| data analytics as a service business model: 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 analytics as a service business model: Churchill by Himself Winston S. Churchill, 2013-09-19 Quotations by the great statesman who helped lead Britain through two world wars: “Magisterial . . . Should be in the library of every Churchill aficionado” (American Spectator). We shall fight on the beaches, we shall fight on the landing grounds, we shall fight in the fields and in the streets, we shall fight in the hills; we shall never surrender . . . Millions have been moved by these words—and by the hundreds of speeches given by Winston S. Churchill to rally the British public, spur its government to armament against Hitler, and defend the causes for which he believed. Churchill by Himself is the first collection of quotations from a leader who had as much talent for wit as he had for inspiration and exhortation. Edited by renowned Churchill scholar Richard Langsworth, this volume is the definitive collection of important quotes from one of the twentieth century’s most persuasive and brilliant orators, whose writings earned him a Nobel Prize in Literature in 1953. |
| data analytics as a service business model: 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 analytics as a service business model: 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 analytics as a service business model: Data Mining For Dummies Meta S. Brown, 2014-09-04 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| data analytics as a service business model: The Decision Maker's Handbook to Data Science Stylianos Kampakis, 2019-11-26 Data science is expanding across industries at a rapid pace, and the companies first to adopt best practices will gain a significant advantage. To reap the benefits, decision makers need to have a confident understanding of data science and its application in their organization. It is easy for novices to the subject to feel paralyzed by intimidating buzzwords, but what many don’t realize is that data science is in fact quite multidisciplinary—useful in the hands of business analysts, communications strategists, designers, and more. With the second edition of The Decision Maker’s Handbook to Data Science, you will learn how to think like a veteran data scientist and approach solutions to business problems in an entirely new way. Author Stylianos Kampakis provides you with the expertise and tools required to develop a solid data strategy that is continuously effective. Ethics and legal issues surrounding data collection and algorithmic bias are some common pitfalls that Kampakis helps you avoid, while guiding you on the path to build a thriving data science culture at your organization. This updated and revised second edition, includes plenty of case studies, tools for project assessment, and expanded content for hiring and managing data scientists Data science is a language that everyone at a modern company should understand across departments. Friction in communication arises most often when management does not connect with what a data scientist is doing or how impactful data collection and storage can be for their organization. The Decision Maker’s Handbook to Data Science bridges this gap and readies you for both the present and future of your workplace in this engaging, comprehensive guide. What You Will Learn Understand how data science can be used within your business. Recognize the differences between AI, machine learning, and statistics.Become skilled at thinking like a data scientist, without being one.Discover how to hire and manage data scientists.Comprehend how to build the right environment in order to make your organization data-driven. Who This Book Is For Startup founders, product managers, higher level managers, and any other non-technical decision makers who are thinking to implement data science in their organization and hire data scientists. A secondary audience includes people looking for a soft introduction into the subject of data science. |
| data analytics as a service business model: In Search of Excellence Thomas J. Peters, Robert H. Waterman, Jr., 2012-11-27 The Greatest Business Book of All Time (Bloomsbury UK), In Search of Excellence has long been a must-have for the boardroom, business school, and bedside table. Based on a study of forty-three of America's best-run companies from a diverse array of business sectors, In Search of Excellence describes eight basic principles of management -- action-stimulating, people-oriented, profit-maximizing practices -- that made these organizations successful. Joining the HarperBusiness Essentials series, this phenomenal bestseller features a new Authors' Note, and reintroduces these vital principles in an accessible and practical way for today's management reader. |
| data analytics as a service business model: Service Business Model Innovation in Healthcare and Hospital Management Mario A. Pfannstiel, Christoph Rasche, 2016-12-16 This book demonstrates how to successfully manage and lead healthcare institutions by employing the logic of business model innovation to gain competitive advantages. Since clerk-like routines in professional organizations tend to overlook patient and service-centered healthcare solutions, it challenges the view that competition and collaboration in the healthcare sector should not only incorporate single-end services, therapies or diagnosis related groups. Moreover, the authors focus on holistic business models, which place greater emphasis on customer needs and put customers and patients first. The holistic business models approach addresses topics such as business operations, competitiveness, strategic business objectives, opportunities and threats, critical success factors and key performance indicators.The contributions cover various aspects of service business innovation such as reconfiguring the hospital business model in healthcare delivery, essential characteristics of service business model innovation in healthcare, guided business modeling and analysis for business professionals, patient-driven service delivery models in healthcare, and continuous and co-creative business model creation. All of the contributions introduce business models and strategies, process innovations, and toolkits that can be applied at the managerial level, ensuring the book will be of interest to healthcare professionals, hospital managers and consultants, as well as scholars, whose focus is on improving value-generating and competitive business architectures in the healthcare sector. |
| data analytics as a service business model: Exploring Service Science Gerhard Satzger, Lia Patrício, Mohamed Zaki, Niklas Kühl, Peter Hottum, 2018-09-12 This book constitutes the proceedings of the 9th International Conference on Exploring Services Science, IESS 2018, held in Karlsruhe, Germany, in September 2018. The 30 papers presented in this volume were carefully reviewed and selected from 67 submissions. The book is structured in six parts, each featuring contributions describing current research in a particular domain of service science: Service Design and Innovation; Smart Service Processes; Big Data in Services; Service Topics Open Exploration; Design Science Research in Services. The book offers an extended, ICT-focused vision on services and addresses multiple relevant aspects, including underlying business models, the necessary processes and technological capabilities like big data and machine learning. The academic work showcased at the conference should help to advance service science and its application in practice. |
| data analytics as a service business model: 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City Mohammed Atiquzzaman, Neil Yen, Zheng Xu, 2021-12-09 This book gathers a selection of peer-reviewed papers presented at the third Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2021) conference, held in Shanghai, China, on Nov. 27, 2021. The contributions, prepared by an international team of scientists and engineers, cover the latest advances made in the field of machine learning, and big data analytics methods and approaches for the data-driven co-design of communication, computing, and control for smart cities. Given its scope, it offers a valuable resource for all researchers and professionals interested in big data, smart cities, and cyber-physical systems. |
| data analytics as a service business model: Business Intelligence Strategy and Big Data Analytics Steve Williams, 2016-04-08 Business Intelligence Strategy and Big Data Analytics is written for business leaders, managers, and analysts - people who are involved with advancing the use of BI at their companies or who need to better understand what BI is and how it can be used to improve profitability. It is written from a general management perspective, and it draws on observations at 12 companies whose annual revenues range between $500 million and $20 billion. Over the past 15 years, my company has formulated vendor-neutral business-focused BI strategies and program execution plans in collaboration with manufacturers, distributors, retailers, logistics companies, insurers, investment companies, credit unions, and utilities, among others. It is through these experiences that we have validated business-driven BI strategy formulation methods and identified common enterprise BI program execution challenges. In recent years, terms like big data and big data analytics have been introduced into the business and technical lexicon. Upon close examination, the newer terminology is about the same thing that BI has always been about: analyzing the vast amounts of data that companies generate and/or purchase in the course of business as a means of improving profitability and competitiveness. Accordingly, we will use the terms BI and business intelligence throughout the book, and we will discuss the newer concepts like big data as appropriate. More broadly, the goal of this book is to share methods and observations that will help companies achieve BI success and thereby increase revenues, reduce costs, or both. - Provides ideas for improving the business performance of one's company or business functions - Emphasizes proven, practical, step-by-step methods that readers can readily apply in their companies - Includes exercises and case studies with road-tested advice about formulating BI strategies and program plans |
| data analytics as a service business model: Handbook of Research on Driving Socioeconomic Development With Big Data Sun, Zhaohao, 2023-02-24 Socioeconomic development has drawn increasing attention in academia, industries, and governments. The relationship between big data and its technologies and socioeconomic development has drawn certain attention in academia. Socioeconomic development depends not only on big data, but also on big data technologies. However, the relationship between big data and socioeconomic development is not adequately covered in current research. The Handbook of Research on Driving Socioeconomic Development With Big Data provides an original and innovative understanding of and insight into how the proposed theories, technologies, and methodologies of big data can improve socioeconomic development and sustainable development in terms of business and services, healthcare, the internet of everything, sharing economy, and more. Covering topics such as corporate social responsibility, management applications, and process mining, this major reference work is an excellent resource for data scientists, business leaders and executives, IT professionals, government officials, economists, sociologists, librarians, students, researchers, and academicians. |
