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| data driven business models: Data-Driven Business Models for the Digital Economy Rado Kotorov, 2020-04-21 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. |
| data driven business models: Data-Driven Business Models for the Digital Economy Rado Kotorov, 2019-12-15 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. |
| data driven business models: The Elements of Big Data Value Edward Curry, Andreas Metzger, Sonja Zillner, Jean-Christophe Pazzaglia, Ana García Robles, 2021-08-01 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 driven business models: Creating a Data-Driven Organization Carl Anderson, 2015-07-23 What do you need to become a data-driven organization? Far more than having big data or a crack team of unicorn data scientists, it requires establishing an effective, deeply-ingrained data culture. This practical book shows you how true data-drivenness involves processes that require genuine buy-in across your company ... Through interviews and examples from data scientists and analytics leaders in a variety of industries ... Anderson explains the analytics value chain you need to adopt when building predictive business models--Publisher's description. |
| data driven business models: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 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. |
| data driven business models: Digital Business Models in Industrial Ecosystems Kai-Ingo Voigt, Julian M. Müller, 2021-10-20 In recent years, digital business models have frequently been the subject of academic and practical discourse. The increasing interconnectivity across the entire supply chain, which is subsumed under the term Industry 4.0, can unlock even farther-reaching potentials for digital business models, affecting entire supply chains and ecosystems. This book examines the specific challenges and obstacles that supply chain and ecosystem management poses with regard to the development of digital business models. The top-quality contributions gathered here focus on the successful implementation of Industry 4.0 in digital business models for industrial organizations in a European context, making the book a valuable asset for researchers and practitioners alike. |
| data driven business models: Data-Driven Business Decisions Chris J. Lloyd, 2011-10-25 A hands-on guide to the use of quantitative methods and software for making successful business decisions The appropriate use of quantitative methods lies at the core of successful decisions made by managers, researchers, and students in the field of business. Providing a framework for the development of sound judgment and the ability to utilize quantitative and qualitative approaches, Data Driven Business Decisions introduces readers to the important role that data plays in understanding business outcomes, addressing four general areas that managers need to know about: data handling and Microsoft Excel, uncertainty, the relationship between inputs and outputs, and complex decisions with trade-offs and uncertainty. Grounded in the author's own classroom approach to business statistics, the book reveals how to use data to understand the drivers of business outcomes, which in turn allows for data-driven business decisions. A basic, non-mathematical foundation in statistics is provided, outlining for readers the tools needed to link data with business decisions; account for uncertainty in the actions of others and in patterns revealed by data; handle data in Excel; translate their analysis into simple business terms; and present results in simple tables and charts. The author discusses key data analytic frameworks, such as decision trees and multiple regression, and also explores additional topics, including: Use of the Excel® functions Solver and Goal Seek Partial correlation and auto-correlation Interactions and proportional variation in regression models Seasonal adjustment and what it reveals Basic portfolio theory as an introduction to correlations Chapters are introduced with case studies that integrate simple ideas into the larger business context, and are followed by further details, raw data, and motivating insights. Algebraic notation is used only when necessary, and throughout the book, the author utilizes real-world examples from diverse areas such as market surveys, finance, economics, and business ethics. Excel® add-ins StatproGo and TreePlan are showcased to demonstrate execution of the techniques, and a related website features extensive programming instructions as well as insights, data sets, and solutions to problems included in the material. Data Driven Business Decisions is an excellent book for MBA quantitative analysis courses or undergraduate general statistics courses. It also serves as a valuable reference for practicing MBAs and practitioners in the fields of statistics, business, and finance. |
| data driven business models: IT-Driven Business Models Henning Kagermann, Hubert Osterle, John M. Jordan, 2010-11-09 A look at business model innovation's crucial role in today's global business environment. Showing organizations how business model innovation should be a key focus area in today's global economy, this book features cases from businesses around the globe that have developed customized business models and achieved spectacular levels of performance. Case examples from well-known innovation leaders IKEA, Apple, Tata, SHARP, Saudi Aramco, De Beers, Telefonica, Valero Energy, LEGO, and Proctor & Gamble Shows businesses how to get beyond traditional business models to take better advantage of emerging opportunities Coauthored by former CEO of SAP AG, the world's largest provider of enterprise software Filled with interviews with key executives, this book reveals the role of technology in driving and enabling changes to fundamental facets of a business. Companies around the world are innovating their business models with tremendous results. IT-Driven Business Models shows interested organizations how they can start the process. |
| data driven business models: The Risk-Driven Business Model Karan Girotra, Serguei Netessine, 2014-06-10 How to outsmart risk Risk has been defined as the potential for losing something of value. In business, that value could be your original investment or your expected future returns. The Risk-Driven Business Model will help you manage risk better by showing how the key choices you make in designing your business models either increase or reduce two characteristic types of risk—information risk, when you make decisions without enough information, and incentive-alignment risk, when decision makers’ incentives are at odds with the broader goals of the company. Leaders who understand how the structure of their business model affects risk have the power to create wealth, revolutionize industries, and shape a better world. INSEAD’s Karan Girotra and Serguei Netessine, noted operations and innovation professors who have consulted with dozens of companies, walk you through a business model audit to determine what key decisions get made in a business, when they get made, who makes them, and why we make the decisions we do. By changing your company’s key decisions within this framework, you can fundamentally alter the risks that will impact your business. This book is for entrepreneurs and executives in companies involved in dynamic industries where the locus of risk is shifting, and includes lessons from Zipcar, Blockbuster, Apple, Benetton, Kickstarter, Walmart, and dozens of other global companies. The Risk-Driven Business Model demystifies business model risk, with clear directives aimed at improving decision making and driving your business forward. |
| data driven business models: What's Your Digital Business Model? Peter Weill, Stephanie Woerner, 2018-04-17 Digital transformation is not about technology--it's about change. In the rapidly changing digital economy, you can't succeed by merely tweaking management practices that led to past success. And yet, while many leaders and managers recognize the threat from digital--and the potential opportunity--they lack a common language and compelling framework to help them assess it and guide them in responding. They don't know how to think about their digital business model. In this concise, practical book, MIT digital research leaders Peter Weill and Stephanie Woerner provide a powerful yet straightforward framework that has been field-tested globally with dozens of senior management teams. Based on years of study at the MIT Center for Information Systems Research (CISR), the authors find that digitization is moving companies' business models on two dimensions: from value chains to digital ecosystems, and from a fuzzy understanding of the needs of end customers to a sharper one. Looking at these dimensions in combination results in four distinct business models, each with different capabilities. The book then sets out six driving questions, in separate chapters, that help managers and executives clarify where they are currently in an increasingly digital business landscape and highlight what's needed to move toward a higher-value digital business model. Filled with straightforward self-assessments, motivating examples, and sharp financial analyses of where profits are made, this smart book will help you tackle the threats, leverage the opportunities, and create winning digital strategies. |
| data driven business models: Advanced Digital Marketing Strategies in a Data-Driven Era Saura, Jose Ramon, 2021-06-25 In the last decade, the use of data sciences in the digital marketing environment has increased. Digital marketing has transformed how companies communicate with their customers around the world. The increase in the use of social networks and how users communicate with companies on the internet has given rise to new business models based on the bidirectionality of communication between companies and internet users. Digital marketing, new business models, data-driven approaches, online advertising campaigns, and other digital strategies have gathered user opinions and comments through this new online channel. In this way, companies are beginning to see the digital ecosystem as not only the present but also the future. However, despite these advances, relevant evidence on the measures to improve the management of data sciences in digital marketing remains scarce. Advanced Digital Marketing Strategies in a Data-Driven Era contains high-quality research that presents a holistic overview of the main applications of data sciences to digital marketing and generates insights related to the creation of innovative data mining and knowledge discovery techniques applied to traditional and digital marketing strategies. The book analyzes how companies are adopting these new data-driven methods and how these strategies influence digital marketing. Discussing topics such as digital strategies, social media marketing, big data, marketing analytics, and data sciences, this book is essential for marketers, digital marketers, advertisers, brand managers, managers, executives, social media analysts, IT specialists, data scientists, students, researchers, and academicians in the field. |
| data driven business models: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that. |
| data driven business models: Fail Fast, Learn Faster Randy Bean, 2021-08-31 Explore why — now more than ever — the world is in a race to become data-driven, and how you can learn from examples of data-driven leadership in an Age of Disruption, Big Data, and AI In Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, Fortune 1000 strategic advisor, noted author, and distinguished thought leader Randy Bean tells the story of the rise of Big Data and its business impact – its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI. The book looks at the impact of Big Data during a period of explosive information growth, technology advancement, emergence of the Internet and social media, and challenges to accepted notions of data, science, and facts, and asks what it means to become data-driven. Fail Fast, Learn Faster includes discussions of: The emergence of Big Data and why organizations must become data-driven to survive Why becoming data-driven forces companies to think different about their business The state of data in the corporate world today, and the principal challenges Why companies must develop a true data culture if they expect to change Examples of companies that are demonstrating data-driven leadership and what we can learn from them Why companies must learn to fail fast and learn faster to compete in the years ahead How the Chief Data Officer has been established as a new corporate profession Written for CEOs and Corporate Board Directors, data professional and practitioners at all organizational levels, university executive programs and students entering the data profession, and general readers seeking to understand the Information Age and why data, science, and facts matter in the world in which we live, Fail Fast, Learn Faster p;is essential reading that delivers an urgent message for the business leaders of today and of the future. |
| data driven business models: Demand-Driven Business Strategy Cor Molenaar, 2022-02-23 Demand-Driven Business Strategy explains the ways of transforming business models from supply driven to demand driven through digital technologies and big data analytics. The book covers important topics such as digital leadership, the role of artificial intelligence, and platform firms and their role in business model transformation. Students are walked through the nature of supply- and demand-driven models and how organizations transform from one to the other. Theoretical insights are combined with real-world application through global case studies and examples from Amazon, Google, Uber, Volvo and Picnic. Chapter objectives and summaries provide consistent structure and aid learning, whilst reflective questions encourage further thought and discussion. Comprehensive and practical, this is an essential text for advanced undergraduate and postgraduate students studying strategic management, marketing, business innovation, consumer behavior, digital transformation and entrepreneurship. |
| data driven business models: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data driven business models: A Business Analyst's Introduction to Business Analytics Adam Fleischhacker, 2020-07-20 This up-to-date business analytics textbook (published in July 2020) will get you harnessing the power of the R programming language to: manipulate and model data, discover and communicate insight, to visually communicate that insight, and successfully advocate for change within an organization. Book Description A frequent teaching-award winning professor with an analytics-industry background shares his hands-on guide to learning business analytics. It is the first textbook addressing a complete and modern business analytics workflow that includes data manipulation, data visualization, modelling business problems with graphical models, translating graphical models into code, and presenting insights back to stakeholders. Book Highlights Content that is accessible to anyone, even most analytics beginners. If you have taken a stats course, you are good to go. Assumes no knowledge of the R programming language. Provides introduction to R, RStudio, and the Tidyverse. Provides a solid foundation and an implementable workflow for anyone wading into the Bayesian inference waters. Provides a complete workflow within the R-ecosystem; there is no need to learn several programming languages or work through clunky interfaces between software tools. First book introducing two powerful R-packages - `causact` for visual modelling of business problems and `greta` which is an R interface to `TensorFlow` used for Bayesian inference. Uses the intuitive coding practices of the `tidyverse` including using `dplyr` for data manipulation and `ggplot2` for data visualization. Datasets that are freely and easily accessible. Code for generating all results and almost every visualization used in the textbook. Do not learn statistical computation or fancy math in a vacuum, learn it through this guide within the context of solving business problems. |
| data driven business models: Data Science for Business Foster Provost, Tom Fawcett, 2013-07-27 Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the data-analytic thinking necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates |
| data driven business models: The Data Driven Leader Jenny Dearborn, David Swanson, 2017-10-06 Data is your most valuable leadership asset—here's how to use it The Data Driven Leader presents a clear, accessible guide to solving important leadership challenges through human resources-focused and other data analytics. This engaging book shows you how to transform the HR function and overall organizational effectiveness by using data to make decisions grounded in facts vs. opinions, identify root causes behind your company’s thorniest problems and move toward a winning, future-focused business strategy. Realistic and actionable, this book tells the story of a successful sales executive who, after leading an analytics-driven turnaround (in Data Driven, this book’s predecessor), faces a new turnaround challenge as chief human resources officer. Each chapter features insightful commentary and practical notes on the points the story raises, guiding you to put HR analytics into action in your organization. HR and other leaders cannot afford to overlook the power and competitive advantages of data-driven decision-making and strategies. This book reflects the growing trend of CEOs choosing analytics-minded business leaders to head HR, at a time when workplaces everywhere face game-changing forces including automation, robotics and artificial intelligence. It is urgent that human resources leaders embrace analytics, not only to remain professionally relevant but also to help their organizations successfully navigate this digital transformation. HR professionals can and must: Understand essential data science principles and corporate analytics models Identify and execute effective data analytics initiatives Boost HR and company productivity and performance with metrics that matter Shape an analytics-centric culture that generates data driven leaders Most organizations capture and report data, but data is useless without analysis that leads to action. The Data Driven Leader shows you how to use this tremendous asset to lead your organization higher. |
| data driven business models: Profit Driven Business Analytics Wouter Verbeke, Bart Baesens, Cristian Bravo, 2017-10-09 Maximize profit and optimize decisions with advanced business analytics Profit-Driven Business Analytics provides actionable guidance on optimizing the use of data to add value and drive better business. Combining theoretical and technical insights into daily operations and long-term strategy, this book acts as a development manual for practitioners seeking to conceive, develop, and manage advanced analytical models. Detailed discussion delves into the wide range of analytical approaches and modeling techniques that can help maximize business payoff, and the author team draws upon their recent research to share deep insight about optimal strategy. Real-life case studies and examples illustrate these techniques at work, and provide clear guidance for implementation in your own organization. From step-by-step instruction on data handling, to analytical fine-tuning, to evaluating results, this guide provides invaluable guidance for practitioners seeking to reap the advantages of true business analytics. Despite widespread discussion surrounding the value of data in decision making, few businesses have adopted advanced analytic techniques in any meaningful way. This book shows you how to delve deeper into the data and discover what it can do for your business. Reinforce basic analytics to maximize profits Adopt the tools and techniques of successful integration Implement more advanced analytics with a value-centric approach Fine-tune analytical information to optimize business decisions Both data stored and streamed has been increasing at an exponential rate, and failing to use it to the fullest advantage equates to leaving money on the table. From bolstering current efforts to implementing a full-scale analytics initiative, the vast majority of businesses will see greater profit by applying advanced methods. Profit-Driven Business Analytics provides a practical guidebook and reference for adopting real business analytics techniques. |
| data driven business models: Business Model Innovation Nicolai J. Foss, Tina Saebi, 2015 Business model innovation is an important source of competitive advantage and corporate renewal. An increasing number of companies have to innovate their business models, not just because of competitive forces but also because of the ongoing change from product-based to service-based business models. Yet, business model innovation also involves organizational change process that challenges existing processes, structures and modes of control. This volume features thirteen chapters written by authorities on business model innovation. The specific angle, and the novel feature of this book, is to thoroughly examine the organizational dimension of business model innovation. Drawing on organizational theory and empirical observation, the contributors specifically highlight organizational design aspects of business model innovation, focusing on how reward systems, power distributions, routines and standard operating procedures, the allocation of authority, and other aspects of organizational structure and control should be designed to support the business model the firm chooses. Also discussed is how existing organizational structures, capabilities, beliefs, cultures and so on influence the firm's ability to flexibly change to new business models. |
