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Analytics and Business Performance: Transforming the Ability to Compete on Analytics into a Lasting Competitive Advantage
by Thomas H. Davenport Jeanne G. HarrisThis chapter explores the links between the extensive use of analytics and business performance, describing how several highly successful companies have transformed their ability to compete analytically into a key point of differentiation and long-term competitive advantage. This chapter was originally published as Chapter 3 of "Competing on Analytics."
Analytics and Data Science: Advances in Research and Pedagogy (Annals of Information Systems #21)
by Ashish Gupta Lakshmi S. Iyer Amit V. Deokar Mary C. JonesThis book explores emerging research and pedagogy in analytics and data science that have become core to many businesses as they work to derive value from data. The chapters examine the role of analytics and data science to create, spread, develop and utilize analytics applications for practice. Selected chapters provide a good balance between discussing research advances and pedagogical tools in key topic areas in analytics and data science in a systematic manner. This book also focuses on several business applications of these emerging technologies in decision making, i. e. , business analytics. The chapters in Analytics and Data Science: Advances in Research and Pedagogy are written by leading academics and practitioners that participated at the Business Analytics Congress 2015. Applications of analytics and data science technologies in various domains are still evolving. For instance, the explosive growth in big data and social media analytics requires examination of the impact of these technologies and applications on business and society. As organizations in various sectors formulate their IT strategies and investments, it is imperative to understand how various analytics and data science approaches contribute to the improvements in organizational information processing and decision making. Recent advances in computational capacities coupled by improvements in areas such as data warehousing, big data, analytics, semantics, predictive and descriptive analytics, visualization, and real-time analytics have particularly strong implications on the growth of analytics and data science.
Analytics and Decision Support in Health Care Operations Management
by Yasar A. OzcanA compendium of health care quantitative techniques based in Excel Analytics and Decision Support in Health Care Operations is a comprehensive introductory guide to quantitative techniques, with practical Excel-based solutions for strategic health care management. This new third edition has been extensively updated to reflect the continuously evolving field, with new coverage of predictive analytics, geographical information systems, flow process improvement, lean management, six sigma, health provider productivity and benchmarking, project management, simulation, and more. Each chapter includes additional new exercises to illustrate everyday applications, and provides clear direction on data acquisition under a variety of hospital information systems. Instructor support includes updated Excel templates, PowerPoint slides, web based chapter end supplements, and data banks to facilitate classroom instruction, and working administrators will appreciate the depth and breadth of information with clear applicability to everyday situations. The ability to use analytics effectively is a critical skill for anyone involved in the study or practice of health services administration. This book provides a comprehensive set of methods spanning tactical, operational, and strategic decision making and analysis for both current and future health care administrators. Learn critical analytics and decision support techniques specific to health care administration Increase efficiency and effectiveness in problem-solving and decision support Locate appropriate data in different commonly-used hospital information systems Conduct analyses, simulations, productivity measurements, scheduling, and more From statistical techniques like multiple regression, decision-tree analysis, queuing and simulation, to field-specific applications including surgical suite scheduling, roster management, quality monitoring, and more, analytics play a central role in health care administration. Analytics and Decision Support in Health Care Operations provides essential guidance on these critical skills that every professional needs.
Analytics and Dynamic Customer Strategy
by John F. Tanner Jr.Key decisions determine the success of big data strategyDynamic Customer Strategy: Big Profits from Big Data is a comprehensive guide to exploiting big data for both business-to-consumer and business-to-business marketing. This complete guide provides a process for rigorous decision making in navigating the data-driven industry shift, informing marketing practice, and aiding businesses in early adoption. Using data from a five-year study to illustrate important concepts and scenarios along the way, the author speaks directly to marketing and operations professionals who may not necessarily be big data savvy. With expert insight and clear analysis, the book helps eliminate paralysis-by-analysis and optimize decision making for marketing performance.Nearly seventy-five percent of marketers plan to adopt a big data analytics solution within two years, but many are likely to fail. Despite intensive planning, generous spending, and the best intentions, these initiatives will not succeed without a manager at the helm who is capable of handling the nuances of big data projects. This requires a new way of marketing, and a new approach to data. It means applying new models and metrics to brand new consumer behaviors. Dynamic Customer Strategy clarifies the situation, and highlights the key decisions that have the greatest impact on a company's big data plan. Topics include:Applying the elements of Dynamic Customer StrategyAcquiring, mining, and analyzing dataMetrics and models for big data utilizationShifting perspective from model to customerBig data is a tremendous opportunity for marketers and may just be the only factor that will allow marketers to keep pace with the changing consumer and thus keep brands relevant at a time of unprecedented choice. But like any tool, it must be wielded with skill and precision. Dynamic Customer Strategy: Big Profits from Big Data helps marketers shape a strategy that works.
