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

Shadow Bank Systems in European Affairs: Measuring Capability of Fraud Risk in Special Reports

In the last twenty years, the European Court of Auditors has placed increasing importance on the production of “special reports” examining the economy, efficiency and effectiveness of EU spending. This institutional focus on performance audit, alongside traditional financial and compliance audit, has occurred at a time when the European Union is increasingly evaluating its own policies and programmes, under political pressure to demonstrate their added value with shadow bank systems. The rapid growth of the market-based financial system since the mid-1980s changed the nature of financial intermediation in the European States profoundly. Within the market-based financial system, “shadow banks” are particularly important institutions. Shadow banks are financial intermediaries that conduct maturity, credit, and liquidity transformation without access to central bank liquidity or public sector credit guarantees. Examples of shadow banks include finance companies, asset-backed commercial paper (ABCP) conduits, limited-purpose finance companies, structured investment vehicles, credit hedge funds, money market mutual funds, securities lenders, and government-sponsored enterprises. This intermediation chain binds shadow banks into a network, which is the shadow banking system. The shadow banking system rivals the traditional banking system in the intermediation of credit to households and businesses. This study contributes theoretically to research and empirically to management practice of agile marketing concepts in digital trsformation and international business context, in order to develop effective competencies of speed, flexibility, and customer responsiveness in marketing strategies and operations

Analysis of the impact of test based development techniques (TDD, BDD, AND ATDD) to the software life cycle

Within the world of software development, there is a permanent need to create quality products that are capable of facing challenges in environments of changing requirements. The industry in this area is aware of this, and so, it makes use of software development methodologies such as: traditional or agile. Agile development represents a distancing from traditional approaches, allowing the creation of applications incrementally and iteratively and, thus, adjusting to the changing requirements of customers. For this reason, companies have recently adopted the use of its practices and techniques, e.g.: Test-Driven Development (TDD), Acceptance Test-Driven Development (ATDD), Behavior-Driven Development (BDD), among others. These techniques promise mainly to improve the quality of the software and the productivity of the programmers; therefore, many experiments, especially using TDD, have been made within the academy and the industry; which shows variant results (some with positive effects and others not so much). In addition, expert programmers have put these techniques into practice in software creation, getting satisfactory results due to the advantages offered by its use. The main objective of this work is to verify the impact produced by the techniques of software development based on tests (TDD, ATDD and BDD), analyzing its primordial promises. A literature research has been conducted in order to understand the strengths and weaknesses of each one of these techniques. With the intention of observing the effectiveness of TDD and BDD, an experiment was planned in an academic scenario, considering education and appropriate training to implement enough knowledge about them. With the results obtained, it was possible to understand that the techniques studied ensured the quality of the product developed and improved the productivity of the programmers; validating its effects within software development

A Framework of Information Systems Development Concepts

Background: Information Systems Development (ISD) is responsible for designing and implementing information systems that support organizational strategy, leveraging business models and processes. Several perspectives on this activity can be found in the literature, addressing – often in an undifferentiated manner – approaches, lifecycles, methodologies, and process models, among others. Objectives: The vast diversity of ideas and concepts surrounding ISD and the multiple underlying views on the subject make it harder for researchers and practitioners to understand the relevant aspects of this important activity. This article aims to systematize and organize ISD’s main concepts to create a coherent perspective. Methods/Approach: We conducted a literature review and thematic analysis of ISD\u27s main concepts. Results: To contribute to filling the research gap, this article proposes a new framework that addresses the key aspects related to ISD. Conclusions: The framework comprises ISD’s core concepts, such as lifecycles, process models, deployment approaches, and methodologies

Business Intelligence Technology, Applications, and Trends

Enterprises are considering substantial investment in Business Intelligence (BI) theories and technologies to maintain their competitive advantages. BI allows massive diverse data collected from virus sources to be transformed into useful information, allowing more effective and efficient production. This paper briefly and broadly explores the business intelligence technology, applications and trends while provides a few stimulating and innovate theories and practices. The authors also explore several contemporary studies related to the future of BI and surrounding fields

The Emergence of Enterprise Systems Management - A Challenge to the IS Curriculum

This paper proposes four cornerstones of a future Information Systems curriculum. It analyzes the challenges of the IS curriculum based on the development of enterprise systems, and further argues that the practice and the research into enterprise systems have progressed to a new stage resulting in the emergence of Enterprise Systems Management (ESM). Enterprise Systems Management calls for new competences and consequently represents new challenges to the IS curriculum. The paper outlines potential teaching issues and discusses the impact on the IS curriculum. Finally the paper suggests ways of approaching the challenges.No; keywords

Data management and Data Pipelines: An empirical investigation in the embedded systems domain

Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. Further, AI is being applied in a growing number of fields, and thus it is evolving as a horizontal technology. Consequently, AI components are increasingly been integrated into embedded systems along with electronics and software. We refer to these systems as AI-enhanced embedded systems. Given the strong dependence of AI on data, this expansion also creates a new space for applying data management techniques. Objective: The overall goal of this thesis is to empirically identify the data management challenges encountered during the development and maintenance of AI-enhanced embedded systems, propose an improved data management approach and empirically validate the proposed approach.Method: To achieve the goal, we conducted this research in close collaboration with Software Center companies using a combination of different empirical research methods: case studies, literature reviews, and action research.Results and conclusions: This research provides five main results. First, it identifies key data management challenges specific to Deep Learning models developed at embedded system companies. Second, it examines the practices such as DataOps and data pipelines that help to address data management challenges. We observed that DataOps is the best data management practice that improves the data quality and reduces the time tdevelop data products. The data pipeline is the critical component of DataOps that manages the data life cycle activities. The study also provides the potential faults at each step of the data pipeline and the corresponding mitigation strategies. Finally, the data pipeline model is realized in a small piece of data pipeline and calculated the percentage of saved data dumps through the implementation.Future work: As future work, we plan to realize the conceptual data pipeline model so that companies can build customized robust data pipelines. We also plan to analyze the impact and value of data pipelines in cross-domain AI systems and data applications. We also plan to develop AI-based fault detection and mitigation system suitable for data pipelines

Mapping of Internal Audit Research: A Post-Enron Structured Literature Review

Purpose — This paper reviews the field of internal auditing (IA) post-Enron to develop insights into how IA research has developed, offer a critique of the research to date and identify ways that future research can help to advance IA. Design/methodology/approach — A structured literature review (SLR) was used to analyse 471 papers from 64 journals published between 2005 and 2018 based on a number of criteria; namely, author, journal type, journal location, year, theme, theory, nature of research, research setting, regional focus, method and citations. Findings — The IA literature has not significantly contributed to knowledge of the internal audit function (IAF), and we still know relatively little about the factors that contribute to making the impact of IA practice effective and measurable. The IA literature is US dominated (authors and journals), focused on the American context (publicly listed companies), reliant on positivist analyses and largely makes no explicit reference to theory. Central regions (emerging economies) and key organisational settings (private SMEs and not-for-profit organisations) are largely absent in prior IA research. This paper evaluates and identifies avenues through which future research can help to advance IA in order to address emerging challenges in the field. Originality/value — This is the first comprehensive review to analyse IA research in the postEnron period (2005–2018). The findings are relevant to researchers who are looking for appropriate research outlets and emerging scholars who wish to identify their own research directions. Keywords — Internal audit, internal audit function, structured literature review, Enron Paper type — Literature revie