Understanding the Impact of Business Analytics on Innovation

The advances in Big Data and Business Analytics (BA) have provided unprecedented opportunities for organizations to innovate. With new and unique insights gained from BA, companies are able to develop new or improve existing products/services. However, few studies have investigated the mechanism through which BA contributes to a firm’s innovation success. This research aims to address this gap. From an information processing and use perspective, a research model is proposed and empirically validated with data collected from a survey with UK businesses. The evidence from the survey of 296 respondents supports the research model that provides a focused and validated view on BA’s contribution to innovation. The key findings suggest that BA directly improves environmental scanning which in turn helps to enhance a company’s innovation in terms of new product novelty and meaningfulness. However, the effect of BA’s contribution would be increased through the mediation role of data-driven culture in the organization. Data-driven culture directly impacts on new product novelty, but indirectly on product meaningfulness through environmental scanning. The findings also confirm that environmental scanning directly contributes to new product novelty and meaningfulness which in turn enhance competitive advantage. The model testing results also reveal that innovation success can be influenced by many other factors which should be addressed alongside the BA applications

Innovative Approaches in Business Development Strategies Through Artificial Intelligence Technology

This study explores the key role of artificial intelligence (AI) in changing business development strategies in an era characterized by rapid technological progress. The goal of this research is to find innovative ways to integrate artificial intelligence into corporate operational structures to increase competitiveness and efficiency. Adopting a qualitative research design, this study conducts an in-depth analysis of the literature retrieved from Google Scholar using a systematic approach to presenting data reduction and drawing conclusions. Research shows that integrating AI into business strategy improves existing operations. It emerges as a catalyst for sophisticated data analytics that drive personalized customer experiences and improve decision-making. This study highlights the transformative potential of artificial intelligence to create dynamic business environments where predictive analytics and customer insights inform strategic decisions. A key innovation of this research is its focus on the symbiotic relationship between artificial intelligence and human decision-making. This indicates a paradigm shift in the formulation of business strategy where artificial intelligence becomes not just a tool but a strategic partner that complements human ingenuity. This approach highlights the importance of connecting AI capabilities with human insights to create innovative business strategies that are responsive and adaptable. In conclusion this study provides a comprehensive understanding of the transformative impact of artificial intelligence on business development strategies. It provides valuable insights for organizations looking to use AI for strategic advantage by highlighting the need for a balanced integration of technology and human expertise in an ever-changing business environment

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

How do top- and bottom-performing companies differ in using business analytics?

Purpose Business analytics (BA) has attracted growing attention mainly due to the phenomena of big data. While studies suggest that BA positively affects organizational performance, there is a lack of academic research. The purpose of this paper, therefore, is to examine the extent to which top- and bottom-performing companies differ regarding their use and organizational facilitation of BA. Design/methodology/approach Hypotheses are developed drawing on the information processing view and contingency theory, and tested using multivariate analysis of variance to analyze data collected from 117 UK manufacture companies. Findings Top- and bottom-performing companies differ significantly in their use of BA, data-driven environment, and level of fit between BA and data-drain environment. Practical implications Extensive use of BA and data-driven decisions will lead to superior firm performance. Companies wishing to use BA to improve decision making and performance need to develop relevant analytical strategy to guide BA activities and design its structure and business processes to embed BA activities. Originality/value This study provides useful management insights into the effective use of BA for improving organizational performance

Essential Micro-foundations for Contemporary Business Operations: Top Management Tangible Competencies, Relationship-based Business Networks and Environmental Sustainability

Although various studies have emphasized linkages between firm competencies, networks and sustainability at organizational level, the links between top management tangible competencies (e.g., contemporary relevant quantitative-focused education such as big data analytics and data-driven applications linked with the internet of things, relevant experience and analytical business applications), relationship-based business networks (RBNs) and environmental sustainability have not been well established at micro-level, and there is a literature gap in terms of investigating these relationships. This study examines these links based on the unique data collected from 175 top management representatives (chief executive officers and managing directors) working in food import and export firms headquartered in the UK and New Zealand. Our results from structural equation modelling indicate that top management tangible competencies (TMTCs) are the key determinants for building RBNs, mediating the correlation between TMTCs and environmental sustainability. Directly, the competencies also play a vital role towards environmental practices. The findings further depict that relationship-oriented firms perform better compared to those which focus less on such networks. Consequently, our findings provide a deeper understanding of the micro-foundations of environmental sustainability based on TMTCs rooted in the resource-based view and RBNs entrenched in the social network theory. We discuss the theoretical and practical implications of our findings, and we provide suggestions for future research

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

EU–originated MOOCs, with focus on multi- and single-institution platforms

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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

Actionable Supply Chain Management Insights for 2016 and Beyond

The summit World Class Supply Chain 2016: Critical to Prosperity , contributed to addressing a need that the Supply Chain Management (SCM) field’s current discourse has deemed as critical: that need is for more academia-­‐industry collaboration to develop the field’s body of actionable knowledge. Held on May 4th, 2016 in Milton, Ontario, the summit addressed that need in a way that proved to be both effective and distinctive in the Canadian SCM environment. The summit, convened in partnership between Wilfrid Laurier University’s Lazaridis School of Business & Economics and CN Rail, focused on building actionable SCM knowledge to address three core questions: What are the most significant SCM issues to be confronted now and beyond 2016? What SCM practices are imperative now and beyond 2016? What are optimal ways of ensuring that (a) issues of interest to SCM practitioners inform the scholarly activities of research and teaching and (b) the knowledge generated from those scholarly activities reciprocally guide SCM practice? These are important questions for supply chain professionals in their efforts to make sense of today’s business environment that is appropriately viewed as volatile, uncertain, complex, and ambiguous. The structure of the deliberations to address these questions comprised two keynote presentations and three panel discussions, all of which were designed to leverage the collective wisdom that comes from genuine peer-­‐to-­‐peer dialogue between the SCM practitioners and SCM scholars. Specifically, the structure aimed for a balanced blend of industry and academic input and for coverage of the SCM issues of greatest interest to attendees (as determined through a pre-­‐summit survey of attendees). The structure produced impressively wide-­‐ranging deliberations on the aforementioned questions. The essence of the resulting findings from the summit can be distilled into three messages: Given today’s globally significant trends such as changes in population demographics, four highly impactful levers that SCM executives must expertly handle to attain excellence are: collaboration; information; technology; and talent Government policy, especially for infrastructure, is a significant determinant of SCM excellence There is tremendous potential for mutually beneficial industry-academia knowledge co-creation/sharing aimed at research and student training This white paper reports on those findings as well as on the summit’s success in realizing its vision of fostering mutually beneficial industry-academia dialogue. The paper also documents what emerged as matters that are inadequately understood and should therefore be targeted in the ongoing quest for deeper understanding of actionable SCM insights. Deliberations throughout the day on May 4th, 2016 and the encouraging results from the pre-­‐summit and post-­‐summit surveys have provided much inspiration to enthusiastically undertake that quest. The undertaking will be through initiatives that include future research projects as well as next year’s summit–World Class Supply Chain 2017

Ethical Implications of Predictive Risk Intelligence

open access articleThis paper presents a case study on the ethical issues that relate to the use of Smart Information Systems (SIS) in predictive risk intelligence. The case study is based on a company that is using SIS to provide predictive risk intelligence in supply chain management (SCM), insurance, finance and sustainability. The pa-per covers an assessment of how the company recognises ethical concerns related to SIS and the ways it deals with them. Data was collected through a document review and two in-depth semi-structured interviews. Results from the case study indicate that the main ethical concerns with the use of SIS in predictive risk intelli-gence include protection of the data being used in predicting risk, data privacy and consent from those whose data has been collected from data providers such as so-cial media sites. Also, there are issues relating to the transparency and accountabil-ity of processes used in predictive intelligence. The interviews highlighted the issue of bias in using the SIS for making predictions for specific target clients. The last ethical issue was related to trust and accuracy of the predictions of the SIS. In re-sponse to these issues, the company has put in place different mechanisms to ensure responsible innovation through what it calls Responsible Data Science. Under Re-sponsible Data Science, the identified ethical issues are addressed by following a code of ethics, engaging with stakeholders and ethics committees. This paper is important because it provides lessons for the responsible implementation of SIS in industry, particularly for start-ups. The paper acknowledges ethical issues with the use of SIS in predictive risk intelligence and suggests that ethics should be a central consideration for companies and individuals developing SIS to create meaningful positive change for society