New Horizons For A Data Driven Economy
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| Author | : José María Cavanillas |
| Publisher | : Springer |
| Total Pages | : 312 |
| Release | : 2016-04-04 |
| Genre | : Computers |
| ISBN | : 3319215698 |
In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.
| Author | : Edward Curry |
| Publisher | : Springer Nature |
| Total Pages | : 399 |
| Release | : 2021-08-01 |
| Genre | : Computers |
| ISBN | : 3030681769 |
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.
| Author | : Elisabetta Di Nitto |
| Publisher | : Springer |
| Total Pages | : 154 |
| Release | : 2016-12-22 |
| Genre | : Technology & Engineering |
| ISBN | : 3319460315 |
This books is open access under a CC BY 4.0 license. This book summarizes work being undertaken within the collaborative MODAClouds research project, which aims to facilitate interoperability between heterogeneous Cloud platforms and remove the constraints of deployment, portability, and reversibility for end users of Cloud services. Experts involved in the project provide a clear overview of the MODAClouds approach and explain how it operates in a variety of applications. While the wide spectrum of available Clouds constitutes a vibrant technical environment, many early-stage issues pose specific challenges from a software engineering perspective. MODAClouds will provide methods, a decision support system, and an open source IDE and run-time environment for the high-level design, early prototyping, semiautomatic code generation, and automatic deployment of applications on multiple Clouds. It will free developers from the need to commit to a fixed Cloud technology stack during software design and offer benefits in terms of cost savings, portability of applications and data between Clouds, reversibility (moving applications and data from Cloud to non-Cloud environments), risk management, quality assurance, and flexibility in the development process.
| Author | : Luise Li Langergaard |
| Publisher | : Springer Nature |
| Total Pages | : 273 |
| Release | : 2021-12-10 |
| Genre | : Business & Economics |
| ISBN | : 3030817431 |
The edited volume New Economies for Sustainability: Limits and Potentials for Possible Futures brings together a range of alternative views on economy and organization to illustrate different perspectives on how to work towards more sustainable solutions to production, consumptions and economic organization more generally. The book brings chapters from the most renowned scholars in the field, who bring their perspectives on how alternative schools theorize politics, society, organization, nature and ethics in their attempts to develop theories with a strong focus on sustainability. The book aims to contribute with a platform for gathering and collecting these theories in a pluralist economic framework, which can provide a strong alternative voice to mainstream economic theories in sustainability debates.
| Author | : B. Joseph Pine |
| Publisher | : Harvard Business Press |
| Total Pages | : 276 |
| Release | : 1999 |
| Genre | : Business & Economics |
| ISBN | : 9780875848198 |
This text seeks to raise the curtain on competitive pricing strategies and asserts that businesses often miss their best opportunity for providing consumers with what they want - an experience. It presents a strategy for companies to script and stage the experiences provided by their products.
| Author | : Bill Schmarzo |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 261 |
| Release | : 2020-11-30 |
| Genre | : Computers |
| ISBN | : 1800569130 |
Build a continuously learning and adapting organization that can extract increasing levels of business, customer and operational value from the amalgamation of data and advanced analytics such as AI and Machine Learning Key Features Master the Big Data Business Model Maturity Index methodology to transition to a value-driven organizational mindset Acquire implementable knowledge on digital transformation through 8 practical laws Explore the economics behind digital assets (data and analytics) that appreciate in value when constructed and deployed correctly Book Description In today's digital era, every organization has data, but just possessing enormous amounts of data is not a sufficient market discriminator. The Economics of Data, Analytics, and Digital Transformation aims to provide actionable insights into the real market discriminators, including an organization's data-fueled analytics products that inspire innovation, deliver insights, help make practical decisions, generate value, and produce mission success for the enterprise. The book begins by first building your mindset to be value-driven and introducing the Big Data Business Model Maturity Index, its maturity index phases, and how to navigate the index. You will explore value engineering, where you will learn how to identify key business initiatives, stakeholders, advanced analytics, data sources, and instrumentation strategies that are essential to data science success. The book will help you accelerate and optimize your company's operations through AI and machine learning. By the end of the book, you will have the tools and techniques to drive your organization's digital transformation. Here are a few words from Dr. Kirk Borne, Data Scientist and Executive Advisor at Booz Allen Hamilton, about the book: "Data analytics should first and foremost be about action and value. Consequently, the great value of this book is that it seeks to be actionable. It offers a dynamic progression of purpose-driven ignition points that you can act upon." What you will learn Train your organization to transition from being data-driven to being value-driven Navigate and master the big data business model maturity index Learn a methodology for determining the economic value of your data and analytics Understand how AI and machine learning can create analytics assets that appreciate in value the more that they are used Become aware of digital transformation misconceptions and pitfalls Create empowered and dynamic teams that fuel your organization's digital transformation Who this book is for This book is designed to benefit everyone from students who aspire to study the economic fundamentals behind data and digital transformation to established business leaders and professionals who want to learn how to leverage data and analytics to accelerate their business careers.
| Author | : Frank Pasquale |
| Publisher | : Harvard University Press |
| Total Pages | : 320 |
| Release | : 2015-01-05 |
| Genre | : Law |
| ISBN | : 0674967100 |
Every day, corporations are connecting the dots about our personal behavior—silently scrutinizing clues left behind by our work habits and Internet use. The data compiled and portraits created are incredibly detailed, to the point of being invasive. But who connects the dots about what firms are doing with this information? The Black Box Society argues that we all need to be able to do so—and to set limits on how big data affects our lives. Hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only scratched the surface of this troubling behavior. Frank Pasquale exposes how powerful interests abuse secrecy for profit and explains ways to rein them in. Demanding transparency is only the first step. An intelligible society would assure that key decisions of its most important firms are fair, nondiscriminatory, and open to criticism. Silicon Valley and Wall Street need to accept as much accountability as they impose on others.
| Author | : Yinhai Wang |
| Publisher | : Elsevier |
| Total Pages | : 302 |
| Release | : 2018-12-04 |
| Genre | : Transportation |
| ISBN | : 0128170271 |
Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. - Synthesizes the newest developments in data-driven transportation science - Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed - Useful for both theoretical and technically-oriented researchers
| Author | : Edward Curry |
| Publisher | : Springer Nature |
| Total Pages | : 555 |
| Release | : 2022 |
| Genre | : Application software |
| ISBN | : 3030783073 |
This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part "Technologies and Methods" contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part "Processes and Applications" details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems.
| Author | : Strydom, Sheryl Kruger |
| Publisher | : IGI Global |
| Total Pages | : 320 |
| Release | : 2018-11-16 |
| Genre | : Computers |
| ISBN | : 1522570780 |
The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth. Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management.