Analytics and Knowledge Management (Data Analytics Applications)
by Suliman Hawamdeh and Hsia-Ching ChangThe process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics technique. Analytics and Knowledge Management examines the role of analytics in knowledge management and the integration of big data theories, methods, and techniques into an organizational knowledge management framework. Its chapters written by researchers and professionals provide insight into theories, models, techniques, and applications with case studies examining the use of analytics in organizations. The process of transforming data into actionable knowledge is a complex process that requires the use of powerful machines and advanced analytics techniques. Analytics, on the other hand, is the examination, interpretation, and discovery of meaningful patterns, trends, and knowledge from data and textual information. It provides the basis for knowledge discovery and completes the cycle in which knowledge management and knowledge utilization happen. Organizations should develop knowledge focuses on data quality, application domain, selecting analytics techniques, and on how to take actions based on patterns and insights derived from analytics. Case studies in the book explore how to perform analytics on social networking and user-based data to develop knowledge. One case explores analyze data from Twitter feeds. Another examines the analysis of data obtained through user feedback. One chapter introduces the definitions and processes of social media analytics from different perspectives as well as focuses on techniques and tools used for social media analytics. Data visualization has a critical role in the advancement of modern data analytics, particularly in the field of business intelligence and analytics. It can guide managers in understanding market trends and customer purchasing patterns over time. The book illustrates various data visualization tools that can support answering different types of business questions to improve profits and customer relationships. This insightful reference concludes with a chapter on the critical issue of cybersecurity. It examines the process of collecting and organizing data as well as reviewing various tools for text analysis and data analytics and discusses dealing with collections of large datasets and a great deal of diverse data types from legacy system to social networks platforms.
Analytics and Optimization for Renewable Energy Integration (Energy Analytics)
by Ning Zhang Yi Wang Chongqing Kang Ershun DuThe scope of this book covers the modeling and forecast of renewable energy and operation and planning of power system with renewable energy integration.The first part presents mathematical theories of stochastic mathematics; the second presents modeling and analytic techniques for renewable energy generation; the third provides solutions on how to handle the uncertainty of renewable energy in power system operation. It includes advanced stochastic unit commitment models to acquire the optimal generation schedule under uncertainty, efficient algorithms to calculate the probabilistic power, and an efficient operation strategy for renewable power plants participating in electricity markets.
Analytics at Work
by Thomas H. Davenport Jeanne G. Harris Robert MorisonMost companies have massive amounts of data at their disposal, yet fail to utilize it in any meaningful way. But a powerful new business tool - analytics - is enabling many firms to aggressively leverage their data in key business decisions and processes, with impressive results.In their previous book, Competing on Analytics, Thomas Davenport and Jeanne Harris showed how pioneering firms were building their entire strategies around their analytical capabilities. Rather than "going with the gut" when pricing products, maintaining inventory, or hiring talent, managers in these firms use data, analysis, and systematic reasoning to make decisions that improve efficiency, risk-management, and profits.Now, in Analytics at Work, Davenport, Harris, and coauthor Robert Morison reveal how any manager can effectively deploy analytics in day-to-day operations-one business decision at a time. They show how many types of analytical tools, from statistical analysis to qualitative measures like systematic behavior coding, can improve decisions about everything from what new product offering might interest customers to whether marketing dollars are being most effectively deployed.Based on all-new research and illustrated with examples from companies including Humana, Best Buy, Progressive Insurance, and Hotels.com, this implementation-focused guide outlines the five-step DELTA model for deploying and succeeding with analytical initiatives. You'll learn how to:· Use data more effectively and glean valuable analytical insights· Manage and coordinate data, people, and technology at an enterprise level· Understand and support what analytical leaders do· Evaluate and choose realistic targets for analytical activity· Recruit, hire, and manage analystsCombining the science of quantitative analysis with the art of sound reasoning, Analytics at Work provides a road map and tools for unleashing the potential buried in your company's data.
Analytics for Customer Insights: A Non-Technical Introduction
by Chuck ChakrapaniThis book is a quick an easy introduction to the techniques used in marketing research. Using no mathematics or formulas, this book explains all major analytic techniques used in marketing research. The techniques explained in this book include: - Factor Analysis - Discriminant Analysis - Boosting - Logistic Regression - Turf Analysis - Regression Analysis - Price Sensitivity Meter - Random Forest - MaxDiff - Decision Trees - Perceptual Mapping - Structural Equation (SEM) - Cluster Analysis - Conjoint Analysis - Support Vector Machines - Naive Bayes - Path Analysis - Artificial Neural Network
Analytics for Insurance: The Real Business of Big Data
by Tony BoobierThe business guide to Big Data in insurance, with practical application insight Big Data and Analytics for Insurers is the industry-specific guide to creating operational effectiveness, managing risk, improving financials, and retaining customers. Written from a non-IT perspective, this book focusses less on the architecture and technical details, instead providing practical guidance on translating analytics into target delivery. The discussion examines implementation, interpretation, and application to show you what Big Data can do for your business, with insights and examples targeted specifically to the insurance industry. From fraud analytics in claims management, to customer analytics, to risk analytics in Solvency 2, comprehensive coverage presented in accessible language makes this guide an invaluable resource for any insurance professional. The insurance industry is heavily dependent on data, and the advent of Big Data and analytics represents a major advance with tremendous potential - yet clear, practical advice on the business side of analytics is lacking. This book fills the void with concrete information on using Big Data in the context of day-to-day insurance operations and strategy. Understand what Big Data is and what it can do Delve into Big Data's specific impact on the insurance industry Learn how advanced analytics can revolutionise the industry Bring Big Data out of IT and into strategy, management, marketing, and more Big Data and analytics is changing business - but how? The majority of Big Data guides discuss data collection, database administration, advanced analytics, and the power of Big Data - but what do you actually do with it? Big Data and Analytics for Insurers answers your questions in real, everyday business terms, tailored specifically to the insurance industry's unique needs, challenges, and targets.
Analytics for Leaders
by N. I. FisherAnalytics for Leaders provides a concise, readable account of a complete system of performance measurement for an enterprise. Based on over 20 years of research and development, the system is designed to provide people at all levels with the quantitative information they need to do their jobs: board members to exercise due diligence about all facets of the business, leaders to decide where to focus attention next, and people to carry out their work well. For senior officers, chapter openers provide quick overviews about the overall approach to a particular stakeholder group and how to connect overall performance measures to business impact. For MBA students, extensive supporting notes and references provide in-depth understanding. For researchers and practitioners, a generic statistical approach is described to encourage new ways of tackling performance measurement issues. The book is relevant to all types of enterprise, large or small, public or private, academic or governmental.
Analytics for Managers: With Excel
by Gregory S. Zaric Peter C. BellAnalytics is one of a number of terms which are used to describe a data-driven more scientific approach to management. Ability in analytics is an essential management skill: knowledge of data and analytics helps the manager to analyze decision situations, prevent problem situations from arising, identify new opportunities, and often enables many millions of dollars to be added to the bottom line for the organization. The objective of this book is to introduce analytics from the perspective of the general manager of a corporation. Rather than examine the details or attempt an encyclopaedic review of the field, this text emphasizes the strategic role that analytics is playing in globally competitive corporations today. The chapters of this book are organized in two main parts. The first part introduces a problem area and presents some basic analytical concepts that have been successfully used to address the problem area. The objective of this material is to provide the student, the manager of the future, with a general understanding of the tools and techniques used by the analyst.
