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

What\u27s So Different about Developing Web Based Information Systems?

This paper considers the suitability of traditional IS development methods to Web-based information systems. A two year e-commerce development project is used to explore Web-based IS development using action research. To distinguish the project from consultancy a framework of ideas – Multiview - is declared and tested in the research process. Multiview was defined in 1985 and has been since refined to become an influential approach to information systems development. It has soft and hard aspects and, as a contingency approach, is not prescriptive but adapted to the particular situation in the organization and the application. The differences and similarities of traditional IS development projects and Web-based projects are reported and found to be more about concrete differences of methodology content than abstract concepts. The project also provided an opportunity to reflect more generally about the role of methodology in IS development

What, When and Where of petitions submitted to the UK Government during a time of chaos

In times marked by political turbulence and uncertainty, as well as increasing divisiveness and hyperpartisanship, Governments need to use every tool at their disposal to understand and respond to the concerns of their citizens. We study issues raised by the UK public to the Government during 2015-2017 (surrounding the UK EU-membership referendum), mining public opinion from a dataset of 10,950 petitions (representing 30.5 million signatures). We extract the main issues with a ground-up natural language processing (NLP) method, latent Dirichlet allocation (LDA). We then investigate their temporal dynamics and geographic features. We show that whilst the popularity of some issues is stable across the two years, others are highly influenced by external events, such as the referendum in June 2016. We also study the relationship between petitions' issues and where their signatories are geographically located. We show that some issues receive support from across the whole country but others are far more local. We then identify six distinct clusters of constituencies based on the issues which constituents sign. Finally, we validate our approach by comparing the petitions' issues with the top issues reported in Ipsos MORI survey data. These results show the huge power of computationally analyzing petitions to understand not only what issues citizens are concerned about but also when and from where.Comment: Preprint; under revie

An empirical exploration of requirements engineering for hybrid products

In this paper we report on an empirical study on requirements engineering of hybrid products. Hybrid products (often also referred to as product service systems) – a combination of product, software and service elements – are an emerging trend on the market. Companies intend to offer holistic solutions for customer problems and not single products. The development of hybrid products differs from the development of “classic” products because of the high-level of technological integration of the elements that hybrid products consist of, the interdisciplinarity and the different lifecycles of their single components. We have conducted fifteen expert interviews to explore current practices in requirements engineering in three industries developing hybrid products: automotive, IT-consulting and system integrators, and medical technology. Our results show that most components of hybrid products are developed independently from each other. Based on our empirical insights we have identified requirements and challenges for the design of an integrated requirements engineering process for hybrid products

Sendero: An Extended, Agent-Based Implementation of Kauffman's NKCS Model

The idea of agents exploring a fitness landscape in which they seek to move from 'fitness valleys' to higher 'fitness peaks' has been presented by Kauffman in the NK and NKCS models. The NK model addresses single species while the NKCS extension illustrates coevolving species on coupled fitness landscapes. We describe an agent-based simulation (Sendero), built in Repast, of the NK and NKCS models. The results from Sendero are validated against Kauffman's findings for the NK and NKCS models. We also describe extensions to the basic model, including population dynamics and communication networks for NK, and directed graphs and variable change rates for NKCS. The Sendero software is available as open source under the BSD licence and is thus available for download and extension by the research community.Coevolution, Agent-Based Modelling, NK, NKCS, Fitness Landscape

Directions in abusive language training data, a systematic review: Garbage in, garbage out

Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets

Persuasion: an analysis and common frame of reference for IS research

Information Systems (IS) researchers persistently examine how Information and Communications Technology (ICT) changes attitudes and behaviours but rarely leverage the persuasion literature when doing so. The hesitance of IS researchers to leverage persuasion literature may be due to this literature’s well-documented complexity. This study aims to reduce the difficulty of understanding and applying persuasion theory within IS research. The study achieves this aim by developing a common frame of reference to help IS researchers to conceptualise persuasion and to conceptually differentiate persuasion from related concepts. In doing this, the study also comprehensively summarises existing research and theory and provides a set of suggestions to guide future IS research into persuasion and behaviour change

Investment appraisal and evaluation: preserving tacit knowledge and competitive advantage

This research asks if intuitive investment appraisal and evaluation are appropriate under conditions of rapid change, uncertain outcomes, limited information, and when competitive advantage derives from tacit knowledge. Measures and rational approaches to appraisal and evaluation require distal knowledge made explicit in documents and techniques. Converting valuable tacit knowledge, residing in individuals and organisational context, into coded distal knowledge, which is more easily replicated, risks jeopardising the uniqueness of competencies and capabilities that underpin competitive advantage. The research investigates e-learning projects in higher education and finds little evidence of formal rational investment appraisal and evaluation in IS projects characterised by uncertainty and a lack of clear information