| data analytics as a service business model: Analytics Across the Enterprise Brenda L. Dietrich, Emily C. Plachy, Maureen F. Norton, 2014-05-15 How to Transform Your Organization with Analytics: Insider Lessons from IBM’s Pioneering Experience Analytics is not just a technology: It is a better way to do business. Using analytics, you can systematically inform human judgment with data-driven insight. This doesn’t just improve decision-making: It also enables greater innovation and creativity in support of strategy. Your transformation won’t happen overnight; however, it is absolutely achievable, and the rewards are immense. This book demystifies your analytics journey by showing you how IBM has successfully leveraged analytics across the enterprise, worldwide. Three of IBM’s pioneering analytics practitioners share invaluable real-world perspectives on what does and doesn’t work and how you can start or accelerate your own transformation. This book provides an essential framework for becoming a smarter enterprise and shows through 31 case studies how IBM has derived value from analytics throughout its business. Coverage Includes Creating a smarter workforce through big data and analytics More effectively optimizing supply chain processes Systematically improving financial forecasting Managing financial risk, increasing operational efficiency, and creating business value Reaching more B2B or B2C customers and deepening their engagement Optimizing manufacturing and product management processes Deploying your sales organization to increase revenue and effectiveness Achieving new levels of excellence in services delivery and reducing risk Transforming IT to enable wider use of analytics “Measuring the immeasurable” and filling gaps in imperfect data Whatever your industry or role, whether a current or future leader, analytics can make you smarter and more competitive. Analytics Across the Enterprise shows how IBM did it--and how you can, too. Learn more about IBM Analytics |
| data analytics as a service business model: AI-Based Data Analytics Kiran Chaudhary, Mansaf Alam, 2023-12-29 Apply analytics to improve customer experience, AI applied to targeted and personalized marketing Debugging and simulation tools and techniques for massive data systems |
| data analytics as a service business model: Entrepreneurship and Big Data Meghna Chhabra, Rohail Hassan, Amjad Shamim, 2021-09-30 The digital age has transformed business opportunities and strategies in a resolutely practical and data-driven project universe. This book is a comprehensive and analytical source on entrepreneurship and Big Data that prospective entrepreneurs must know before embarking upon an entrepreneurial journey in this present age of digital transformation. This book provides an overview of the various aspects of entrepreneurship, function, and contemporary forms. It covers a real-world understanding of how the entrepreneurial world works and the required new analytics thinking and computational skills. It also encompasses the essential elements needed when starting an entrepreneurial journey and offers inspirational case studies from key industry leaders. Ideal reading for aspiring entrepreneurs, Entrepreneurship and Big Data: The Digital Revolution is also useful to students, academicians, researchers, and practitioners. |
| data analytics as a service business model: Predictive Analytics and Generative AI for Data-Driven Marketing Strategies Hemachandran K, Debdutta Choudhury, Raul Villamarin Rodriguez, Jorge A. Wise, Revathi T, 2024-12-10 In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies. This book: • Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data. • Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making. • Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context. • Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions. • Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing. |
| data analytics as a service business model: 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 analytics as a service business model: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| data analytics as a service business model: Integration of Cloud Computing with Internet of Things Monika Mangla, Suneeta Satpathy, Bhagirathi Nayak, Sachi Nandan Mohanty, 2021-03-08 The book aims to integrate the aspects of IoT, Cloud computing and data analytics from diversified perspectives. The book also plans to discuss the recent research trends and advanced topics in the field which will be of interest to academicians and researchers working in this area. Thus, the book intends to help its readers to understand and explore the spectrum of applications of IoT, cloud computing and data analytics. Here, it is also worth mentioning that the book is believed to draw attention on the applications of said technology in various disciplines in order to obtain enhanced understanding of the readers. Also, this book focuses on the researches and challenges in the domain of IoT, Cloud computing and Data analytics from perspectives of various stakeholders. |