Analytics for Retail: A Step-by-Step Guide to the Statistics Behind a Successful Retail Business
by Rhoda OkunevExamine select retail business scenarios to learn basic mathematics, as well as probability and statistics required to analyze big data. This book focuses on useful and imperative applied analytics needed to build a retail business and explains mathematical concepts essential for decision making and communication in retail business environments. Everyone is a buyer or seller of products these days whether through a physical department store, Amazon, or their own business website. This book is a step-by-step guide to understanding and managing the mechanics of markups, markdowns, and basic statistics, math and computers that will help in your retail business. You'll tackle what to do with data once it is has accumulated and see how to arrange the data using descriptive statistics, primarily means, median, and mode, and then how to read the corresponding charts and graphs. Analytics for Retail is your path to creating visual representations that powerfully communicate information and drive decisions. What You'll LearnReview standard statistical concepts to enhance your understanding of retail dataUnderstand the concepts of markups, markdowns and profit margins, and probability Conduct an A/B testing email campaign with all the relevant analytics calculated and explainedWho This Book Is ForThis is a primer book for anyone in the field of retail that needs to learn or refresh their skills or for a reader who wants to move in their company to a more analytical position.
Analytics for Smart Energy Management: Tools and Applications for Sustainable Manufacturing (Springer Series in Advanced Manufacturing)
by Seog-Chan Oh Alfred J. HildrethThis book introduces the issues and problems that arise when implementing smart energy management for sustainable manufacturing in the automotive manufacturing industry and the analytical tools and applications to deal with them. It uses a number of illustrative examples to explain energy management in automotive manufacturing, which involves most types of manufacturing technology and various levels of energy consumption. It demonstrates how analytical tools can help improve energy management processes, including forecasting, consumption, and performance analysis, emerging new technology identification as well as investment decisions for establishing smart energy consumption practices. It also details practical energy management systems, making it a valuable resource for professionals involved in real energy management processes, and allowing readers to implement the procedures and applications presented.
Analytics for the Internet of Things (IoT)
by Andrew MinteerThis book targets developers, IoT professionals, and those in the field of data science who are trying to solve business problems through IoT devices and would like to analyze IoT data. IoT enthusiasts, managers, and entrepreneurs who would like to make the most of IoT will find this equally useful. A prior knowledge of IoT would be helpful but is not necessary. Some prior programming experience would be useful
Analytics for the Sharing Economy: Mathematics, Engineering and Business Perspectives
by Giovanni Russo Emanuele Crisostomi Robert Shorten Bissan Ghaddar Florian Häusler Joe Naoum-SawayaThe book provides an encompassing overview of all aspects relating to the sharing economy paradigm in different fields of study, and shows the ongoing research efforts in filling previously identified gaps in understanding in this area. Control and optimization analytics for the sharing economy explores bespoke analytics, tools, and business models that can be used to help design collaborative consumption services (the shared economy). It provides case studies of collaborative consumption in the areas of energy and mobility.The contributors review successful examples of sharing systems, and explore the theory for designing effective and stable shared-economy models. They discuss recent innovations in and uses of shared economy models in niche areas, such as energy and mobility. Readers learn the scientific challenging issues associated with the realization of a sharing economy. Conceptual and practical matters are examined, and the state-of-the-art tools and techniques to address such applications are explained. The contributors also show readers how topical problems in engineering, such as energy consumption in power grids, or bike sharing in transportation networks, can be formulated and solved from a general collaborative consumption perspective. Since the book takes a mathematical perspective to the topic, researchers in business, computer science, optimization and control find it useful. Practitioners also use the book as a point of reference, as it explores and investigates the analytics behind economy sharing.
Analytics in Finance and Risk Management (Information Technology, Management and Operations Research Practices)
by Ewa Ziemba Shivani Agarwal Nga Thi Hong NguyenThis book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management. Analytics in Finance and Risk Management covers statistical techniques for data analysis in finance It explores applications in finance and risk management, covering empirical properties of financial systems. It addresses data science involving the study of statistical and computational models and includes basic and advanced concepts. The chapters incorporate the latest methodologies and challenges facing financial and risk management and illustrate related issues and their implications in the real world. The primary users of this book will include researchers, academicians, postgraduate students, professionals in engineering and business analytics, managers, consultants, and advisors in IT firms, financial markets, and services domains.
