Developing Strategic Capability through Business Intelligence Applications: A case study from the German Healthcare Insurance Industry

Wynn, M. and Brinkmann, D., (2018), in Yeoh, W. and Miah, S. (eds) Business Intelligence in Organisational Settings, IGI-Global. Company performance can be measured at all levels across an organisation, and in the German healthcare industry, Business Intelligence systems play a crucial role in achieving this. For one major health insurance company (discussed here as an alias - AK Healthcare), the deployment of Business Intelligence applications has supported sustained growth in turnover and market share in the past five years. In this article, these tools are classified within an appropriate conceptual framework which encompasses the organisation’s information infrastructure and associated processes. Different components of the framework are identified and examples are given - systems infrastructure, data provision/access control, the BI tools and technologies, report generation, and information users. The use and integration of Business Intelligence tools in the strategy development process is then analyzed, and the key functions and features of these tools for strategic capability development are discussed. Research findings encompass system access, report characteristics, and end-users capabilities

Ecosystemic Evolution Feeded by Smart Systems

Information Society is advancing along a route of ecosystemic evolution. ICT and Internet advancements, together with the progression of the systemic approach for enhancement and application of Smart Systems, are grounding such an evolution. The needed approach is therefore expected to evolve by increasingly fitting into the basic requirements of a significant general enhancement of human and social well-being, within all spheres of life (public, private, professional). This implies enhancing and exploiting the net-living virtual space, to make it a virtuous beneficial integration of the real-life space. Meanwhile, contextual evolution of smart cities is aiming at strongly empowering that ecosystemic approach by enhancing and diffusing net-living benefits over our own lived territory, while also incisively targeting a new stable socio-economic local development, according to social, ecological, and economic sustainability requirements. This territorial focus matches with a new glocal vision, which enables a more effective diffusion of benefits in terms of well-being, thus moderating the current global vision primarily fed by a global-scale market development view. Basic technological advancements have thus to be pursued at the system-level. They include system architecting for virtualization of functions, data integration and sharing, flexible basic service composition, and end-service personalization viability, for the operation and interoperation of smart systems, supporting effective net-living advancements in all application fields. Increasing and basically mandatory importance must also be increasingly reserved for human–technical and social–technical factors, as well as to the associated need of empowering the cross-disciplinary approach for related research and innovation. The prospected eco-systemic impact also implies a social pro-active participation, as well as coping with possible negative effects of net-living in terms of social exclusion and isolation, which require incisive actions for a conformal socio-cultural development. In this concern, speed, continuity, and expected long-term duration of innovation processes, pushed by basic technological advancements, make ecosystemic requirements stricter. This evolution requires also a new approach, targeting development of the needed basic and vocational education for net-living, which is to be considered as an engine for the development of the related ‘new living know-how’, as well as of the conformal ‘new making know-how’

Exploiting Business Intelligence for Strategic Knowledge Management: A German Healthcare Insurance Industry Case Study

In the German healthcare industry, Business Intelligence systems play a cru-cial role. For one major health insurance company (discussed here as an alias - AK Healthcare), the deployment of Business Intelligence applications has supported sustained growth in turnover and market share in the past five years. In this article, these tools are classified within an appropriate conceptual framework which encompasses the organisation’s information infrastructure and associated processes. Different components of the framework are identified and examples are given - systems infrastructure, data provision/access control, the BI tools and technologies themselves, report generation, and in-formation users. The use and integration of Business Intelligence tools in the strategy development process is then analyzed. Finally, the key functions and features of these tools for strategic knowledge management are discussed. Research findings encompass system access, report characteristics, and end users profiles and capabilitie

Big data : evolution, components, challenges and opportunities

Thesis (S.M. in Management of Technology)--Massachusetts Institute of Technology, Sloan School of Management, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 122-126).This work reviews the evolution and current state of the "Big Data" industry, and to understand the key components, challenges and opportunities of Big Data and analytics face in today business environment, this is analyzed in seven dimensions: Historical Background. The historical evolution and milestones in data management that eventually led to what we know today as Big Data. What is Big Data? Reviews the key concepts around big data, including Volume, Variety, and Velocity, and the key components of successful Big Data initiatives. Data Collection. The most important issue to consider before any big data initiative is to identify the "Business Case" or "Question" we want to answer, no "big data" initiative should be launched without clearly identify the business problem we want to tackle. Data collection strategy has to be closely defined taking in consideration the business case in question. Data Analysis. This section explores the techniques available to create value by aggregate, manipulate, analyze and visualize big data. Including predictive modeling, data mining, and statistical inference models. Data Visualization. Visualization of data is one of the most powerful and appealing techniques for data exploration. This section explores the main techniques for data visualization so that the characteristics of the data and the relationships among data items can be reported and analyzed. Impact. This section explores the potential impact and implications of big data in value creation in five domains: Insurance, Healthcare, Politics, Education and Marketing. Human Capital. This chapter explores the way big data will influence business processes and human capital, explore the role of the "Data Scientist" and analyze a potential shortage of data experts in coming years. Infrastructure and Solutions. This chapter explores the current professional services and infrastructure offering and how this industry and makes a review of vendors available in different specialties around big data.by Alejandro Zarate Santovena.S.M.in Management of Technolog