| data analytics as a service business model: Digital Business Models Sébastien Ronteau, Laurent Muzellec, Deepak Saxena, Daniel Trabucchi, 2022-12-19 A business model basically describes the way a company makes money. Yet, often we use digital services for free (e.g. Facebook, Google or WhatsApp) or for what seems to be a relatively minor price (e.g. Blablacar, Airbnb, and Amazon). Digital business models are different to traditional business models. Digital Business Models explains the key challenges and characteristics of the various business models that are used by digital businesses. These companies can be a source of inspiration for traditional bricks-and-mortar companies that aim to go digital and/or revamp their traditional business model. Most businesses rely on some form of digital technology for their marketing communication, customer relationship management, supply chain or distribution, yet digital transformation entails a complete reassessment of the way value is created and captured. Digital Business Models details the successful customer acquisition tactics and the development of business ecosystems by digital players. Using the relevant academic and managerial body of knowledge, the authors define the concepts, describe the various ways digital businesses create and capture value and propose some useful tools for managers to analyse a situation, formulate or implement a strategy. Different digital business types are evaluated, such as multisided platforms, digital merchants, subscription-based model, freemium, social media and sharing economy. Each chapter is illustrated with several examples and the appendix comprises four full-length case studies. |
| data analytics as a service business model: Research Handbook on Digital Strategy Carmelo Cennamo, Giovanni B. Dagnino, Feng Zhu, 2023-05-09 This state-of-the-art Research Handbook presents a comprehensive overview of the key strategic challenges that firms face when dealing with digital markets, platforms, and products and services, from old strategy questions in need of different solutions to entirely novel issues posed by the new competitive digital context. This title contains one or more Open Access chapters. |
| data analytics as a service business model: The business model cycle Sophia von Berg, 2020-12-17 Today, firms all over the world have to deal with dynamic business environments. Fast-moving digitalization has made information more transparent, strengthening the role of the customer. At the same time, the provider can have a much closer relationship with the user, thanks to real-time communication. However, corporate practice does not have a process for developing dynamic business models, and user-centric business models that can be designed and changed using smart technologies have not yet been systematically integrated. To stay competitive, companies need to rise to this challenge. The aim of this dissertation was to develop a dynamic, user-centric process model for business model design and change, and to evaluate the model’s ability to maintain a competitive advantage in the mobility sector. First, the differences between static, dynamic, and user-centric business models and their corresponding attributes were deduced. Then, these findings were combined into a process model using system dynamics logic. This model considers the user a co-creator of value and helps managers react to real-time changes in their business model environment. Finally, a mobility sector case study is presented to highlight the relevance of this model to real-world application. This business model cycle (BMC) supports the strategic management of dynamic, user-centric business model design and change activities. It describes a step by step procedure of business model design that includes ideation, prototyping, and integration of business model options. Moreover, it allows continuous monitoring of the business model environment and adaption of the model accordingly. At the same time, bidirectional interaction between the user and provider is possible, allowing the provider to adapt to their users’ needs. The BMC is unique in that these processes can take place simultaneously. Finally, the real-world case study in the mobility sector confirmed that using the BMC for strategic management maintains a lasting competitive business advantage. |
| data analytics as a service business model: Smart Grid Analytics for Sustainability and Urbanization Gontar, Zbigniew H., 2018-06-27 Information and communication technologies play an essential role in the effectiveness and efficiency of smart city processes. Recognizing the role of process analysis in energy usage and how it can be enhanced is essential to improving city sustainability. Smart Grid Analytics for Sustainability and Urbanization provides emerging research on the development of information technology and communication systems in smart cities and smart grids. While highlighting topics such as process mining, innovation management, and sustainability optimization, this publication explores technology development and the mobilization of different environments in smart cities. This book is an important resource for graduate students, researchers, academics, engineers, and government officials seeking current research on how process analysis in energy usage is manifested and how it can be enhanced. |
| data analytics as a service business model: Computational Intelligence Applications in Business Intelligence and Big Data Analytics Vijayan Sugumaran, Arun Kumar Sangaiah, Arunkumar Thangavelu, 2017-06-26 There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book. |