| data driven business models: The New Oil Arent van 't Spijker, 2014 The New Oil shows how data changes the traditional business paradigm. How it impacts not just high-tech, high-profile companies, but also old-school, low-tech industries all around the world; data lives and breathes within every single company. |
| data driven business models: Data Driven Business Transformation Peter Jackson, Caroline Carruthers, 2019-05-28 OPTIMIZE YOUR BUSINESS DATA FOR FIRST-CLASS RESULTS Data Driven Business Transformation illustrates how to find the secrets to fast adaptation and disruptive origination hidden in your data and how to use them to capture market share. Digitalisation – or the Digital Revolution – was the first step in an evolving process of analysis and improvement in the operations and administration of commerce. The popular author team of Caroline Carruthers and Peter Jackson, two global leaders in data transformation and education, pick up the conversation here at the next evolutionary step where data from these digital systems generates value, and really use data science to produce tangible results. Optimise the performance of your company through data-driven processes by: Following step-by-step guidance for transitioning your company in the real world to run on a data-enabled business model Mastering a versatile set of data principles powerful enough to produce transformative results at any stage of a business’s development Winning over the hearts of your employees and influencing a cultural shift to a data-enabled business Reading first-hand stories from today’s thought leaders who are shaping data transformation at their companies Enable your company’s data to lift profits with Data Driven Business Transformation. |
| data driven business models: Big Data Analytics Soraya Sedkaoui, Mounia Khelfaoui, Nadjat Kadi, 2021-07-04 This volume explores the diverse applications of advanced tools and technologies of the emerging field of big data and their evidential value in business. It examines the role of analytics tools and methods of using big data in strengthening businesses to meet today’s information challenges and shows how businesses can adapt big data for effective businesses practices. This volume shows how big data and the use of data analytics is being effectively adopted more frequently, especially in companies that are looking for new methods to develop smarter capabilities and tackle challenges in dynamic processes. Many illustrative case studies are presented that highlight how companies in every sector are now focusing on harnessing data to create a new way of doing business. |
| data driven business models: The Business of Data Martin De Saulles, 2020-06-08 This book is about the rise of data as a driver of innovation and economic growth. It charts the evolution of business data as a valuable resource and explores some of the key business, economic and social issues surrounding the data-driven revolution we are currently going through. Readers will gain an understanding of the historical underpinnings of the data business and why the collection and use of data has been driven by commercial needs. Readers will also gain insights into the rise of the modern data-driven technology giants, their business models and the reasons for their success. Alongside this, some of the key social issues including privacy are considered and the challenges these pose to policymakers and regulators. Finally, the impact of pervasive computing and the Internet of Things (IoT) is explored in the context of the new sources of data that are being generated. This book is useful for students and practitioners wanting to better understand the origins and drivers of the current technological revolution and the key role that data plays in innovation and business success. |
| data driven business models: Knowledge Management, Innovation, and Entrepreneurship in a Changing World Jennex, Murray Eugene, 2020-03-27 In today’s world of business, gaining an advantage of competitors is a focal point for organizations and a driving force in the economy. New practices are being studied and implemented constantly by rivaling companies. Many industries have begun putting emphasis on intensive knowledge practices, with the belief that implementing cutting-edge learning practices will fuel research and innovation within the company. Understanding this dynamic method of management is critical for managers and executives who wish to propel the success of their organizations. Knowledge Management, Innovation, and Entrepreneurship in a Changing World is a collection of pioneering research on the methods of gaining organizational advantages based on knowledge innovation and management. While highlighting topics including human-robot teaming, organizational learning, and e-collaboration, this book will explore the sustainable links between knowledge management influences and organizational capability. This book is ideally designed for managers, strategists, economists, policymakers, entrepreneurs, business professionals, researchers, students, and academics seeking research on recent trends in innovative economics and business technologies. |
| data driven business models: Data-driven Modeling for Diabetes Vasilis Marmarelis, Georgios Mitsis, 2014-04-22 This contributed volume presents computational models of diabetes that quantify the dynamic interrelationships among key physiological variables implicated in the underlying physiology under a variety of metabolic and behavioral conditions. These variables comprise for example blood glucose concentration and various hormones such as insulin, glucagon, epinephrine, norepinephrine as well as cortisol. The presented models provide a powerful diagnostic tool but may also enable treatment via long-term glucose regulation in diabetics through closed-look model-reference control using frequent insulin infusions, which are administered by implanted programmable micro-pumps. This research volume aims at presenting state-of-the-art research on this subject and demonstrating the potential applications of modeling to the diagnosis and treatment of diabetes. The target audience primarily comprises research and experts in the field but the book may also be beneficial for graduate students. |
| data driven business models: Big Data Driven Supply Chain Management Nada R. Sanders, 2014-05-07 Master a complete, five-step roadmap for leveraging Big Data and analytics to gain unprecedented competitive advantage from your supply chain. Using Big Data, pioneers such as Amazon, UPS, and Wal-Mart are gaining unprecedented mastery over their supply chains. They are achieving greater visibility into inventory levels, order fulfillment rates, material and product delivery… using predictive data analytics to match supply with demand; leveraging new planning strengths to optimize their sales channel strategies; optimizing supply chain strategy and competitive priorities; even launching powerful new ventures. Despite these opportunities, many supply chain operations are gaining limited or no value from Big Data. In Big Data Driven Supply Chain Management, Nada Sanders presents a systematic five-step framework for using Big Data in supply chains. You'll learn best practices for segmenting and analyzing customers, defining competitive priorities for each segment, aligning functions behind strategy, dissolving organizational boundaries to sense demand and make better decisions, and choose the right metrics to support all of this. Using these techniques, you can overcome the widespread obstacles to making the most of Big Data in your supply chain — and earn big profits from the data you're already generating. For all executives, managers, and analysts interested in using Big Data technologies to improve supply chain performance. |
