Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Design and initial validation of the Raster method for telecom service availability risk assessment

Crisis organisations depend on telecommunication services; unavailability of these services reduces the effectiveness of crisis response. Crisis organisations should therefore be aware of availability risks, and need a suitable risk assessment method. Such a method needs to be aware of the exceptional circumstances in which crisis organisations operate, and of the commercial structure of modern telecom services. We found that existing risk assessment methods are unsuitable for this problem domain. Hence, crisis organisations do not perform any risk assessment, trust their supplier, or rely on service level agreements, which are not meaningful during crisis situations. We have therefore developed a new risk assessment method, which we call RASTER. We have tested RASTER using a case study at the crisis organisation of a government agency, and improved the method based on the analysis of case results. Our initial validation suggests that the method can yield practical results

AAPOR Report on Big Data

In recent years we have seen an increase in the amount of statistics in society describing different phenomena based on so called Big Data. The term Big Data is used for a variety of data as explained in the report, many of them characterized not just by their large volume, but also by their variety and velocity, the organic way in which they are created, and the new types of processes needed to analyze them and make inference from them. The change in the nature of the new types of data, their availability, the way in which they are collected, and disseminated are fundamental. The change constitutes a paradigm shift for survey research.There is a great potential in Big Data but there are some fundamental challenges that have to be resolved before its full potential can be realized. In this report we give examples of different types of Big Data and their potential for survey research. We also describe the Big Data process and discuss its main challenges

Cross-disciplinary lessons for the future internet

There are many societal concerns that emerge as a consequence of Future Internet (FI) research and development. A survey identified six key social and economic issues deemed most relevant to European FI projects. During a SESERV-organized workshop, experts in Future Internet technology engaged with social scientists (including economists), policy experts and other stakeholders in analyzing the socio-economic barriers and challenges that affect the Future Internet, and conversely, how the Future Internet will affect society, government, and business. The workshop aimed to bridge the gap between those who study and those who build the Internet. This chapter describes the socio-economic barriers seen by the community itself related to the Future Internet and suggests their resolution, as well as investigating how relevant the EU Digital Agenda is to Future Internet technologists

Enriching Policy with Research

{Excerpt} The failure of researchers to link evidence to policy and practice produces evidence that no one uses, impedes innovation, and leads to mediocre or even detrimental development policies. To help improve the definition, design, and implementation of policy research, researchers should adopt a strategic outcome-oriented approach. In the development sector, research in science, technologies, and ideas can make a difference if they identify what tools, methods, and approaches no longer work; test new ways of doing things; and link knowledgeof that in ways that inform policy and practice. Research, the systematic effort to increase the stock of knowledge, has innumerable applications. For this reason, educational institutions, governments, and philanthropic organizations—the three major purveyors of money—spend billions of dollars on research every year. A propos developing countries, where utilitarian science policy is favored, proponents contend persuasively that it can help save lives, reduce poverty, and improve the quality of human existence

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis