| data-driven decision making case study: Management Decision-Making, Big Data and Analytics Simone Gressel, David J. Pauleen, Nazim Taskin, 2020-10-12 Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels. |
| data-driven decision making case study: Data-Driven Decision-Making in Schools: Lessons from Trinidad J. Yamin-Ali, 2014-01-24 Yamin-Ali shows how schools can undertake responsible decision-making through gathering and evaluating data, using as examples six fully developed case studies that shed light on common questions of school culture and student life, including student stress, subject selection, and the role of single-sex classes. |
| data-driven decision making case study: The Data-Driven Project Manager Mario Vanhoucke, 2018-03-27 Discover solutions to common obstacles faced by project managers. Written as a business novel, the book is highly interactive, allowing readers to participate and consider options at each stage of a project. The book is based on years of experience, both through the author's research projects as well as his teaching lectures at business schools. The book tells the story of Emily Reed and her colleagues who are in charge of the management of a new tennis stadium project. The CEO of the company, Jacob Mitchell, is planning to install a new data-driven project management methodology as a decision support tool for all upcoming projects. He challenges Emily and her team to start a journey in exploring project data to fight against unexpected project obstacles. Data-driven project management is known in the academic literature as “dynamic scheduling” or “integrated project management and control.” It is a project management methodology to plan, monitor, and control projects in progress in order to deliver them on time and within budget to the client. Its main focus is on the integration of three crucial aspects, as follows: Baseline Scheduling: Plan the project activities to create a project timetable with time and budget restrictions. Determine start and finish times of each project activity within the activity network and resource constraints. Know the expected timing of the work to be done as well as an expected impact on the project’s time and budget objectives. Schedule Risk Analysis: Analyze the risk of the baseline schedule and its impact on the project’s time and budget. Use Monte Carlo simulations to assess the risk of the baseline schedule and to forecast the impact of time and budget deviations on the project objectives. Project Control: Measure and analyze the project’s performance data and take actions to bring the project on track. Monitor deviations from the expected project progress and control performance in order to facilitate the decision-making process in case corrective actions are needed to bring projects back on track. Both traditional Earned Value Management (EVM) and the novel Earned Schedule (ES) methods are used. What You'll Learn Implement a data-driven project management methodology (also known as dynamic scheduling) which allows project managers to plan, monitor, and control projects while delivering them on time and within budget Study different project management tools and techniques, such as PERT/CPM, schedule risk analysis (SRA), resource buffering, and earned value management (EVM) Understand the three aspects of dynamic scheduling: baseline scheduling, schedule risk analysis, and project control Who This Book Is For Project managers looking to learn data-driven project management (or dynamic scheduling) via a novel, demonstrating real-time simulations of how project managers can solve common project obstacles |
| data-driven decision making case study: Transforming Teaching and Learning Through Data-Driven Decision Making Ellen B. Mandinach, Sharnell S. Jackson, 2012-04-10 Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management.--Publisher's description. |
| data-driven decision making case study: Evidence-based Initiatives for Organizational Change and Development Robert G. Hamlin, Bob Hamlin, Andrea D. Ellinger, Jenni Jones, 2019 Without change, there can be no progress. To influence change, organizations attempt to harmonize internally and become accustomed to dealing with a variety of situations that may require a number of solutions. Evidence-Based Initiatives for Organizational Change and Development discusses what helps or hinders the organizational-change-and-development-related agency and provides practical insights and lessons to be learned from many reflections on evidence-based OCD practice. Featuring research on topics such as human resource development, organizational behavior, and management consultancy, this book is ideally designed for business academics, organizational change leaders, line managers, HRD professionals, OD/management consultants, and executive coaches seeking coverage on the implementation of OCD intervention strategies and the associated changes in management processes. |
| data-driven decision making case study: Smart Healthcare System Design S. K. Hafizul Islam, Debabrata Samanta, 2021-06-29 SMART HEALTHCARE SYSTEM DESIGN This book deeply discusses the major challenges and issues for security and privacy aspects of smart health-care systems. The Internet-of-Things (IoT) has emerged as a powerful and promising technology, and though it has significant technological, social, and economic impacts, it also poses new security and privacy challenges. Compared with the traditional internet, the IoT has various embedded devices, mobile devices, a server, and the cloud, with different capabilities to support multiple services. The pervasiveness of these devices represents a huge attack surface and, since the IoT connects cyberspace to physical space, known as a cyber-physical system, IoT attacks not only have an impact on information systems, but also affect physical infrastructure, the environment, and even human security. The purpose of this book is to help achieve a better integration between the work of researchers and practitioners in a single medium for capturing state-of-the-art IoT solutions in healthcare applications, and to address how to improve the proficiency of wireless sensor networks (WSNs) in healthcare. It explores possible automated solutions in everyday life, including the structures of healthcare systems built to handle large amounts of data, thereby improving clinical decisions. The 14 separate chapters address various aspects of the IoT system, such as design challenges, theory, various protocols, implementation issues, as well as several case studies. Smart Healthcare System Design covers the introduction, development, and applications of smart healthcare models that represent the current state-of-the-art of various domains. The primary focus is on theory, algorithms, and their implementation targeted at real-world problems. It will deal with different applications to give the practitioner a flavor of how IoT architectures are designed and introduced into various situations. Audience: Researchers and industry engineers in information technology, artificial intelligence, cyber security, as well as designers of healthcare systems, will find this book very valuable. |
| data-driven decision making case study: Business Analytics: Data-Driven Decision Making , Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data-driven decision making case study: Data-based Decision Making in Education Kim Schildkamp, Mei Kuin Lai, Lorna Earl, 2012-09-18 In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students’ assessments, classroom observations etc. This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are ‘best practice’ studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field. |
| data-driven decision making case study: Data Driven Decision Making for Small Businesses Tracey Smith, 2012-07-02 The reader of this book need not be a mathematician. This book is intended for the business-minded individual interested in learning about the strategic advantages which can be obtained from business analytics.Small Business Trends magazine reported that, you don't need to be a Fortune 500 company with revenue in the stratosphere to benefit from the application of business intelligence. A simple analysis of data your business may already be collecting could hold the answer.Perhaps you would like to reduce your inventory, determine product and customer profitability, gain insight into customer ordering behaviour. Perhaps you would like to know where you are spending your business dollars or how to determine if your cash flow is getting better or worse. Is your business becoming more or less efficient as it grows? Perhaps you would like to predict upcoming retirements to determine the impact of the baby boomer generation on your organization.This book will present some of the simpler approaches to data analysis and will show the value of these analyses to business. The intent is to show the reader what is possible rather than teaching the mathematical techniques. From simple to the more advanced, this book will deliver a series of analytics suitable for anyone wishing to take their business to the next level.I will present a series of real-world case studies from various functional areas, the majority of which will be conducted with every day software that most businesses already possess.The book will go on to examine the advantages and disadvantages of trying to build these capabilities in-house and will provide a realistic view of the challenges associated with analytics in the business world.Finally, I will provide some advice on data analysis and visualization tools. Specifically, I will focus on the tools that are available to the reader for prices that are in line with typical office software. |
| data-driven decision making case study: The Power of Experiments Michael Luca, Max H. Bazerman, 2021-03-02 How tech companies like Google, Airbnb, StubHub, and Facebook learn from experiments in our data-driven world—an excellent primer on experimental and behavioral economics Have you logged into Facebook recently? Searched for something on Google? Chosen a movie on Netflix? If so, you've probably been an unwitting participant in a variety of experiments—also known as randomized controlled trials—designed to test the impact of different online experiences. Once an esoteric tool for academic research, the randomized controlled trial has gone mainstream. No tech company worth its salt (or its share price) would dare make major changes to its platform without first running experiments to understand how they would influence user behavior. In this book, Michael Luca and Max Bazerman explain the importance of experiments for decision making in a data-driven world. Luca and Bazerman describe the central role experiments play in the tech sector, drawing lessons and best practices from the experiences of such companies as StubHub, Alibaba, and Uber. Successful experiments can save companies money—eBay, for example, discovered how to cut $50 million from its yearly advertising budget—or bring to light something previously ignored, as when Airbnb was forced to confront rampant discrimination by its hosts. Moving beyond tech, Luca and Bazerman consider experimenting for the social good—different ways that governments are using experiments to influence or “nudge” behavior ranging from voter apathy to school absenteeism. Experiments, they argue, are part of any leader's toolkit. With this book, readers can become part of “the experimental revolution.” |
| data-driven decision making case study: The Intelligent Company Bernard Marr, 2010-03-10 Today's most successful companies are Intelligent Companies that use the best available data to inform their decision making. This is called Evidence-Based Management and is one of the fastest growing business trends of our times. Intelligent Companies bring together tools such as Business Intelligence, Analytics, Key Performance Indicators, Balanced Scorecards, Management Reporting and Strategic Decision Making to generate real competitive advantages. As information and data volumes grow at explosive rates, the challenges of managing this information is turning into a losing battle for most companies and they end up drowning in data while thirsting for insights. This is made worse by the severe skills shortage in analytics, data presentation and communication. This latest book by best-selling management expert Bernard Marr will equip you with a set of powerful skills that are vital for successful managers now and in the future. Increase your market value by gaining essential skills that are in high demand but in short supply. Loaded with practical step-by-step guidance, simple tools and real life examples of how leading organizations such as Google, CocaCola, Capital One, Saatchi & Saatchi, Tesco, Yahoo, as well as Government Departments and Agencies have put the principles into practice. The five steps to more intelligent decision making are: Step 1: More intelligent strategies by identifying strategic priorities and agreeing your real information needs Step 2: More intelligent data by creating relevant and meaningful performance indicators and qualitative management information linked back to your strategic information needs Step 3: More intelligent insights by using good evidence to test and prove ideas and by analysing the data to gain robust and reliable insights Step 4: More intelligent communication by creating informative and engaging management information packs and dashboards that provide the essential information, packaged in an easy-to-read way Step 5: More intelligent decision making by fostering an evidence-based culture of turning information into actionable knowledge and real decisions Bernard Marr did it again! This outstanding and practical book will help your company become more intelligent and more successful. Marr takes the fields of business-intelligence, analytics and scorecarding to bring them together into a powerful and easy-to-follow 5-step framework. The Intelligent Company is THE must-read book of our times. Bruno Aziza, Co-author of best-selling book Drive Business Performance and Worldwide Strategy Lead, Microsoft Business Intelligence Book after book Bernard Marr is redefining the fundamentals of good business management. The Intelligent Company is a must read in these changing times and a reference you will want on your desk every day! Gabriel Bellenger, Accenture Strategy |
| data-driven decision making case study: Data-Driven Decision-Making in Schools: Lessons from Trinidad J. Yamin-Ali, 2014-01-24 Yamin-Ali shows how schools can undertake responsible decision-making through gathering and evaluating data, using as examples six fully developed case studies that shed light on common questions of school culture and student life, including student stress, subject selection, and the role of single-sex classes. |
| data-driven decision making case study: Teaching to Change the World Jeannie Oakes, Martin Lipton, Lauren Anderson, Jamy Stillman, 2015-11-17 This is an up-to-the-moment, engaging, multicultural introduction to education and teaching and the challenges and opportunities they present. Together, the four authors bring a rich blend of theory and practical application to this groundbreaking text. Jeannie Oakes is a leading education researcher and former director of the UCLA teacher education program. Martin Lipton is an education writer and consultant and has taught in public schools for 31 years. Lauren Anderson and Jamy Stillman are former public school teachers, now working as teacher educators. This unique, comprehensive foundational text considers the values and politics that pervade the U.S. education system, explains the roots of conventional thinking about schooling and teaching, asks critical questions about how issues of power and privilege have shaped and continue to shape educational opportunity, and presents powerful examples of real teachers working for equity and justice. Taking the position that a hopeful, democratic future depends on ensuring that all students learn, the text pays particular attention to inequalities associated with race, social class, language, gender, and other social categories and explores teachers role in addressing them. The text provides a research-based and practical treatment of essential topics, and it situates those topics in relation to democratic values; issues of diversity; and cognitive, sociocultural, and constructivist perspectives on learning. The text shows how knowledge of education foundations and history can help teachers understand the organization of today s schools, the content of contemporary curriculum, and the methods of modern teaching. It likewise shows how teachers can use such knowledge when thinking about and responding to headline issues like charter schools, vouchers, standards, testing, and bilingual education, to name just a few. Central to this text is a belief that schools can and must be places of extraordinary educational quality and institutions in the service of social justice. Thus, the authors address head-on tensions between principles of democratic schooling and competition for always-scarce high-quality opportunities. Woven through the text are the voices of a diverse group of teachers, who share their analyses and personal anecdotes concerning what teaching to change the world means and involves. Click Here for Book Website Pedagogical Features: Digging Deeper sections referenced at the end of each chapter and featured online include supplementary readings and resources from scholars and practitioners who are addressing issues raised in the text. Instructor s Manual offers insights about how to teach course content in ways that are consistent with cognitive and sociocultural learning theories, culturally diverse pedagogy, and authentic assessment.New to this Edition: |
| data-driven decision making case study: 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 decision making case study: Efficient Learning Machines Mariette Awad, Rahul Khanna, 2015-04-27 Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning. |
| data-driven decision making case study: Data-Driven Decision Making in Fragile Contexts Alexander Hamilton, Craig Hammer, 2017-06-12 Data deficiencies contribute to state fragility and exacerbate fragile states’ already limited capacity to provide basic services, public security and rule of law. The lack of robust, good quality data can also have a disabling effect on government efforts to manage political conflict, and indeed can worsen conflict, since violent settings pose substantial challenges to knowledge generation, capture and application. In short, in fragile contexts the need for reliable evidence at all levels is perhaps greater than anywhere else. The development of sustainable and professional ‘data-literate’ stakeholders who are able to produce and increase the quality and accessibility of official statistics can contribute to improved development outcomes. Good quality and reliable statistics are also required to track the progress of development policies through the monitoring of performance indicators and targets and to ensure that public resources are achieving results. While data alone cannot have a transformative effect without the right contextual incentives it is an essential and necessary prerequisite for greater accountability and more efficient decision-making. This volume explores methods and insights for data collection and use in fragile contexts, with a focus on Sudan. It begins by posing several questions on the political economy of data, and then sets out a framework for assessing the validity, reliability, and potential impact of data on decision-making in a fragile country. It also sets out insights on challenges associated with fragile states, derived from recent data collected in Sudan: the 2014/2015 DFID Sudan household survey. This includes data-driven analysis of topics including female genital mutilation, public service delivery, and the interplay of governance, service quality, and state legitimacy. |