Analytics in Healthcare: A Practical Introduction (SpringerBriefs in Health Care Management and Economics)
by Christo El Morr Hossam Ali-HassanThis book offers a practical introduction to healthcare analytics that does not require a background in data science or statistics. It presents the basics of data, analytics and tools and includes multiple examples of their applications in the field. The book also identifies practical challenges that fuel the need for analytics in healthcare as well as the solutions to address these problems. In the healthcare field, professionals have access to vast amount of data in the form of staff records, electronic patient record, clinical findings, diagnosis, prescription drug, medical imaging procedure, mobile health, resources available, etc. Managing the data and analyzing it to properly understand it and use it to make well-informed decisions can be a challenge for managers and health care professionals. A new generation of applications, sometimes referred to as end-user analytics or self-serve analytics, are specifically designed for non-technical users such as managers and business professionals. The ability to use these increasingly accessible tools with the abundant data requires a basic understanding of the core concepts of data, analytics, and interpretation of outcomes. This book is a resource for such individuals to demystify and learn the basics of data management and analytics for healthcare, while also looking towards future directions in the field.
Analytics in Healthcare: An Introduction (HIMSS Book Series)
by Raymond A. GensingerThe editors of the HIMSS Books' best-seller Health: From Smartphones to Smart Systems have returned to deliver an expansive survey of the initiatives, innovators, and technologies driving the patient-centered mobile healthcare revolution. mHealth Innovation: Best Practices from the Mobile Frontier explores the promise of mHealth as a balance between emerging technologies and process innovations leading to improved outcomes-with the ultimate aim of creating a patient-centered and consumer-driven healthcare ecosystem. Examining the rapidly changing mobile healthcare environment from myriad perspectives, the book includes a comprehensive survey of the current-state ecosystem-app development, interoperability, security, standards, organizational and governmental policy, innovation, next-generation solutions, and mBusiness-and 20 results-driven, world-spanning case studies covering behavior change, patient engagement, patient-provider decision making, mobile gaming, mobile prescription therapy, home monitoring, mobile-to-mobile online delivery, access to care, app certification and quality evaluations, mixed media campaigns, and much more.
Analytics in Smart Tourism Design: Concepts and Methods (Tourism on the Verge)
by Daniel R. Fesenmaier Zheng XiangThis book presents cutting edge research on the development of analytics in travel and tourism. It introduces new conceptual frameworks and measurement tools, as well as applications and case studies for destination marketing and management. It is divided into five parts: Part one on travel demand analytics focuses on conceptualizing and implementing travel demand modeling using big data. It illustrates new ways to identify, generate and utilize large quantities of data in tourism demand forecasting and modeling. Part two focuses on analytics in travel and everyday life, presenting recent developments in wearable computers and physiological measurement devices, and the implications for our understanding of on-the-go travelers and tourism design. Part three embraces tourism geoanalytics, correlating social media and geo-based data with tourism statistics. Part four discusses web-based and social media analytics and presents the latest developments in utilizing user-generated content on the Internet to understand a number of managerial problems. The final part is a collection of case studies using web-based and social media analytics, with examples from the Sochi Olympics on Twitter, leveraging online reviews in the hotel industry, and evaluating destination communications and market intelligence with online hotel reviews. The chapters in this section collectively describe a range of different approaches to understanding market dynamics in tourism and hospitality.
Analytics in a Big Data World
by Bart BaesensThe guide to targeting and leveraging business opportunities using big data & analyticsBy leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments.The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and governmentContains an overview of the visionary ideas and current developments on the strategic use of analytics for businessCovers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysisFor organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.
Analytics the Right Way: A Business Leader's Guide to Putting Data to Productive Use
by Tim Wilson Joe SutherlandCLEAR AND CONCISE TECHNIQUES FOR USING ANALYTICS TO DELIVER BUSINESS IMPACT AT ANY ORGANIZATION Organizations have more data at their fingertips than ever, and their ability to put that data to productive use should be a key source of sustainable competitive advantage. Yet, business leaders looking to tap into a steady and manageable stream of “actionable insights” often, instead, get blasted with a deluge of dashboards, chart-filled slide decks, and opaque machine learning jargon that leaves them asking, “So what?” Analytics the Right Way is a guide for these leaders. It provides a clear and practical approach to putting analytics to productive use with a three-part framework that brings together the realities of the modern business environment with the deep truths underpinning statistics, computer science, machine learning, and artificial intelligence. The result: a pragmatic and actionable guide for delivering clarity, order, and business impact to an organization’s use of data and analytics. The book uses a combination of real-world examples from the authors’ direct experiences—working inside organizations, as external consultants, and as educators—mixed with vivid hypotheticals and illustrations—little green aliens, petty criminals with an affinity for ice cream, skydiving without parachutes, and more—to empower the reader to put foundational analytical and statistical concepts to effective use in a business context.
