Academic Analytics in quality assurance using organisational analytical capabilities

The combination of increased environmental complexity and greater quantities of data presents higher education with new problems. Institutions have responded by adopting business analytics approaches from the commercial sector. These approaches, applied in higher education as academic analytics or learning analytics, are designed to improve organisational and educational effectiveness. However, despite extensive research in academic analytics there is an identified need for further work in making analytics “actionable”, a problem of ‘IT in use’. Recent research in business analytics has investigated this problem using a business process orientation combined with an examination of business capabilities for analytics use. Adopting this perspective we apply it to academic analytics in the context of quality assurance, describing an outline approach to the problem of actionable academic analytics

Artificial intelligence and UK national security: Policy considerations

RUSI was commissioned by GCHQ to conduct an independent research study into the use of artificial intelligence (AI) for national security purposes. The aim of this project is to establish an independent evidence base to inform future policy development regarding national security uses of AI. The findings are based on in-depth consultation with stakeholders from across the UK national security community, law enforcement agencies, private sector companies, academic and legal experts, and civil society representatives. This was complemented by a targeted review of existing literature on the topic of AI and national security. The research has found that AI offers numerous opportunities for the UK national security community to improve efficiency and effectiveness of existing processes. AI methods can rapidly derive insights from large, disparate datasets and identify connections that would otherwise go unnoticed by human operators. However, in the context of national security and the powers given to UK intelligence agencies, use of AI could give rise to additional privacy and human rights considerations which would need to be assessed within the existing legal and regulatory framework. For this reason, enhanced policy and guidance is needed to ensure the privacy and human rights implications of national security uses of AI are reviewed on an ongoing basis as new analysis methods are applied to data

Redistributed manufacturing in healthcare: Creating new value through disruptive innovation

The RiHN White Paper is the first serious attempt to gather expertise and to explore applications in promising areas of healthcare that could benefit from RDM and covers early-stage user needs, challenges and priorities. The UK has an opportunity to lead in this area and RiHN has identified an extensive number of areas for fruitful R&D, crossing production technology, infrastructure, business and organisations. The paper serves as a foundation for discussing future technological roadmaps and engaging the wider community and stakeholders, as well as policy makers, in addressing the potential impact of RDM.The RiHN White Paper is of particular value to policy makers and funders seeking to specify action and to direct attention where it is needed. The White Paper is also useful for the research community, to support their proposals with credible research propositions and to show where collaboration with industry and the public sector will deliver the most benefits.In order to seize the opportunities presented by RDM RiHN proposes a bold new agenda that incorporates a whole healthcare system view of future implementation pathways and wider transformation implications. The priority areas for Future R&D can be summarised as follows: throughAutomated production platform technologies and supporting manufacturing infrastructuresAdvances in analytics and metrologyNew regulatory frameworks and governance pathwaysNew frameworks for business model and organisational transformationThe time to take action is now. Technologies are developing that have the potential to disrupt traditional healthcare pathways and offer therapies tailored to individual needs and physiological characteristics. The challenge is seizing this opportunity and make the UK a world leader in RDM

Improving Consulting Processes in Web Analytics: A Framework for Multichannel Analytics

To control and optimise their marketing activities, organisations analyse customer behaviour on their online and offline channels. This is referred to as multichannel analytics (MCA). As enterprises often do not have the necessary know-how to implement analytics processes, analytics consultants support them in such projects. The problem for the consultants is that a standardised approach, which provides orientation and guidance during such projects, is currently not available. The goal of this paper is to develop a framework, which guides consultants in order to avoid common project-related problems. It is developed employing Design Science Research Methodology. Empirical data collection and iterative validation of the framework are based on literature research, document analysis, expert interviews and a focus group. Results highlight that it is useful to combine a capability maturity model and an analytics procedure model. This allows taking into account the different degrees of organisational maturity during the consulting process

Digitalisation of Development and Supply Networks: Sequential and Platform-Driven Innovations

We draw from an eight-year dataset of 98 organisational entities involved in pre-competitive innovation networks across the UK pharmaceutical sector. These data map into three networks that are representative of: (i) a product development-led sequential pathway that begins with digitalised product development, followed by digitalisation of supply networks, (ii) a supply network-led sequential pathway that starts with digitalised supply networks, followed by digitalisation of product development, and (iii) a parallel — platform-driven — pathway that enables simultaneous digitalisation of development, production, and supply networks. We draw upon extant literature to assess these network structures along three dimensions — strategic intent, the integrative roles of nodes with high centrality, and innovation performance. We conduct within-case and cross-case analyses to postulate 10 research propositions that compare and contrast modalities for sequential and platform-based digitalisation involving collaborative innovation networks. With sequential development, our propositions are congruent with conventional pathways for mitigating innovation risks through modular moves. On the other hand, we posit that platform-based design rules, rather than modular moves, mitigate the risks for parallel development pathways, and lead to novel development and delivery mechanisms

Technical factors for implementing SOA-Based business intelligence architecture : an exploratory study

Business intelligence (BI) architecture based on service-oriented architecture (SOA) concept enables enterprises to deploy agile and reliable BI applications. However, the key factors for implementing a SOA-based BI architecture from technical perspectives have not yet been systematically investigated. Most of the prior studies focus on organisational and managerial perspectives rather than technical factors. Therefore, this study explores the key technical factors that are most likely to have an impact on the implementation of a SOA-based BI architecture. This paper presents a conceptual model of BI architecture built on SOA concept. Drawing on academic and practitioner literature related to SOA and software architectural design, we propose fourteen key factors that may influence the implementation of a SOA-based BI architecture. This study bridges the gap between academic and practitioners.<br /

Understanding Effective Use of Big Data: Challenges and Capabilities (A Management Perspective)

While prior research has provided insights into challenges and capabilities related to effective Big Data use, much of this contribution has been conceptual in nature. The aim of this study is to explore such challenges and capabilities through an empirical approach. Accordingly, this paper reports on a multiple case study approach, involving eight organizations from the private and public sectors. The study provides empirical support for capabilities and challenges identified through prior research and identifies additional insights viz. problem-driven approach, time to value, data readiness, data literacy, data misuse, operational agility, and organizational maturity assessment

Roadmap for implementing business intelligence systems in Higher Education Institutions: exploratory work

Higher Education Institutions must define and monitor strategies and policies essential for decision-making in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the first instance, to identify and characterise the strategic objectives that underpin decision-making, activities, processes, indicators and information in Higher Education Institutions. After a literature review, it was found that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data and information available in the various systems and platforms.This work has received funding from FEDER Funds through COMPETE program and from National Funds through Portugal 2020 under the project "SATDAP - Capacitação da Administração Pública operation BI@UTAD", grant number POCI-05- 5762-FSE-000264. The authors acknowledge the work facilities and equipment provided by CeDRI (UIDB/05757/2020 and UIDP/05757/2020) to the project team.info:eu-repo/semantics/publishedVersio