| data-driven decision making case study: Data-Driven Decision Making for Product Service Systems Giuditta Pezzotta, |
| data-driven decision making case study: Response to Intervention William N. Bender, Cara Shores, 2007-04-05 Of the many RTI materials published today, this one is user-friendly and much broader in scope. Written in clear and understandable yet professional language, this excellent book is appropriate for all K–12 educators and administrators. —Carla Osberg, Program Specialist, Special Populations Nebraska Department of Education Offers a unique organization of key concepts, and addresses current implementation issues with integrity. The strategies, suggestions, and tips contribute to the overall reader-friendliness of the book. The comparison/contrast of the problem-solving and standard treatment protocol approaches is well written and provides the reader information to determine the best approach for the students, school, or district. —Linda Palenchar, Coordinator, Office of Special Education West Virginia Department of Education Discover a resource that shows teachers how to implement RTI in the classroom! As a result of NCLB legislation and the reauthorization of IDEA 2004, Response to Intervention (RTI) is now a mandated process for documenting the existence or nonexistence of a learning disability. For educators new to the RTI approach, Response to Intervention presents an overview of key concepts with guidelines for accountability practices that benefit students in inclusive classrooms. Presenting the three tiers of RTI techniques, the authors demonstrate how general and special education teachers can use research-based interventions effectively to individualize instruction, monitor individual student progress, and implement strategies to meet the specific needs of all students. Response to Intervention assists educators with the basic and necessary steps to provide students with a Free Appropriate Public Education (FAPE) in the Least Restrictive Environment (LRE), and includes: Vignettes, examples, and forms based on the problem-solving and standards-based approaches to RTI A chapter illustrating how RTI techniques benefit students who are economically underprivileged and/or culturally and linguistically diverse A chapter devoted to Frequently Asked Questions Featuring helpful charts and reproducibles, this timely resource is sure to become a valuable guide as educators implement programs to document how individual students respond to specific educational interventions. |
| data-driven decision making case study: Bursting the Big Data Bubble Jay Liebowitz, 2014-07-25 As we get caught up in the quagmire of big data and analytics, it is important to be able to reflect and apply insights, experience, and intuition as part of the decision-making process. This book focuses on this intuition-based decision making. The first part of the book presents contributions from leading researchers worldwide on the topic of intuition-based decision making as applied to management. In the second part, executives and senior managers in industry, government, universities, and not-for-profits present vignettes that illustrate how they have used intuition in making key decisions. |
| data-driven decision making case study: Introduction to Organizational Behavior St. Clements University Academic Staff - Türkiye, Work motivation can be defined as the level of energy, commitment, and creativity that a company's workers bring to their roles. It is a multidimensional construct influenced by intrinsic and extrinsic factors, as well as contextual elements within the workplace. Theories of work motivation provide frameworks through which organizations can understand not only why employees are motivated but how they can enhance motivation levels to achieve organizational goals. |
| data-driven decision making case study: Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia Anthony M. Townsend, 2013-10-07 An unflinching look at the aspiring city-builders of our smart, mobile, connected future. From Beijing to Boston, cities are deploying smart technology—sensors embedded in streets and subways, Wi-Fi broadcast airports and green spaces—to address the basic challenges faced by massive, interconnected metropolitan centers. In Smart Cities, Anthony M. Townsend documents this emerging futuristic landscape while considering the motivations, aspirations, and shortcomings of the key actors—entrepreneurs, mayors, philanthropists, and software developers—at work in shaping the new urban frontier. |
| data-driven decision making case study: Handbook of Data-Based Decision Making in Education Theodore Kowalski, Thomas J. Lasley, 2010-04-15 Education has fought long and hard to gain acceptance as a profession and, since professionals by definition use data to shape the decisions they make, education has little choice but to continue moving in this direction. This 3-part handbook represents a major contribution to the literature of education. It is a unique compendium of the most original work currently available on how, when and why evidence should be used to ground practice. It is a comprehensive, cross-disciplinary, research-based, and practice-based resource that all educators can turn to as a guide to data-based decision making. The Handbook of Data-Based Decision Making in Education is a must read for researchers who are just beginning to explore the scientifically based nature of educational practice. It is also appropriate for policy makers and practitioners who are confronted with young people who need to be in classrooms where best practices are the norm and not the exception. |
| data-driven decision making case study: Big Data and Decision-Making Anna Visvizi, Orlando Troisi, Mara Grimaldi, 2023-01-30 Big Data and Decision-Making: Applications and Uses in the Public and Private Sector breaks down the concept of big data to reveal how it has become integrated into the fabric of both public and private domains, as well as how its value can ultimately be exploited. |