| data analytics as a service business model: Research Anthology on Big Data Analytics, Architectures, and Applications Management Association, Information Resources, 2021-09-24 Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians. |
| data analytics as a service business model: Data Analytics for Organisational Development Uwe H. Kaufmann, Amy B. C. Tan, 2021-07-26 A practical guide for anyone who aspires to become data analytics–savvy Data analytics has become central to the operation of most businesses, making it an increasingly necessary skill for every manager and for all functions across an organisation. Data Analytics for Organisational Development: Unleashing the Potential of Your Data introduces a methodical process for gathering, screening, transforming, and analysing the correct datasets to ensure that they are reliable tools for business decision-making. Written by a Six Sigma Master Black Belt and a Lean Six Sigma Black Belt, this accessible guide explains and illustrates the application of data analytics for organizational development and design, with particular focus on Customer and Strategy Analytics, Operations Analytics and Workforce Analytics. Designed as both a handbook and workbook, Data Analytics for Organisational Development presents the application of data analytics for organizational design and development using case studies and practical examples. It aims to help build a bridge between data scientists, who have less exposure to actual business issues, and the non-data scientists. With this guide, anyone can learn to perform data analytics tasks from translating a business question into a data science hypothesis to understanding the data science results and making the appropriate decisions. From data acquisition, cleaning, and transformation to analysis and decision making, this book covers it all. It also helps you avoid the pitfalls of unsound decision making, no matter where in the value chain you work. Follow the “Five Steps of a Data Analytics Case” to arrive at the correct business decision based on sound data analysis Become more proficient in effectively communicating and working with the data experts, even if you have no background in data science Learn from cases and practical examples that demonstrate a systematic method for gathering and processing data accurately Work through end-of-chapter exercises to review key concepts and apply methods using sample data sets Data Analytics for Organisational Development includes downloadable tools for learning enrichment, including spreadsheets, Power BI slides, datasets, R analysis steps and more. Regardless of your level in your organisation, this book will help you become savvy with data analytics, one of today’s top business tools. |
| data analytics as a service business model: Reconfiguration of Business Models and Ecosystems Svetla T. Marinova, Marin A. Marinov, 2023-02-10 Decoupling of business models and ecosystems is the disconnection of certain characteristic activities originally planned and completed in coincidence. It could bring in an immense adverse shock in the functioning of established business models and ecosystems possibly bringing them to resilience. Core causes for decoupling and resilience of business models and ecosystems are jolts, known as global crisis, universal pandemics, etc. The undesirable outcomes of critical events can reveal unique circumstances for business model and ecosystem resilience. Business model and ecosystem resilience represents a mandatory prerequisite for firms challenging their functioning and even very existence. Research has been conducted thus far, nevertheless this theme requires significantly more consideration. The key objective of this book is to bring further insights in the field delivering a thorough examination of the ways in which business models and ecosystems can develop resilience under extraordinary conditions. In the book, the resilience of business models and ecosystems is analysed aiming to investigate further the specifics of the relevant processes securing resilience and its outcomes. The resilience of business models and ecosystems is scrutinised as a credible way for enhancing the predispositions of firm’s survivability. Chapter 9 of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons Attribution-NonCommercial-No Derivatives 4.0 license. |
| data analytics as a service business model: Data Science for Business Professionals Probyto Data Science and Consulting Pvt. Ltd., 2020-05-06 Primer into the multidisciplinary world of Data Science KEY FEATURESÊÊ - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTIONÊ The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FORÊ This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.Ê TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business IntelligenceÊ 16. Data Visualization Tools 17. Industry Use Case 1 Ð FormAssist 18. Industry Use Case 2 Ð PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments |
| data analytics as a service business model: OECD Reviews of Digital Transformation Going Digital in Brazil OECD, 2020-10-26 Going Digital in Brazil analyses recent developments in Brazil’s digital economy, reviews policies related to digitalisation and makes recommendations to increase policy coherence in this area. |