| data driven business models: Mobile Service Innovation and Business Models Harry Bouwman, Henny de Vos, Timber Haaker, 2009-08-29 Modern economies depend on innovation in services for their future growth. Service innovation increasingly depends on information technology and digitization of information processes. Designing new services is a complex matter, since collaboration with other companies and organizations is necessary. Service innovation is directly related to business models that support these services, i.e. services can only be successful in the long run with a viable business model that creates value for its customers and providers. This book presents a theoretically grounded yet practical approach to designing viable business models for electronic services, including mobile ones, i.e. the STOF model and – based on it – the STOF method. The STOF model provides a ‘holistic’ view on business models with four interrelated perspectives, i.e., Service, Technology, Organization and Finance. It elaborates on critical design issues that ultimately shape the business model and drive its viability. |
| data driven business models: Data-driven Organization Design Rupert Morrison, 2015-10-03 SHORTLISTED: CMI Management Book of the Year 2017 - Management Futures Category Data is changing the nature of competition. Making sense of it is tough; taking advantage of it is even tougher. There is a clear business opportunity for organizations to use data and analytics to transform business performance. Data-driven Organization Design provides a practical framework for HR and organization design practitioners to build a baseline of data, set objectives, carry out fixed and dynamic process design, map competencies, and right-size the organization so everyone performs to their potential and organizations have a hope of getting and sustaining a competitive edge. Data-driven Organization Design shows how to collect the right data on organizations, present it meaningfully and ask the right questions of it to help complex, fluid organizations constantly evolve and meet moving objectives. Through the use of case studies, practical tips, and sample exercises, it explains in detail how to use data and analytics to connect all the elements of the system so you can design an environment for people to perform, an organization which has the right people, in the right place, doing the right things, at the right time. Whether you are looking to implement a long-term transformation, large redesign, or a one-off small scale project, Data-driven Organization Design will guide you through making the most of organizational data and analytics to drive business performance. |
| data driven business models: AI-Driven Intelligent Models for Business Excellence Samala Nagaraj, Korupalli V. Rajesh Kumar, 2022 As digital technology is taking the world in a revolutionary way and business related aspects are getting smarter this book is a potential research source on the Artificial Intelligence-based Business Applications and Intelligence-- |
| data driven business models: Production at the Leading Edge of Technology Bernd-Arno Behrens, Alexander Brosius, Welf-Guntram Drossel, Wolfgang Hintze, Steffen Ihlenfeldt, Peter Nyhuis, 2021-09-04 This congress proceedings provides recent research on leading-edge manufacturing processes. The aim of this scientific congress is to work out diverse individual solutions of production at the leading edge of technology and transferable methodological approaches. In addition, guest speakers with different backgrounds will give the congress participants food for thoughts, interpretations, views and suggestions. The manufacturing industry is currently undergoing a profound structural change, which on the one hand produces innovative solutions through the use of high-performance communication and information technology, and on the other hand is driven by new requirements for goods, especially in the mobility and energy sector. With the social discourse on how we should live and act primarily according to guidelines of sustainability, structural change is gaining increasing dynamic. It is essential to translate politically specified sustainability goals into socially accepted and marketable technical solutions. Production research is meeting this challenge and will make important contributions and provide innovative solutions from different perspectives. |
| data driven business models: Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering Shahab Araghinejad, 2013-11-26 “Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering. |
| data driven business models: Lean Analytics Alistair Croll, Benjamin Yoskovitz, 2024-02-23 Whether you're a startup founder trying to disrupt an industry or an entrepreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. Lean Analytics steers you in the right direction. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word. Packed with more than thirty case studies and insights from over a hundred business experts, Lean Analytics provides you with hard-won, real-world information no entrepreneur can afford to go without. Understand Lean Startup, analytics fundamentals, and the data-driven mindset Look at six sample business models and how they map to new ventures of all sizes Find the One Metric That Matters to you Learn how to draw a line in the sand, so you'll know it's time to move forward Apply Lean Analytics principles to large enterprises and established products |
| data driven business models: Data-Driven Marketing Content Lee Wilson, 2019-06-19 This practical content guide empowers businesses to understand, identify and act on big-data opportunities, producing superior business insights for prolific marketing gains. |
| data driven business models: Strategic Digital Transformation Alex Fenton, Gordon Fletcher, Marie Griffiths, 2019-11-25 Emerging technologies are having a profound impact upon business as individuals and organisations increasingly embrace the benefits of the ‘always on’ attitude that digital technologies produce. The use of the web, apps, cloud storage, GPS and Internet-connected devices has transformed the way we live, learn, play and interact – yet how a business can fully benefit from this transformation is not always clear. In response, this book enables students and business leaders to take a strategic and sustainable approach to realising the value of digital technologies. It offers results-driven solutions that successfully transform organisations into data-driven, people-focused businesses capable of sustainably competing at a global level. Split across four key parts, the material moves through understanding digital business to planning, implementing and assessing digital transformation. The current challenges facing all small organisations, including limited resources, financial pressures and the lack of dedicated IT departments, are explored. The authors consider the ways in which innovation can increase competitive advantage, how innovative business models can create new opportunities and how a data-driven perspective can release embedded value within the organisation. Contemporary international case studies and examples throughout each chapter bridge theory with practical application and systematically document the patterns of activities that enable success. This textbook is a vital resource for postgraduate and undergraduate students of digital business, innovation and transformation. By showing how to initiate digital transformation across an organisation, it will prepare business owners, directors and management of small- and medium-sized businesses to take strategic advantage of new and emerging technologies to stay ahead of their competition. |
| data driven business models: Big Data and Analytics Vincenzo Morabito, 2015-01-31 This book presents and discusses the main strategic and organizational challenges posed by Big Data and analytics in a manner relevant to both practitioners and scholars. The first part of the book analyzes strategic issues relating to the growing relevance of Big Data and analytics for competitive advantage, which is also attributable to empowerment of activities such as consumer profiling, market segmentation, and development of new products or services. Detailed consideration is also given to the strategic impact of Big Data and analytics on innovation in domains such as government and education and to Big Data-driven business models. The second part of the book addresses the impact of Big Data and analytics on management and organizations, focusing on challenges for governance, evaluation, and change management, while the concluding part reviews real examples of Big Data and analytics innovation at the global level. The text is supported by informative illustrations and case studies, so that practitioners can use the book as a toolbox to improve understanding and exploit business opportunities related to Big Data and analytics. |
| data driven business models: Data-Driven Marketing Mark Jeffery, 2010-02-08 NAMED BEST MARKETING BOOK OF 2011 BY THE AMERICAN MARKETING ASSOCIATION How organizations can deliver significant performance gains through strategic investment in marketing In the new era of tight marketing budgets, no organization can continue to spend on marketing without knowing what's working and what's wasted. Data-driven marketing improves efficiency and effectiveness of marketing expenditures across the spectrum of marketing activities from branding and awareness, trail and loyalty, to new product launch and Internet marketing. Based on new research from the Kellogg School of Management, this book is a clear and convincing guide to using a more rigorous, data-driven strategic approach to deliver significant performance gains from your marketing. Explains how to use data-driven marketing to deliver return on marketing investment (ROMI) in any organization In-depth discussion of the fifteen key metrics every marketer should know Based on original research from America's leading marketing business school, complemented by experience teaching ROMI to executives at Microsoft, DuPont, Nisan, Philips, Sony and many other firms Uses data from a rigorous survey on strategic marketing performance management of 252 Fortune 1000 firms, capturing $53 billion of annual marketing spending In-depth examples of how to apply the principles in small and large organizations Free downloadable ROMI templates for all examples given in the book With every department under the microscope looking for results, those who properly use data to optimize their marketing are going to come out on top every time. |
| data driven business models: Competing in the Age of AI Marco Iansiti, Karim R. Lakhani, 2020-01-07 a provocative new book — The New York Times AI-centric organizations exhibit a new operating architecture, redefining how they create, capture, share, and deliver value. Now with a new preface that explores how the coronavirus crisis compelled organizations such as Massachusetts General Hospital, Verizon, and IKEA to transform themselves with remarkable speed, Marco Iansiti and Karim R. Lakhani show how reinventing the firm around data, analytics, and AI removes traditional constraints on scale, scope, and learning that have restricted business growth for hundreds of years. From Airbnb to Ant Financial, Microsoft to Amazon, research shows how AI-driven processes are vastly more scalable than traditional processes, allow massive scope increase, enabling companies to straddle industry boundaries, and create powerful opportunities for learning—to drive ever more accurate, complex, and sophisticated predictions. When traditional operating constraints are removed, strategy becomes a whole new game, one whose rules and likely outcomes this book will make clear. Iansiti and Lakhani: Present a framework for rethinking business and operating models Explain how collisions between AI-driven/digital and traditional/analog firms are reshaping competition, altering the structure of our economy, and forcing traditional companies to rearchitect their operating models Explain the opportunities and risks created by digital firms Describe the new challenges and responsibilities for the leaders of both digital and traditional firms Packed with examples—including many from the most powerful and innovative global, AI-driven competitors—and based on research in hundreds of firms across many sectors, this is your essential guide for rethinking how your firm competes and operates in the era of AI. |
| data driven business models: Business Intelligence David Loshin, 2012-11-27 Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge. Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI. It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata. Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered. This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. - Guides managers through developing, administering, or simply understanding business intelligence technology - Keeps pace with the changes in best practices, tools, methods and processes used to transform an organization's data into actionable knowledge - Contains a handy, quick-reference to technologies and terminology |
| data driven business models: The Innovation Mode George Krasadakis, 2020-07-29 This book presents unique insights and advice on defining and managing the innovation transformation journey. Using novel ideas, examples and best practices, it empowers management executives at all levels to drive cultural, technological and organizational changes toward innovation. Covering modern innovation techniques, tools, programs and strategies, it focuses on the role of the latest technologies (e.g., artificial intelligence to discover, handle and manage ideas), methodologies (including Agile Engineering and Rapid Prototyping) and combinations of these (like hackathons or gamification). At the same time, it highlights the importance of culture and provides suggestions on how to build it. In the era of AI and the unprecedented pace of technology evolution, companies need to become truly innovative in order to survive. The transformation toward an innovation-led company is difficult – it requires a strong leadership and culture, advanced technologies and well-designed programs. The book is based on the author’s long-term experience and novel ideas, and reflects two decades of startup, consulting and corporate leadership experience. It is intended for business, technology, and innovation leaders. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