Data science for industry 4.0 and sustainability: a survey and analysis based on open data

The last few years have been marked by the transition of companies and organizations to more efficient, productive and leaner practices in their processes and systems. In the spectrum of Industry and Engineering, the successful transition to Industry 4.0 is a clear goal for many Small and Medium Enterprises (SMEs) and bigger-sized companies. However, there are economic, social and environmental challenges for this transition that require innovative approaches to overcome them. The starting point for the development of this dissertation is exploring the importance of Data as a crucial resource and Data-science as a tool for companies, organizations and even public institutions to achieve innovative solutions through collaboration. As it will be further explained, Data is essential in decision making, but in many cases, organizations can’t access relevant information and tools because they are either proprietary or because there is a lack of collaboration between them and third parties. There is a common misconception that competition between companies within the same industry prohibits them from collaborating with each other. However, many times data-sharing and collaborative approaches can actually benefit both of them, increase the market they operate in, and accelerate innovation. Even though the adoption of Industry 4.0 has been already underway, this transition cannot be considered successful unless it improves sustainability across the economic, social and environmental areas of society. Those three sustainable pillars should always be considered a priority in the research of industrial and engineering evolution. Today, more than ever before, information about those topics is widely available but there is still a lack of interest by scientists and scholars in studying some of them. The following research aims to study Industry 4.0 and Sustainability themes through Data Science by incorporating open data and leveraging open-source tools in order to achieve Sustainable Industry 4.0. For that, studying the trends and current state of Industry 4.0, Sustainability and open data in the world, as well as identifying the industries, regions, and enterprises that benefit the most from Industry 4.0 adoption, and understanding if openness of data has a positive impact on Social Sustainability are the main objectives of the study. For that are used methods such as SLR (Sistematic Literature Review) in the bibliographic review and quantitative analysis through open-source software such as Python and R in the development of the research. The main results show a positive trend in Industry 4.0 adoption through sustainable practices, mainly on developed countries, and a growing trend of openness of data, which can be positive for transparency in both Industry and Sustainability.Os últimos anos têm sido marcados pela transição por parte de empresas e organizações para práticas mais eficientes, produtivas e de menores desperdícios nos seus processos e sistemas. No espectro da Indústria e Engenharia, a transição bem sucedida para a Indústria 4.0 é um objetivo claro por várias Pequenas e Médias Empresas (PMEs) e também por empresas maiores. No entanto, existem desafios de cariz económico, social e ambiental para esta transição, que requerem abordagens inovadoras para que os mesmos sejam ultrapassados. O ponto de partida para o desenvolvimento desta dissertação passou por explorar a importância de Dados como um recurso crucial e da Ciência de Dados como uma ferramenta para empresas, organizações e até mesmo instituições públicas atingirem soluções inovadoras através de colaboração. Como será explicado ao longo da dissertação, os dados são essenciais em tomadas de decisão, mas em muitos casos, as organizações não conseguem aceder a informação ou ferramentas relevantes porque ou são proprietárias, ou porque existe a falta de colaboração entre elas e terceiros. Existe também o conceito errado de que a competição entre empresas numa dada indústria as proíbe de colaborarem entre si. No entanto, muitas vezes a partilha de informação e abordagens colaborativas podem, na verdade, beneficiar ambas, expandindo o mercado onde operam e acelerando inovação. Apesar da adoção da Indústria 4.0 estar em progresso, esta transição não pode ser considerada bem sucedida se não melhorar a sustentabilidade nas áreas económicas, sociais e ambientais da sociedade. Esses três pilares da sustentabilidade devem ser considerados uma prioridade no estudo da evolução industrial e da engenharia. Hoje, mais do que nunca, a informação acerca desses tópicos é facilmente acedida, mas continua a existir interesse por parte de cientistas e académicos no estudo de alguns deles. A presente pesquisa tenciona estudar a Indústria 4.0 e temas de Sustentabilidade através de Ciência de Dados, incorporando dados abertos e explorando ferramentas open-source, para contribuir para uma Indústria 4.0 Sustentável. Para tal, estudar a tendência e estado atual da Indústria 4.0, Sustentabilidade e abertura de dados no mundo, assim como identificar as indústrias, regiões e empresas que mais beneficiam desta adoção, e finalmente compreender se uma maior abertura de dados pode ter um impacto positivo na Sustentabilidade Social são os principais objetivos do estudo. Assim, são usados métodos como RSL (Revisão Sistemática da Literatura) na revisão bibliográfica e análise quantitativa através de software open-source como o Python e R nos capítulos de desenvolvimento. Os principais resultados mostram uma tendência positiva na adoção da Indústria 4.0 através de praticas sustentáveis, principalmente em países desenvolvidos, e uma tendência crescente na abertura de dados, que pode ser positiva para uma indústria mais sustentável e transparente

Factors Influencing Willingness To Adopt Advanced Analytics In Small Businesses

Business analytics (BA) continues to be one of the top technology trends in recent years as well as one of the top priorities for CIO’s in many large enterprises. Business analytic tools can significantly help small businesses in quickly responding to changing market conditions and improving their organizational performance. However, prior studies report that the adoption rate of business analytics in small businesses is extremely low such that only 32 percent small businesses have adopted Business Intelligence (BI) and analytics solutions till now (SMB Group, 2018). As small businesses constitute a major force in the US economy, a slow rate of adoption of significant technological innovations, such as BA, may be a critical concern that can affect the economy in the longer run. Despite this, the extant small business literature as well as the information systems literature fails to provide an understanding of why small businesses are not receptive to current BA trends. Therefore, drawing upon the theoretical underpinnings of organizing vision theory, strategic orientation literature, and theory of upper echelon, this study investigates the willingness of small businesses to adopt newer innovations in BA. More specifically, this study investigates the impact of the reception of organizing vision of BA by owner managers, learning orientation of small businesses, analytics orientation of small businesses, and personal characteristics of owner-mangers on small businesses’ willingness to adopt BA. By drawing its motivation from prior strategic orientation and v BA literature, this study is also among the first one to propose, formally develop, and validate the measurement construct of analytics orientation

Digitale Transformation aus unternehmensübergreifender Perspektive: Management der Koevolution von Plattformbesitzern und Komplementoren in Plattformökosystemen

Digital platforms have the potential to transform how organizations are doing business in their respective ecosystems. Motivated by this transformation, the purpose of this thesis is to increase the understanding of digital transformation from an inter-organizational perspective. Therefore, this thesis clarifies the phenomenon of digital transformation, and models and analyzes multiple digital platform ecosystems. Building upon that, this dissertation reflects on multiple case studies on how platform owners can manage the co-evolution of their complementors in digital transformations in digital platform ecosystems.Digitale Plattformen haben das Potential, die Art und Weise, wie Unternehmen in ihren jeweiligen Ökosystemen Geschäfte machen, zu verändern. Motiviert durch diese Transformation, ist das Ziel dieser Arbeit, das Verständnis von digitaler Transformation aus einer inter-organisatorischen Perspektive zu erhöhen. Daher erläutert diese Arbeit das Phänomen der digitalen Transformation, und modelliert und analysiert mehrere digitale Plattformökosysteme. Darauf aufbauend reflektiert diese Dissertation in mehreren Fallstudien darüber, wie Plattformbesitzer die Koevolution ihrer Komplementoren in digitalen Transformationen in digitalen Plattformökosystemen steuern können

Quantified vehicles: data, services, ecosystems

Advancing digitalization has shown the potential of so-called Quantified Vehicles for gathering valuable sensor data about the vehicle itself and its environment. Consequently, (vehicle) Data has become an important resource, which can pave the way to (Data-driven) Services. The (Data-driven Service) Ecosystem of actors that collaborate to ultimately generate services, has only shaped up in recent years. This cumulative dissertation summarizes the author's contributions and includes a synopsis as well as 14 peer-reviewed publications, which contribute to answer the three research questions.Die Digitalisierung hat das Potenzial für Quantified Vehicles aufgezeigt, um Sensordaten über das Fahrzeug selbst und seine Umgebung zu sammeln. Folglich sind (Fahrzeug-)Daten zu einer wichtigen Ressource der Automobilindustrie geworden, da sie auch (datengetriebene) Services ermöglichen. Es bilden sich Ökosysteme von Akteuren, die zusammenarbeiten, um letztlich Services zu generieren. Diese kumulative Dissertation fasst die Beiträge des Autors zusammen und enthält eine Synopsis sowie 14 begutachtete Veröffentlichungen, die zur Beantwortung der drei Forschungsfragen beitragen