| data analytics as a service business model: The Ecosystem of e-Business: Technologies, Stakeholders, and Connections Jennifer J. Xu, Bin Zhu, Xiao Liu, Michael J. Shaw, Han Zhang, Ming Fan, 2019-06-27 This book constitutes revised selected papers from the 17th Workshop on e-Business, WeB 2018, which took place in Santa Clara, CA, USA, in December 2018. The purpose of WeB is to provide an open forum for e-Business researchers and practitioners world-wide, to share topical research findings, explore novel ideas, discuss success stories and lessons learned, map out major challenges, and collectively chart future directions for e-Business. The WeB 2018 theme was “The Ecosystem of e-Business: Technologies, Stakeholders, and Connections.” There was a total of 47 submissions and 41 papers were presented at the conference. Of these, 19 revised papers are presented in this volume. These contributions are organized in the following topical sections: social, policy, and privacy issues; e-market; FinTech; and artificial intelligence. |
| data analytics as a service business model: Data as a Service Pushpak Sarkar, 2015-07-31 Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions |
| data analytics as a service business model: The Influences of Big Data Analytics Dr. Joseph Aluya, D.B.A., 2014-09-05 The theoretical framework for this book was our ground-up theory of the Scope, Size, Speed, and Skill (4Ss) and Technological Situational Happenstances (TSHs) applied to Big data analytics. With in-depth research, we catechized the effects of the coalesced insights from big data influencing the architectures of incremental and radical business models. We discussed data inflation and the global impact of TSHs. We showed how deft leadership used insights gleaned from big data analytics to make strategic decisions. The big data syndrome led to Microsoft's acquisition of Nokia in our case study. Our study of APPLE Corporation's use of large datasets was explicitly analyzed. Leaderships' failure to incorporate those contextual elements afforded by insights gleaned from big data analytics, concomitant with the associated costs led to acute forms of irrational rationalism, groupthink, and faulty decision making. We explained the statistics used to essentially describe this paradigm shift, such as high dimensionality, incidental endogeneity, noise accumulation, spurious correlation, and computational costs. Significantly, machine learning challenged the status quo by effectively changing the existing technological landscape. To scholarly critics, how would supervised and un-supervised learning algorithms advance the trajectory of perspectives in applied knowledge under the umbrella of big data? Further, political and socio-economics tied to big data was examined. We recommended leaders should have a shared cognition on how to leverage analytics from large datasets for competitive advantages. Most significantly, leaders or managers should be cognizant of the inextricable synergies that seamlessly flow from adroitly implementing a strategy to profit from the speed, size, skill, and scope (i.e. the 4Ss) of the big data environment, conditioned by the leveraging of those transactional situational happenstances generated by increases in market volatility. We concluded the algorithmic processes of leveraging insights from big data have globally resulted in a disruption of current technological pathways. |
| data analytics as a service business model: Enterprise Big Data Engineering, Analytics, and Management Atzmueller, Martin, 2016-06-01 The significance of big data can be observed in any decision-making process as it is often used for forecasting and predictive analytics. Additionally, big data can be used to build a holistic view of an enterprise through a collection and analysis of large data sets retrospectively. As the data deluge deepens, new methods for analyzing, comprehending, and making use of big data become necessary. Enterprise Big Data Engineering, Analytics, and Management presents novel methodologies and practical approaches to engineering, managing, and analyzing large-scale data sets with a focus on enterprise applications and implementation. Featuring essential big data concepts including data mining, artificial intelligence, and information extraction, this publication provides a platform for retargeting the current research available in the field. Data analysts, IT professionals, researchers, and graduate-level students will find the timely research presented in this publication essential to furthering their knowledge in the field. |
| data analytics as a service business model: The Emerging Business Models Chong Guan, Zhiying Jiang, Ding Ding, 2020-05-21 The Emerging Business Models describes current issues that the business leaders and professionals are facing, as well as developments in digitalization. This book consisting of 10 chapters introduces the new technology trends and challenges that businesses today face. The authors cover several increasingly important new areas such as the Fourth Industrial Revolution, Internet of Things (IoT), financial technology (FinTech), social media, platform strategy, analytics, artificial intelligence (AI) and many other forces of disruption and innovation that shape today's realities of the world.These digital transformations are taking place at an exponential rate. The speed of innovations and breakthroughs is disrupting the traditional businesses. A better understanding of the changing environment in the new economy can enable business professionals and leaders to recognize realities, embrace changes, and create new opportunities — locally and globally — in this inevitable digital age. |