UNDERSTANDING THE ANATOMY OF DATA-DRIVEN …
data-driven business models refers to new dynamics in “the interplay between the offering and the customer” (Lycett, 2013, p. 382). By collecting and analyzing consumer data, organizations can …
The Role of Digital Technologies in a Data-driven Circular …
Data-driven business model (data OR “data collection” OR “data gathering” OR “data analysis” OR “data analytics” OR “data mining” ) Table 1: Keywords used in the search settings
INNOVATIVE BUSINESS MODELS DRIVEN BY AI …
AI-driven business models are distinguished by their ability to leverage data and machine learning (ML) technologies to create more efficient, responsive, and intelligent business processes. …
Evaluating the impact of AI on insurance: The four emerging
Oct 1, 2019 · business models? Is it improving existing models or disrupting them? The next section reviews the literature. This is followed by the methodology section that explain how the …
THE GENERAL DATA PROTECTION’S (GDPR) IMPACT ON …
data-driven business models (DDBMs) of firms, yet nonetheless they may lead to a variety of positive effects. Indeed, the principles and individual rights in the GDPR tackle monopolistic …
Accountable algorithms? The ethical implications of data …
disconnected perspectives on technology-enabled service, data-driven business models, data ethics and business ethics to introduce a novel analytical framework centered on data-driven …
Data or business first? Manufacturers' transformation toward …
2.1 Data-Driven Business Models and Archetypes in Manufacturing In the manufacturing sector, cyber-physical systems and the industrial internet of things have led more and more …
What about Data-Driven Business Models? Mapping the …
Taisch, 2015; Hunke, Seebacher, Schüritz, & Illi, 2017). This trend of value generation from data has driven the conceptualisation of a data-driven business model (DDBM; Schüritz & Satzger …
An Exploratory Analysis of the Current Status and Potential of …
Considering service-oriented business models, a subset of them are formed by data-driven business models [52,53]. Thereby, data represent the foundation of these business models. …
Data-Driven Decision-Making in Digital Entrepreneurship
1 Abstract—Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion …
Charting the Emerging Financial Services Ecosystem of …
4. Key Results: Data-driven Business Models in the Fintech Sector 4.1. Fintechs’ Business Areas We clustered the fintechs’ business models into seventeen business areas (see Table 1). …
Berlin Start-ups – The Rise of Data-Driven Business Models
AI data-driven business models have become a new unit of analysis in business model research. Tracing back to different roots in scholarly business model literature,
How does Enterprise Architecture support the Design and
2.1 Big Data Analytics and Data-Driven Business Models The research on big data is deeply rooted in the information system discipline [7–10]. However, the term under which it was …
Data-driven strategies for business expansion: Utilizing …
expansion initiatives (Henke and Jacques Bughin, 2016). Data-driven strategies involve leveraging vast amounts of data to derive actionable insights, enabling businesses to make …
Capturin g Value from Big Data – A Taxonomy of Data
data-driven business models (DDBMs), are needed. Notably, scholars have published surprisingly little on this topic. Hence, understanding the nature of business models that rely on data …
Data-Driven Transformation - Boston Consulting Group
data-driven approaches and make them sustainable, including building a data lake. And it has begun identifying new data-driven business models. The overall goal for the transformation is …
Accountable algorithms? The ethical implications of data …
data-driven business models Christoph F. Breidbach University of Queensland, Brisbane, Australia, and Paul Maglio University of California, Merced, California, USA Abstract
What about Data-Driven Business Models? Mapping the …
International Journal of Business and Management; Vol. 16, No. 8; 2021 ISSN 1833-3850 E-ISSN 1833-8119 Published by Canadian Center of Science and Education
Business Model Analysis of Netflix - ResearchGate
of the relevant literature on media platform strategies, data analytics, and customer engagement. Section 3 presents the methodology employed to analyze Netflix's data analytics-driven …
How to monetize data in data-driven business models
Data-driven business models (e.g., Data Provider, Analytics-as-a-Service) are based on data as a key resource, while digital platform business models (e.g., Software Platform
Employee perspectives on value realization from data within …
Keywords Data-driven business models · Value realization from data JEL classication L62 · O32 Introduction In the course of big data and data analytics’ technologi-cal progress, and the …
Unlocking the value from car data: A taxonomy and …
of data‑driven business models in the connected car domain? To address this question, we follow a sequential research design comprising two phases. In the first phase, we follow the …
Making Data Tangible for Data-driven Innovations in a …
implementing data-driven business models (DDBMs) is comparatively limited because the field is relatively new (Hartmann et al. 2014). First and foremost, the specific characteristics of DDBMs …
NLOCKING THE OTENTIAL OF DATA DRIVEN BUSINESS …
Data-driven business models are expected to stimulate new economic growth by promoting innovation and value creation through data. However, in addition to concerns about privacy
Formative Evaluation of Data-Driven Business Models – The …
As a consequence, new data-driven business models (DDBMs) appear. These business models have special characteristics which need to be included in the business model development …
Capturing Value from Data: Exploring Factors Influencing …
a taxonomy of data-driven business models and argue that the effective use of big data, e.g. by offering purely data-driven services, could lead to a competitive advantage. In their recent …
TOWARDS A COST-BENEFIT-ANALYSIS OF DATA-DRIVEN …
Data-driven Business Models / DDBM A business model that relies on data as a key resource (Hartmann et al., 2014) Data act as facilitator of innovative services, based on enabling …
BUILDING A DATA-DRIVEN ORGANISATION
Go To Home | Building a data-driven organisation | 5 A data-driven digital strategy can help organisations create entirely new business models, improve the customer experience and …
New Business Models for Data-Driven Services - ResearchGate
New Business Models for Data-Driven Services 2 This study has been prepared as part of data.europa.eu. Data.europa.eu is an initiative of the European Commission.
