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

Role clarity deficiencies can wreck agile teams

Background One of the twelve agile principles is to build projects around motivated individuals and trust them to get the job done. Such agile teams must self-organize, but this involves conflict, making self-organization difficult. One area of difficulty is agreeing on everybody’s role. Background What dynamics arise in a self-organizing team from the negotiation of everybody’s role? Method We conceptualize observations from five agile teams (work observations, interviews) by Charmazian Grounded Theory Methodology. Results We define role as something transient and implicit, not fixed and named. The roles are characterized by the responsibilities and expectations of each team member. Every team member must understand and accept their own roles (Local role clarity) and everbody else’s roles (Team-wide role clarity). Role clarity allows a team to work smoothly and effectively and to develop its members’ skills fast. Lack of role clarity creates friction that not only hampers the day-to-day work, but also appears to lead to high employee turnover. Agile coaches are critical to create and maintain role clarity. Conclusions Agile teams should pay close attention to the levels of Local role clarity of each member and Team-wide role clarity overall, because role clarity deficits are highly detrimental

Active learning based laboratory towards engineering education 4.0

Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns