The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

Data Analysis Services Related to the IoT and Big Data: Potential Business Opportunities for Third Parties

The Internet of Things (IoT) provides the tools for the development of a major, global data-driven ecosystem. When accessible to people and businesses, this information can make every area of life, including business, more data-driven. \ \ In this ecosystem, with its emphasis on Big Data, there has been a focus on building business models for the provision of services, the so-called Internet of Services (IoS). These models assume the existence and development of the necessary IoT measurement and control instruments, communications infrastructure, and easy access to the data collected and information generated by any party. \ \ Different business models may support opportunities that generate revenue and value for various types of customers. \ \ This paper contributes to the literature by considering business models and opportunities for third-party data analysis services and discusses access to information generated by third parties in relation to Big Data techniques and potential opportunities

Aligning Business Analytics Programs with Industry Required Knowledge, Skills and Abilities

This paper describes results from a topic modeling analysis of online data analytics-related job advertisements. Five distinct clusters emerged, each with a focus on different knowledge, skills and abilities (KSAs) profiles. We labelled these clusters big data, systems analysis, business, healthcare, and technical research. Identification of these clusters provides a framework that can be used by information systems and business analytics faculty to offer customized and specialized information systems and business analytics programs that prepare graduates to fill specific roles in the data ecosystem of the workplace

Value co-creation and potential benefits through big data analytics: Health Benefit Analysis

Big data analytics in healthcare context is often studied from a technical point of view. In the field of strategic management, researchers have indicated a research gap in how big data analytics create business value. This study examines how big data and advanced analytics generate potential benefits and business value for the healthcare service provider, and value for the individual patients and population health. In addition, the effects of advanced analytics to the value co-creation practices and actors in healthcare ecosystem are studied. The theoretical framework used for the purpose is the big data analytics-enabled transformation model which is adapted to answer the research questions. The study is conducted as a single case study. The studied case is the Health Benefit Analysis (HBA) tool. The empirical data is collected in eight semi-structured interviews with participants of the tool development project. Using the HBA tool reveals several paths-to-value chains. The most evident path shows how using advanced analytics affects the personalized care practice by enabling a more interactive service process between the health professionals and patients. It denotes a business scope redefinition as patients are now being interpreted as essential actors in the value co-creation of their own health outcomes. The benefits that arise from the advanced analytics are of several dimensions; operational, managerial, strategic, and organizational. Using the HBA tool generates strategic business value for the healthcare service provider as a differentiator that contributes to gaining competitive advantage compared to other service providers not using this innovation. Value emerges for the individual patient as improved patient experience and better health outcomes. Population health gains most value from the reduced health inequalities. The evolving value co-creation practices set requirements for the healthcare ecosystem actors as they need to conform to new practices with patients and other professionals from other sectors and levels of the ecosystem. The healthcare work and service culture need to develop and adapt to new tools, related processes, and a more diversified professional base, including health analysts and other new professionals. To conclude, it can be claimed that advanced analytics of healthcare big data contributes to the shift to value-based healthcare.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Digital Transformation Powered by Big Data Analytics: The Case of Retail Grocery Business

Companies are investing in big data analytics capabilities as they look for ways to understand and innovate their business models by leveraging digital transformation. We explore this phenomenon from the perspective of retail grocery business where evolving consumer attitudes and behaviors, rapid technological advances, new competitive pressures, laser thin margins, and the COVID-19 pandemic have accelerated the pace of digital transformation. We specifically analyze the role of big data analytics capabilities of the top five grocery companies in the United States in light of their digital transformation initiatives. We find that retailers are making major investments in big data analytics capabilities to power all aspects of their digital ecosystem—the online shopping experience for the digital consumer, digital store operations, pickup and delivery mechanisms—to enhance shopping experience, customer loyalty, revenue, and ultimately profit

Platform-Based Business Models: Insights from an Emerging Ai-Enabled Smart Building Ecosystem

Artificial intelligence (AI) is emerging to become a highly potential enabling technology for smart buildings. However, the development of AI applications quite often follows a traditional, closed, and product-oriented approach. This study aims to introduce the platform model and ecosystem thinking to the development of AI-enabled smart buildings. The study identifies the needs for a user-oriented digital service ecosystem and business model in the smart building sector in Finland, which aimed to facilitate the launch of scalable businesses and an experiential and dynamic business ecosystem. A multi-method, interpretive case study was applied in the focal ecosystem, with the leading real estate and facility management operators in Northern Europe as part of a Finnish national innovation project. Our results propose an extended comprehensive framework of the 5C ecosystemic model (Connection, Content, Computation, Context, and Commerce) and the possible paths of ecosystem players in the domain of smart building and smart built environment, both theoretically and empirically. The platform-oriented business models are missing, yet desired, by the ecosystem actors. The value chain and ecosystem platforms imply the quest for new (platform) models. Finally, our research discusses the need for new value-chain- and ecosystem-oriented AI development and big data platforms in the future.Keywords: smart building; artificial intelligence; platform; ecosystem; business model</div

Ecosystem of Gig-economics and entrepreneurial university: evolutionary synergy of “virus innovation” and “digital jump”

У статті досліджено екосистему інноваційно-підприємницького університету, що функціонує в системі координат гіг-економіки та розкрито переваги, які надає екосистема університету і її подальший розвиток на засадах конкуренції. Визначено ефективну роль функціонування екосистеми гіг-економіки в ході цифровізації економіки України. Представлено мету, яку переслідує екосистема інноваційно-підприємницького університету на основі реалізації стратегії цифрових рішень екосистеми гіг-економіки. Запропоновано авторське бачення та осмислення екосистеми гіг-економіки і її науково-освітні, техніко-технологічні, соціально-екологічні, інноваційно-підприємницькі структурні складники, серед чого: кластерне, платформине, екосистемне виробництво; фундація інноваційні парків, що працює за повним циклом; кластер коворкінгів (co-working-офіс: start-up-школа, start-up лабораторія, start-up-акселератор, майстер-класи експертів бізнес-шкіл); STEM-освіта; цифрова освіта (школа Big Data, школа BlockChain, школа AI, школа FinTech, бізнес табори); Індустрія 4.0: Бізнес для Smart city, Smart-підприємство, промислові Hightech, RetailTech, LegalTech, InsurTech, GovTech, IoT. Аргументовано факт, що структура екосистеми гіг-економіки під впливом відкритих інновацій, ціни на послуги, цифрове підприємництво, доступу до широкосмугового Інтернету, набуває вигляду ланцюга типу: “Розробники – Власники – Провайдери – Рекламодавці – Регулятори – Користувачі”. В результаті цього автори дійшли висновку, що стратегія цифрових рішень екосистеми університету є такою, що орієнтована на продукти/послуги з додаванням інформації, яка забезпечує нову вартість для клієнтів. Роботу з інформацією в інноваційно-підприємницькому університеті можна представити ланцюгом типу: “пошук – отримання – розпізнання – аналіз – фільтрація – збагачення – конструювання інформації – застосування”. Авторами висловлено думку про те, що умовами конкурентоспроможності інноваційної екосистеми гіг-економіки можна вважати: корпоративне стартап співробітництво; цільове фінансування інновацій; узгодженна співпраця уряду і суспільства та їх повна долученість до ефективної роботи екосистеми; підтримка підприємницького таланту та гендерна рівність; цифрова сумісність господарюючих суб’єктів; гармонізація законодавства та стандартів.The article explores the ecosystem of the innovation-entrepreneurial university, which functions in the coordinate system of gig-economy and reveals the advantages that the ecosystem of the university and its further development on the basis of competition. The effective role of functioning of gig-economy in the course of digitization of the Ukrainian economy is determined. The goal pursued by the ecosystem of the innovation and entrepreneurial university on the basis of the implementation of digital ecosystem strategy of gig-economy is presented. Author’s vision and understanding of the ecosystem of gig-economy and its scientific and educational, technical, technological, socio-ecological, innovative and entrepreneurial structural components are offered, including: cluster, platform, ecosystem production; the foundation of innovative parks, working on a full cycle; coworking cluster (co-working-office: start-up-school, start-up laboratory, start-up-accelerator, master-classes of experts of business schools); STEM education; digital education (Big Data school, BlockChain school, AI school, FinTech school, business camps); Industry 4.0: Business for Smart City, Smart Enterprise, Industrial Hightech, RetailTech, LegalTech, InsurTech, GovTech, IoT. The fact that the structure of the ecosystem of gig-economy is influenced by open innovation, prices for services, digital entrepreneurship, access to broadband Internet, takes the form of a chain of type: “Developers – Owners – ISPs – Advertisers – Regulators – Users”. As a result, authors concluded that the university’s digital ecosystem strategy is product/service oriented with value added information that delivers new value for customers. Work with information at the university of innovation and entrepreneurship can be represented by a chain of type: “search – receive – recognition – analysis – filtering – enrichment – construction of information – application”. Authors suggested that conditions of competitiveness of the innovative ecosystem of gig-economy could be considered: corporate startup cooperation; targeted financing of innovations; coordinated cooperation between government and society and their full involvement in the effective functioning of the ecosystem; support for entrepreneurial talent and gender equality; digital compatibility of business entities; harmonization of legislation and standards