| data-driven decision making case study: Data-Driven School Improvement Ellen B. Mandinach, Margaret Honey, 2008 The first comprehensive examination of the field, this book brings together stakeholders representing a variety of perspectives to explore how educators actually use data and technology tools to achieve lasting improvement in student performance. Contributors: David V. Abbott, Carrie Amon, Jonathan Bertfield, Cornelia Brunner, Fred Carrigg, Jere Confrey, Katherine Conoly, Valerie M. Crawford, Chris Dede, John Gasko, Greg Gunn, Juliette Heinze, Naomi Hupert, Sherry P. King, Mary Jane Kurabinski, Daniel Light, Lisa Long, Michael Merrill, Liane Moody, William R. Penuel, Luz M. Rivas, Mark S. Schlager, John Stewart, Sam Stringfield, Ronald Thorpe, Yukie Toyama, Jeffrey C. Wayman, and Viki M. Young. If you want to understand usable knowledge, read Data-Driven School Improvement. Ellen Condliffe Lagemann, Harvard University It is reassuring to know that at least some of the data being generated in our data-driven age are being used to make wiser decisions. We can all learn from these illustrative accounts. David C. Berliner, Mary Lou Fulton College of Education, Arizona State University Replete with examples from real schools and districts, this volume provides a multi-layered portrait of what it takes to establish a culture of data use. Readers will come away with an appreciation of the systemic changes needed to reap the full potential of data-driven decision making. Barbara Means, Center for Technology in Learning, SRI International |
| data-driven decision making case study: Endovascular Surgery and Devices Zaiping Jing, Huajuan Mao, Weihui Dai, 2018-08-08 This book provides a systematic description of fundamental knowledge, application methods, and management issues about the clinical application of endovascular surgery and device. It is organized as the three parts. Part 1 introduces the development background of endovascular device and its knowledge hierarchy, and gives an overview on classification, structure, shape, and characteristics of the above device. Part 2 is based on a large number of clinical practices. It firstly summarizes the basic operation skills and conventional methods of endovascular device, and then exemplifies the application scheme of special device for complex cases. Part 3 discusses the management theory and methods of endovascular device in clinical application, puts forward the agile supply chain management model and autonomous intelligent decision-making method of device supply and cooperation management for clinical surgery, and designs its managerial system and guides. This book provides comprehensive and professional knowledge, advanced theory, and referential methods for clinical application and management of endovascular surgery and device. It is a useful guide for the clinical practice in specialized study and professional training in endovascular surgery, and provides the methods of neuro-management and smart medical service for patients. |
| data-driven decision making case study: Data-Driven Decision Making in Entrepreneurship Nikki Blackmith, Maureen E. McCusker, 2024-04-02 Since the beginning of the 21st century, there has been an explosion in startup organizations. Together, these organizations have been valued at over $3 trillion. In 2019, alone, nearly $300 billion of venture capital was invested globally (Global Startup Ecosystem Report 2020). Simultaneously, an explosion in high volume and high velocity of big data is rapidly changing how organizations function. Gone are the days where organizations can make decisions solely on intuition, logic, or experience. Some have gone as far as to say that data is the most valuable currency and resource available to businesses, and startups are no exception. However, startups and small businesses do differ from their larger counterparts and corporations in three distinct ways: 1) they tend to have fewer resources, time, and specialized training to devote to data analytics; 2) they are part of a unique entrepreneurial ecosystem with unique needs; 3) scholarship and academic research on human capital data analytics in startups is lacking. Existing entrepreneurship research focuses almost exclusively on macro-level aspects. There has been little to no integration of micro- and meso-level research (i.e., individual and team sciences), which is unfortunate given how organizational scientists have significantly advanced human capital data analytics. Unlike other books focused on data analytics and decision for organizations, this proposed book is purposefully designed to be more specifically aimed at addressing the unique idiosyncrasies of the science, research, and practice of startups. Each chapter highlights a specific organizational domain and discuss how a novel data analytic technique can help enhance decision-making, provides a tutorial of said regarding the data analytic technique, and lists references and resources for the respective data analytic technique. The volume will be grounded in sound theory and practice of organizational psychology, entrepreneurship and management and is divided into two parts: assessing and evaluating human capital performance and the use of data analytics to manage human capital. |
| data-driven decision making case study: Microsoft Certified Exam guide - Power BI Certified (DA-100) Cybellium Ltd, Unleash the Full Potential of Data Visualization with Power BI! Are you ready to become a certified Power BI professional and elevate your skills in the world of data analysis and visualization? Look no further than the Microsoft Certified Exam Guide - Power BI Certified (DA-100). This comprehensive book is your ultimate companion on the journey to mastering Power BI and acing the DA-100 exam. In today's data-driven business landscape, the ability to transform raw data into actionable insights is a game-changer. Microsoft's Power BI is a leading business intelligence tool, and organizations worldwide rely on it to make informed decisions. Whether you're a data enthusiast, an analyst, or a business professional, this book equips you with the knowledge and skills needed to excel in Power BI. Inside this book, you will discover: ✔ Comprehensive Coverage: A deep dive into all the essential concepts, tools, and best practices for creating stunning data visualizations and reports with Power BI. ✔ Real-World Scenarios: Practical examples and case studies that showcase how Power BI is used to turn data into meaningful insights, making learning engaging and relevant. ✔ Exam-Ready Preparation: Thorough coverage of DA-100 exam objectives, complete with practice questions and expert tips to ensure you're well-prepared for exam day. ✔ Proven Expertise: Authored by Power BI professionals who hold the certification and have extensive experience in designing impactful data solutions, offering you invaluable insights and practical guidance. Whether you aim to enhance your career, validate your expertise, or simply become a proficient Power BI user, Microsoft Certified Exam Guide - Power BI Certified (DA-100) is your trusted companion on this journey. Don't miss this opportunity to become a sought-after data visualization expert in a competitive job market. Prepare, practice, and succeed with the ultimate resource for DA-100 certification. Order your copy today and unlock a world of data-driven possibilities with Power BI! © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data-driven decision making case study: Monitoring the Quality of Education in Schools Vanessa Scherman, Roel J. Bosker, Sarah J. Howie, 2016-12-27 The monitoring of quality has been part of the educational landscape for many decades. Originally the need to monitor arose as part of an economic process whereby policy makers wanted to discern the return on investment in education. This bottom line thinking, while still prominent, has receded into the background in light of global changes and the emergence of a global economy. Now in addition to the question “what is the return on investment?”, the more important question is “are the students in schools ready to participate in the economy of a 21st century society?”. This is underpinned by the inquiry into what knowledge and competencies are required for students to participate meaningfully in nation-building. This inquiry can only be undertaken by means of monitoring, evaluating where the students are and what is required so that students reach their potential. In an ever-changing technologically-oriented world the manner in which competencies and knowledge are identified and how these need to be measured and identified is important. In this book, the theory and practice of underpinning the monitoring of the quality of education is described. This is followed by a number of practical examples, in the form of country case studies, on how theory plays out in practice. The book further provides common themes across developed and developing emerging economies underscoring the need for approaches which are locally relevant but internationally transferable. |
| data-driven decision making case study: Data-Driven Leadership Amanda Datnow, Vicki Park, 2014-03-10 Tools and techniques from the trailblazers in data-based education reform Over a period of several years, Amanda Datnow and Vicki Park visited public schools with a reputation for being ahead of the pack in data-driven decision making. The results of this pioneering study reveal how education leaders can make data work for students and teachers, rather than against them. This book is an essential guide to meeting the challenges of high-stakes accountability, building performance-based schools, and improving student outcomes. By following the advice in this book, you’ll be able to transform data overload into a data-positive school culture. You’ll learn the difference between “data-driven leadership” and “data-informed leadership,” and how to use distributed leadership to inspire collaboration and guided analysis. Incorporating narrative reflections drawn from real educators and administrators, the authors refine their observations and interviews into practical conclusions that leaders can put to use immediately. This book empowers leaders to support inquiry, build trust in data-based initiatives, establish goals for evidence use, and provide educators with the skills they need to mobilize data for the good of all stakeholders. “Datnow and Park’s ideas are easily accessible and grounded in clear examples, and their seven ‘calls’ about what needs to be done nail the problem and the solutions. Use this book as your action guide and you’ll be rewarded with better results in student learning.” —Michael Fullan, professor emeritus, University of Toronto “Datnow and Park uncover, at last, what it means to use data to inform leadership. Documenting the four P’s (people, policies, practices, and patterns) in schools, we learn about the organization and dynamics of reform informed by data. A must read!” —Ann Lieberman, senior scholar, Stanford University |
| data-driven decision making case study: Data-Driven Situational Awareness and Decision Making for Smart Grid Operation Lipeng Zhu, Yuchen Zhang, Yue Song, Xinran Zhang, Xue Lyu, 2023-10-05 |
| data-driven decision making case study: Science and Decisions National Research Council, Division on Earth and Life Studies, Board on Environmental Studies and Toxicology, Committee on Improving Risk Analysis Approaches Used by the U.S. EPA, 2009-03-24 Risk assessment has become a dominant public policy tool for making choices, based on limited resources, to protect public health and the environment. It has been instrumental to the mission of the U.S. Environmental Protection Agency (EPA) as well as other federal agencies in evaluating public health concerns, informing regulatory and technological decisions, prioritizing research needs and funding, and in developing approaches for cost-benefit analysis. However, risk assessment is at a crossroads. Despite advances in the field, risk assessment faces a number of significant challenges including lengthy delays in making complex decisions; lack of data leading to significant uncertainty in risk assessments; and many chemicals in the marketplace that have not been evaluated and emerging agents requiring assessment. Science and Decisions makes practical scientific and technical recommendations to address these challenges. This book is a complement to the widely used 1983 National Academies book, Risk Assessment in the Federal Government (also known as the Red Book). The earlier book established a framework for the concepts and conduct of risk assessment that has been adopted by numerous expert committees, regulatory agencies, and public health institutions. The new book embeds these concepts within a broader framework for risk-based decision-making. Together, these are essential references for those working in the regulatory and public health fields. |
| data-driven decision making case study: Data Science for Beginners: A Hands-On Guide to Big Data Michael Roberts, Unlock the power of data with Data Science for Beginners: A Hands-On Guide to Big Data. This comprehensive guide introduces you to the world of data science, covering everything from the basics of data collection and preparation to advanced machine learning techniques and practical data science projects. Whether you're new to the field or looking to enhance your skills, this book provides step-by-step instructions, real-world examples, and best practices to help you succeed. Discover the tools and technologies used by data scientists, learn how to analyze and visualize data, and explore the vast opportunities that data science offers in various industries. Start your data science journey today and transform data into actionable insights. |
| data-driven decision making case study: Reshaping Environmental Science Through Machine Learning and IoT Gupta, Rajeev Kumar, Jain, Arti, Wang, John, Pateriya, Rajesh Kumar, 2024-05-06 In the face of escalating environmental challenges such as climate change, air and water pollution, and natural disasters, traditional approaches to understanding and addressing these issues have yet to be proven sufficient. Academic scholars are compelled to seek innovative solutions that marry digital intelligence and natural ecosystems. Reshaping Environmental Science Through Machine Learning and IoT serves as a comprehensive exploration into the transformative potential of Machine Learning (ML) and the Internet of Things (IoT) to address critical environmental challenges. The book establishes a robust foundation in ML and IoT, explaining their relevance to environmental science. As the narrative unfolds, it delves into diverse applications, providing theoretical insights alongside practical knowledge. From interpreting weather patterns to predicting air and water quality, the book navigates through the intricate web of environmental complexities. Notably, it unveils approaches to disaster management, waste sorting, and climate change monitoring, showcasing the symbiotic relationship between digital intelligence and natural ecosystems. This book is ideal for audiences from students and researchers to data scientists and disaster management professionals with a nuanced understanding of IoT, ML, and Artificial Intelligence (AI). |
| data-driven decision making case study: Navigating Tomorrow - The Future of Product Management and Artifical Intelligence - A Handbook for Product Managers Sajjad Ahmad, 2024-01-04 In the ever-evolving terrain of technology, the convergence of artificial intelligence (AI) and product management has become a defining feature of the contemporary business landscape. The symbiotic relationship between these two domains has ushered in a new era of innovation, challenging traditional paradigms and reshaping the way products are conceptualized, developed, and delivered to users. As we stand at the crossroads of technological advancement, it is crucial to understand the intricate interplay between AI and product management. Product managers are no longer mere architects of features; they are orchestrators of intelligent systems, harnessing the power of machine learning, data analytics, and automation to redefine user experiences and business processes. This journey into the future of product management and AI is not merely a forecast but a roadmap that navigates the complex terrains of opportunities and challenges. It requires a keen understanding of the profound changes AI introduces to the product development lifecycle, the impact on user-centric design, and the transformative nature of data-driven decision-making. The landscape we traverse is marked by the fusion of human ingenuity with the capabilities of machines. It's a realm where algorithms augment creativity, where data becomes the currency of innovation, and where products evolve dynamically in response to real-time insights. As we embark on this exploration, it is imperative to grasp the significance of AI not as a standalone entity but as an integral force shaping the future trajectory of product management. This book seeks to unravel the intricacies of this evolving landscape, offering a comprehensive guide for product managers, business leaders, and enthusiasts who aim to harness the potential of AI to propel their products into the future. We will delve into the collaborative synergy between human expertise and machine intelligence, explore ethical considerations in design, navigate the challenges of agile methodologies in AI projects, and decipher the critical role of continuous learning in staying ahead of the curve. Join us on this expedition as we define the landscape where the pulse of AI harmonizes with the heartbeat of product management. Through insights, strategies, and real-world case studies, we aim to empower you to not only adapt to this changing landscape but to thrive within it, creating products that resonate with users in a world where the only constant is relentless innovation. |
| data-driven decision making case study: Family and Community Partnerships Margaret Caspe, Reyna Hernandez, 2023-08-01 Family and Community Partnerships: Promising Practices for Teachers and Teacher Educators, offers a fresh new look at the competencies, strategies, and practices that effective educators develop to build strong partnerships with families and communities. Written by leaders in the field, the book is an outgrowth of a cutting-edge initiative led by the National Association for Family, School, and Community Engagement to reimagine how educators are prepared for family and community engagement. Based on four guiding practices - reflect, connect, collaborate, and lead alongside families – each section of the book highlights theory, real-world strategies, discussion questions, and activities that can be used by teachers, teacher educators, and professional learning specialists to inspire new ideas for courses, workshops, and for self-reflection. |
| data-driven decision making case study: AI-Oriented Competency Framework for Talent Management in the Digital Economy Alex Khang, 2024-05-29 In the digital-driven economy era, an AI-oriented competency framework (AIoCF) is a collection to identify AI-oriented knowledge, attributes, efforts, skills, and experiences (AKASE) that directly and positively affect the success of employees and the organization. The application of skills-based competency analytics and AI-equipped systems is gradually becoming accepted by business and production organizations as an effective tool for automating several managerial activities consistently and efficiently in developing and moving the capacity of a company up to a world-class level. AI-Oriented Competency Framework for Talent Management in the Digital Economy: Models, Technologies, Applications, and Implementation discusses all the points of an AIoCF, which includes predictive analytics, advisory services, predictive maintenance, and automated processes, which help to make the operations of project management, personnel management, or administration more efficient, profitable, and safe. The book includes the functionality of emerging career pathways, hybrid learning models, and learning paths related to the learning and development of employees in the production or delivery fields. It also presents the relationship between skills taxonomy and competency framework with interactive methods using datasets, processing workflow diagrams, and architectural diagrams for easy understanding of the application of intelligent functions in role-based competency systems. By also covering upcoming areas of AI and data science in many government and private organizations, the book not only focuses on managing big data and cloud resources of the talent management system but also provides cybersecurity techniques to ensure that systems and employee competency data are secure. This book targets a mixed audience of students, engineers, scholars, researchers, academics, and professionals who are learning, researching, and working in the field of workforce training, human resources, talent management systems, requirement, headhunting, outsourcing, and manpower consultant services from different cultures and industries in the era of digital economy. |
| data-driven decision making case study: Data-based Decision Making in Education Kim Schildkamp, Mei Kuin Lai, Lorna Earl, 2012-09-17 In a context where schools are held more and more accountable for the education they provide, data-based decision making has become increasingly important. This book brings together scholars from several countries to examine data-based decision making. Data-based decision making in this book refers to making decisions based on a broad range of evidence, such as scores on students’ assessments, classroom observations etc. This book supports policy-makers, people working with schools, researchers and school leaders and teachers in the use of data, by bringing together the current research conducted on data use across multiple countries into a single volume. Some of these studies are ‘best practice’ studies, where effective data use has led to improvements in student learning. Others provide insight into challenges in both policy and practice environments. Each of them draws on research and literature in the field. |
| data-driven decision making case study: Driven by Data Paul Bambrick-Santoyo, 2010-04-12 Offers a practical guide for improving schools dramatically that will enable all students from all backgrounds to achieve at high levels. Includes assessment forms, an index, and a DVD. |
| data-driven decision making case study: Using Web and Paper Questionnaires for Data-Based Decision Making Susan J. Thomas, 2004-03-06 Excerpt: ...tribe. He had faculties. He had also various idiosyncrasies. He was undeniably the best hunter and trapper and trainer of dogs to sledge, as well as the most expert upon snowshoes of all the Indians living upon the point, and he was, furthermore, one of the dirtiest of them and the biggest drunkard whenever opportunity afforded. Fortunately for him and for his squaw, Bigbeam, as she had been facetiously named by an agent of the company, the opportunities for getting drunk were rare, for the company is conservative in the distribution of that which makes bad hunters. Given an abundance of firewater and tobacco, Red Dog was the happiest Indian between the northern boundary of the United States and Lake Gary; deprived of them both he hunted vigorously, thinking all the while of the coming hour when, after a long journey and much travail, he should be in what was his idea of heaven again. To-day, though, the rifle bought from the company stood idle beside the ridge-pole, the sledge dogs snarled and fought upon the snow outside, and Bigbeam, squat and broad as became her name, looked askance at her lord as she prepared the moose meat, uncertain of his temper, for his face was cloudy. Red Dog was, in fact, perplexed, and was planning deeply. Good reason was there for Red Dog's thought. Events of the immediate future were of moment to him and all his fellows, among whom, though no chief was formally acknowledged, he was recognized as leader; for had he not at one time been with the company as a hired hunter? Had he not once gone with a fur-carrying party even to Hudson's Bay, and thence to the far south and even to Quebec? And did he not know the ways of the company, and could not he talk a French patois which enabled him to be understood at the stations? Now, as fitting representative of himself and of his clan, a great responsibility had come upon him, and he was lost in as anxious thought as could come to a biped of his quality. Like a more or less... |
| data-driven decision making case study: Real-World Decision Support Systems Jason Papathanasiou, Nikolaos Ploskas, Isabelle Linden, 2016-12-19 This book presents real-world decision support systems, i.e., systems that have been running for some time and as such have been tested in real environments and complex situations; the cases are from various application domains and highlight the best practices in each stage of the system’s life cycle, from the initial requirements analysis and design phases to the final stages of the project. Each chapter provides decision-makers with recommendations and insights into lessons learned so that failures can be avoided and successes repeated. For this reason unsuccessful cases, which at some point of their life cycle were deemed as failures for one reason or another, are also included. All decision support systems are presented in a constructive, coherent and deductive manner to enhance the learning effect. It complements the many works that focus on theoretical aspects or individual module design and development by offering ‘good’ and ‘bad’ practices when developing and using decision support systems. Combining high-quality research with real-world implementations, it is of interest to researchers and professionals in industry alike. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a Transnationa…
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; …
Belmont Forum Adopts Open Data Principles for Environmental Chan…
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and …
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 …
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 …
