drivers of business information data collection and analysis continues to build steam. But it seems not everyone is taking notice. Whilst scholars in main are excited about the “fields of possibilities” big data and related analytics offer, in terms of optimising firm capabilities, supply chain scholars have been surprisingly quiet. In this work we hope to break this silence and we achieve this through a comprehensive survey of the literature with the aim of exposing the dynamics of big data analytics in the supply chain context. Our findings suggest that the benefits of a big data driven supply chain are many on the proviso that organisations can overcome their own myopic understanding of this socio-technical phenomenon. However, this is not to suggest a one-size fits all approach, our findings also reveal that adopting a big data strategy in the supply chain is a strategic decision and as such, given the idiosyncrasies of industries, firms should leverage these technologies in congruence with their core capabilities. Strategic fit between a firm core competences and its big data strategy creates causal ambiguity which can in turn lead to sustainable competitive advantage

Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research