Development of a supervisory internet of things (IoT) system for factories of the future

Big data is of great importance to stakeholders, including manufacturers, business partners, consumers, government. It leads to many benefits, including improving productivity and reducing the cost of products by using digitalised automation equipment and manufacturing information systems. Some other benefits include using social media to build the agile cooperation between suppliers and retailers, product designers and production engineers, timely tracking customers’ feedbacks, reducing environmental impacts by using Internet of Things (IoT) sensors to monitor energy consumption and noise level. However, manufacturing big data integration has been neglected. Many open-source big data software provides complicated capabilities to manage big data software for various data-driven applications for manufacturing. In this research, a manufacturing big data integration system, named as Data Control Module (DCM) has been designed and developed. The system can securely integrate data silos from various manufacturing systems and control the data for different manufacturing applications. Firstly, the architecture of manufacturing big data system has been proposed, including three parts: manufacturing data source, manufacturing big data ecosystem and manufacturing applications. Secondly, nine essential components have been identified in the big data ecosystem to build various manufacturing big data solutions. Thirdly, a conceptual framework is proposed based on the big data ecosystem for the aim of DCM. Moreover, the DCM has been designed and developed with the selected big data software to integrate all the three varieties of manufacturing data, including non-structured, semi-structured and structured. The DCM has been validated on three general manufacturing domains, including product design and development, production and business. The DCM cannot only be used for the legacy manufacturing software but may also be used in emerging areas such as digital twin and digital thread. The limitations of DCM have been analysed, and further research directions have also been discussed

A Framework to Build a Big Data Ecosystem Oriented to the Collaborative Networked Organization

A Collaborative Networked Organization (CNO) is a set of entities that operate in heterogeneous contexts and aim to collaborate to take advantage of a business opportunity or solve a problem. Big data allows CNOs to be more competitive by improving their strategy, management and business processes. To support the development of big data ecosystems in CNOs, several frameworks have been reported in the literature. However, these frameworks limit their application to a specific CNO manifestation and cannot conduct intelligent processing of big data to support decision making at the CNO. This paper makes two main contributions: (1) the proposal of a metaframework to analyze existing and future frameworks for the development of big data ecosystems in CNOs and (2) to show the Collaborative Networked Organizations–big data (CNO-BD) framework, which includes guidelines, tools, techniques, conceptual solutions and good practices for the building of a big data ecosystem in different kinds of Collaborative Networked Organizations, overcoming the weaknesses of previous issues. The CNO-BD framework consists of seven dimensions: levels, approaches, data fusion, interoperability, data sources, big data assurance and programmable modules. The framework was validated through expert assessment and a case study