Business Models For The Data Economy

Today the fastest growing companies have no physical assets. Instead, they create innovative digital products and new data-driven business models. They capture huge market share fast and their capitalizations skyrocket. The success of these digital giants is pushing all companies to rethink their business models and to start digitizing their products and services. Whether you are a new start-up building a digital product or service, or an employee of an established company that is transitioning to digital, you need to consider how digitization has transformed every aspect of management. Data-driven business models scale not through asset accumulation and product standardization, but through disaggregation of supply and demand. The winners in the new economy master the demand for one and the supply to millions. Throughout the book the author illustrates with examples and use cases how the market competition has changed and how companies adept to the new rules of the game. The economic levers of scale and scope are also different in the digital economy and companies have to learn new tactics how to achieve and sustain their competitive advantage. While data is at the core of all digital business models, the monetization strategies vary across products, services and business models. Our Monetization Matrix is a model that helps managers, marketers, sales professionals, and technical product designers to align the digital product design with the data-driven business model.

You're sitting on a pile of interesting data. How do you transform that into money? It's easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply collecting and reselling it in raw form. While that's certainly profitable right now, you'd do well to explore other opportunities if you expect to be in the data business long-term. In this paper, we'll share a framework we developed around monetizing data. We'll show you how to think beyond pure collection and storage, to move up the value chain and consider longer-term opportunities.

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.

Filling a gap in the current literature, this book addresses the social approach to the design and use of innovative business models in the digital economy. It focuses on three areas that are of increasing importance to businesses and industry today: social issues and sustainability; digitization; and new economic business models, specifically the sharing and circular economies. The authors aim to solve current scientific concerns around the conceptualization and operationalization of social business models, addressing management intentions and the impact of these models on society. Based on observation of social phenomena and the authors' research and practical experience, the book highlights best practices for designing and assessing social business models.

Annotation You're sitting on a pile of interesting data. How do you transform that into money? It's easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply collecting and reselling it in raw form. While that's certainly profitable right now, you'd do well to explore other opportunities if you expect to be in the data business long-term. In this paper, we'll share a framework we developed around monetizing data. We'll show you how to think beyond pure collection and storage, to move up the value chain and consider longer-term opportunities.

You're sitting on a pile of interesting data. How do you transform that into money? It's easy to focus on the contents of the data itself, and to succumb to the (rather unimaginative) idea of simply collecting and reselling it in raw form. While that's certainly profitable right now, you'd do well to explore other opportunities if you expect to be in the data business long-term. In this paper, we'll share a framework we developed around monetizing data. We'll show you how to think beyond pure collection and storage, to move up the value chain and consider longer-term opportunities.

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

Competitive success in the old economy was determined by accumulation, aggregation, and concentration of physical resources. Success in today's new digital economy is achieved by disaggregation and scale. The most strategic resource is data and the most important managerial tools are data management and analytics. Digital companies own no physical resources and manage less assets and people; and, yet, they achieve larger market share and higher market capitalizations. Using data to disaggregate every aspect of the business in order to manage based on micro-trends, the digital companies achieve unprecedented concentration of demand for their data products and services.