FIRMS GOING DIGITAL: TAPPING INTO THE POTENTIAL OF …
data-driven business models of firms that successfully innovate with data, which are often firms that were born digital. The insights provided in this paper reveal how such firms use data and …
Data-driven business and data privacy: Challenges and …
value through data-driven business models (Casadesus-Masanell & Hervas-Drane, 2020; Mazurek & Małagocka, 2019). 3.1. User-centered perspective Data-driven businesses require a …
Privacy of European Citizens in the Face of the Development …
Data-Driven Business Models, right to privacy, personal information, right to be forgotten, confidentiality of communications, image, identity. University of Economics and Human …
Knowledge Leaks in Data-Driven Business Models?
2.1 Data-DrivenBusiness Models Data-driven business models (DDBMs) have a conceptual focus on value creation from data (Guggenberger et al. 2020). A business model is a conceptual tool …
Data - a new emerging type of intangibles - ICAEW
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BUSINESS MODEL TRANSFORMATION PATTERNS OF DATA …
data-driven business models in the years 2011-2016 will grow annually by 46.6% (Bitkom, 2015). Many companies plan to expand their current IT infrastructure to Big Data solutions. However, …
OPERATING MODEL FOR DATA GOVERNANCE AND …
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Capturing Value from Data: Revenue Models for Data-Driven …
These new data-driven business models create value for customers through the generation, aggregation and analysis of data [6]. In addition to value creation, capturing the value …
ATA INNOVATION EXPLORER DESIGN OF A DATA DRIVEN …
for people collaborating on the design of data-driven business models. 2 Related Work 2.1 Data-driven Business Models Data-driven innovation represents a significant shift away from …
Pathways of Data-driven Business Model Design and …
business mode with data or to realize new data-driven business models [3, 4]. The latter has led to the emergence of a new research field, which investigates data-driven business models …
Data-driven mastery in commercial banking - Accenture
Data-driven mastery in commercial banking 13 A common strand in data-driven leaders across different sectors is the prioritization of data on the C-suite agenda, with leaders encouraging …
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Towards a Process Model for Data-Driven Business Model …
Keywords—Business Model Innovation, Data-driven Business Models, Design Science I. INTRODUCTION The amount of data that is created in an industrial context is rapidly …
Data-Driven Business Models - lisboncouncil.net
Data-Driven Business Models: Powering Startups in the Digital Age Startups • European companies are embracing data analytics • Agile and dynamic startups are well placed to …
The Data Value Chain - GSMA
data can be used for multiple purposes and cycles of collection and exploitation become self-sustaining. It is important, therefore, that the data value chain works well and delivers …
AI-Driven Business Model Innovation: Pioneering New
for insurance inspections, transforming traditional business models into agile, responsive, and technologically integrated frameworks. This evolu-tion underscores a strategic shift toward data …
A Data-Driven Business Model Framework for Value Capture …
4. The Hybrid Business Model Canvas 4.1. Populating a BMC Template Each of the four examples of data-driven business models can be captured in a BMC
Patterns of Data-Infused Business Model Innovation
that there are no data-driven business models per se; instead, the utilization of data and analytics opens a “continuum” of transfor-mation options for business models. We identify five distinct
Balancing Value Propositions with Privacy: Exploring Data …
world of data-driven digital business models. Business Models and Value Propositions Although the literature on business models is expanding, it is still in its early stages, with different …
Data and Analytics - Data-Driven Business Models: A …
1! Data and Analytics - Data-Driven Business Models: A Blueprint for Innovation The Competitive Advantage of the New Big Data World Josh Brownlow1, Mohamed Zaki2, Andy Neely2, and …
Capturing Value from Data: Revenue Models for Data-Driven …
These new data-driven business models create value for customers through the generation, aggregation and analysis of data [6]. In addition to value creation, capturing the value …