| data analytics as a service business model: Asset Intelligence through Integration and Interoperability and Contemporary Vibration Engineering Technologies Joseph Mathew, C.W. Lim, Lin Ma, Don Sands, Michael E. Cholette, Pietro Borghesani, 2018-11-11 These proceedings include a collection of papers on a range of topics presented at the 12th World Congress on Engineering Asset Management (WCEAM) in Brisbane, 2 – 4 August 2017. Effective strategies are required for managing complex engineering assets such as built environments, infrastructure, plants, equipment, hardware systems and components. Following the release of the ISO 5500x set of standards in 2014, the 12th WCEAM addressed important issues covering all aspects of engineering asset management across various sectors including health. The topics discussed by the congress delegates are grouped into a number of tracks, including strategies for investment and divestment of assets, operations and maintenance of assets, assessment of assets’ health conditions, risk and vulnerability, technologies, and systems for management of assets, standards, education, training and certification. |
| data analytics as a service business model: Business Intelligence Jerzy Surma, 2011-03-06 This book is about using business intelligence as a management information system for supporting managerial decision making. It concentrates primarily on practical business issues and demonstrates how to apply data warehousing and data analytics to support business decision making. This book progresses through a logical sequence, starting with data model infrastructure, then data preparation, followed by data analysis, integration, knowledge discovery, and finally the actual use of discovered knowledge. All examples are based on the most recent achievements in business intelligence. Finally this book outlines an overview of a methodology that takes into account the complexity of developing applications in an integrated business intelligence environment. This book is written for managers, business consultants, and undergraduate and postgraduates students in business administration. |
| data analytics as a service business model: Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work Management Association, Information Resources, 2020-10-30 As the use of remote work has recently skyrocketed, digital transformation within the workplace has gone under a microscope, and it has become abundantly clear that the incorporation of new technologies in the workplace is the future of business. These technologies keep businesses up to date with their capabilities to perform remote work and make processes more efficient and effective than ever before. In understanding digital transformation in the workplace there needs to be advanced research on technology, organizational change, and the impacts of remote work on the business, the employees, and day-to-day work practices. This advancement to a digital work culture and remote work is rapidly undergoing major advancements, and research is needed to keep up with both the positives and negatives to this transformation. The Research Anthology on Digital Transformation, Organizational Change, and the Impact of Remote Work contains hand-selected, previously published research that explores the impacts of remote work on business workplaces while also focusing on digital transformation for improving the efficiency of work. While highlighting work technologies, digital practices, business management, organizational change, and the effects of remote work on employees, this book is an all-encompassing research work intended for managers, business owners, IT specialists, executives, practitioners, stakeholders, researchers, academicians, and students interested in how digital transformation and remote work is affecting workplaces. |
| data analytics as a service business model: Competing on Analytics Thomas H. Davenport, Jeanne G. Harris, 2007-03-06 You have more information at hand about your business environment than ever before. But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool. In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling. Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics. |
| data analytics as a service business model: Handbook on Digital Platforms and Business Ecosystems in Manufacturing Sabine Baumann, 2024-03-14 This timely Handbook examines the rapidly expanding research area of digital platforms and business ecosystems in the context of manufacturing industries. Chapters analyze core topics such as business model transformation, ecosystem design, and governance, offering an up-to-date overview of crucial research. |
| data analytics as a service business model: Research Handbook on Services Management Davis, Mark M., 2022-08-05 This comprehensive Research Handbook reflects the latest research breakthroughs and practices in services management. Addressing services management from a broader strategic perspective, it delves into the key issues of analytics and service robots, and their potential impact. Edited by the late Mark M. Davis, it represents an early foray into the new frontier of services management and provides insights into the future of the field. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
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Belmont Forum
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Analytics Services Brochure
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The value of Big Data: How analytics differentiates winners
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INFOSYS ANALYTICS WORKBENCH
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People Analytics Maturity Assessment Framework - PwC
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Data Management Capability Assessment Model (DCAM)
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