Analytics und Artificial Intelligence: Datenprojekte mehrwertorientiert, agil und nachhaltig planen und umsetzen
by Matthias Böck David Berger Ramona GreinerDie Autoren zeigen in diesem Buch, wie man für eigene Data-Science-Projekte mit Data Analytics und AI einen echten (Mehr-)Wert schafft. Sie entwickeln einen Leitfaden, mit dem Sie Ihre Datenanalyse systematisch, agil und nutzer:innenzentriert aufbauen und betreiben können. Zunächst machen die Autoren klar, wie wichtig es ist zu Beginn Ihrer Analytics-Projekte die für Ihr Geschäftsmodell richtigen und wertstiftenden Fragen zu stellen. Im Anschluss erläutern sie, wie Sie Technologien und Daten so einsetzen, dass sie einen echten Mehrwert erzeugen können. Schließlich zeigen sie, wie Sie die Projekte effektiv, effizient und gewinnbringend umsetzen können. Das Fundament dafür bilden agile Methoden und Design Thinking, die die Autoren für alltägliche Analytics- und Data-Science-Projekte überführt und adaptiert haben.Mit zahlreichen Beispielen und Erfahrungen aus Daten-, Web- und Digital-Analytics-Projekten sowie zwei realen Beispielen, wie man von der Idee und dem Auftrag zum Prototypen kommt. Aus dem Inhalt Agile Basics: Agile Prinzipien und ErfolgsfaktorenVom Design Thinking zum Data Thinking – wie Design Thinking Datenprojekte besser machtArtificial Intelligence – wie Künstliche Intelligenz mehrwertorientiert in Data Analytics eingesetzt werden kannEthische, rechtliche und ökologische Implikationen – wie Data Analytics und AI doch kein Schreckgespenst werdenDer Data Value Loop - Datenmehrwert agil und nutzer:innenzentriertAnalytics in der Praxis – von der Konzeption über Tracking und Reporting bis zum Arbeitsmeeting im AlltagAI in der Praxis - Data Science und Agile, geht das überhaupt zusammen? Zwei exemplarische ProjektdurchführungenGlossar
Analytics, Innovation, and Excellence-Driven Enterprise Sustainability (Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth)
by Elias G. Carayannis Stavros SindakisThis book offers a unique view of how innovation and competitiveness improve when organizations establish alliances with partners who have strong capabilities and broad social capital, allowing them to create value and growth as well as technological knowledge and legitimacy through new knowledge resources. Organizational intelligence integrates the technology variable into production and business systems, establishing a basis to advance decision-making processes. When strategically integrated, these factors have the power to promote enterprise resilience, robustness, and sustainability. This book provides a unique perspective on how knowledge, information, and data analytics create opportunities and challenges for sustainable enterprise excellence. It also shows how the value of digital technology at both personal and industrial levels leads to new opportunities for creating experiences, processes, and organizational forms that fundamentally reshape organizations.
Analytics, Machine Learning, and Artificial Intelligence: Second Analytics Global Conference, AGC 2024, Kolkata, India, March 6–7, 2024, Revised Selected Papers (Communications in Computer and Information Science #2224)
by Chandan Mazumdar Suparna Dhar Sanjay Goswami Indranil Bose Rameshwar Dubey Dinesh Kumar Unni KrishnanThis book constitutes the refereed proceedings of the Second Analytics Global Conference on Analytics, Machine Learning, and Artificial Intelligence, AGC 2024, held in Kolkata, India, during March 6-7, 2024. The 15 full papers and 3 short papers presented in these proceedings were carefully reviewed and selected from 60 submissions. The papers are organized in these topical sections: applications of analytics in business; analytics methods, tools & techniques.
Analytics, Policy, and Governance
by Benjamin Ginsberg Jennifer Bachner Kathryn Wagner HillThe first available textbook on the rapidly growing and increasingly important field of government analytics This first textbook on the increasingly important field of government analytics provides invaluable knowledge and training for students of government in the synthesis, interpretation, and communication of "big data," which is now an integral part of governance and policy making. Integrating all the major components of this rapidly growing field, this invaluable text explores the intricate relationship of data analytics to governance while providing innovative strategies for the retrieval and management of information.