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 Anonymous

Nutzung von Business Intelligence zur Identifikation und Entwicklung digitaler Geschäftsmodelle

In Digitizing the Supply Chain, Nick Seiersen explores the transformative potential of information technologies in modern supply chain management. This book examines the foundational technologies, tools, and challenges of the digital transformation of supply chains, offering insights into future trends and opportunities.

The author reviews key technologies such as AI, IoT, blockchain, and Big Data, illustrating their application through real-world case studies. Readers will gain a practical understanding of how to leverage these technologies to optimize logistics, enhance cybersecurity, and manage risk.

This book serves as an essential resource for executives and professionals seeking to navigate the complexities of deploying new technologies to build resilient, future-proof supply chains.

How AI and 8 Technology Building Blocks Can Supercharge Your Supply Chain

Digitizing the Supply Chain

Management for Professionals

This book offers a comprehensive guide for sports organizations to effectively manage and leverage data for optimal outcomes. It bridges theory and practice, providing frameworks for sports data strategy and analytics, and emphasizing data governance, quality, and security. Through case studies and examples, it demonstrates data-driven improvements in player recruitment, game strategy, fan engagement, and revenue generation. Exploring emerging trends like AI and machine learning, it empowers readers to make informed decisions and meet the growing demand for data-driven decision-making in sports. The book aims to unlock the potential of sports data for sustained success in the industry.

This book offers a comprehensive guide for sports organizations to effectively manage and leverage data for optimal outcomes. It bridges theory and practice, providing frameworks for sports data strategy and analytics, and emphasizing data governance, quality, and security. Through case studies and examples, it demonstrates data-driven improvements in player recruitment, game strategy, fan engagement, and revenue generation. Exploring emerging trends like AI and machine learning, it empowers readers to make informed decisions and meet the growing demand for data-driven decision-making in sports. The book aims to unlock the potential of sports data for sustained success in the industry.

A Practical Guide for Sports Organizations

Data Excellence in Sports

Management on the Cutting Edge

In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it. “Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. A forward-looking, practical book and a must-read for anyone in the information economy.” —Scott Galloway, NYU Stern Professor of Marketing; bestselling author of The Four “An antidote to today’s relentless AI hype—why some AI initiatives thrive while others fail and what it takes for companies and people to succeed.” —Charles Duhigg, author of bestsellers The Power of Habit and Smarter Faster Better The greatest tool is the hardest to use. Machine learning is the world's most important general-purpose technology—but it's notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What's missing? A specialized business practice suitable for wide adoption. In The AI Playbook, bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects, aka predictive AI projects, from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals, and it establishes a sorely needed strategic framework for data professionals. Beyond detailing the practice, this book also upskills business professionals—painlessly. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need to lead or participate in machine learning projects, end to end. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative—getting them right paves the way for machine learning's value-driven deployment. A note from the author: The buzzword AI can mean many things, but this book is about the most vital use cases of machine learning,&nbsp;those designed to improve large-scale business operations—aka predictive AI or predictive analytics.

In his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it.

Mastering the Rare Art of Machine Learning Deployment

The AI Playbook, with a new preface by the author

Mastering Business Analytics

Data Analytics for Strategic Decisions explores how businesses can harness the power of data, technology, and analytics to make strategic decisions that drive growth and innovation. It provides a comprehensive guide to integrating business strategy with data-driven methodologies, offering practical tools and insights for modern organizations.Readers will gain a deep understanding of how to transform data into actionable insights, improving decision-making across marketing, finance, operations, and strategy. The book combines theoretical foundations with real-world applications, including case studies, datasets, and practical exercises. It also addresses emerging topics like AI, predictive analytics, and data governance, ensuring readers are equipped with cutting-edge knowledge and skills.This book is designed for undergraduate and master's students in Business Administration, Business Analytics, and Digital Transformation, as well as professionals seeking to enhance their data-driven decision-making capabilities. It is ideal for courses in Business Analytics, Data Analytics for Business, Business Intelligence, and Digital Transformation.The book includes supplementary materials such as datasets for practical exercises, case studies, and discussion questions, making it suitable for both classroom and online learning environments.

Fundamentals of Business Technology and AI Integration

Data Analytics for Strategic Decisions

Moderne, effiziente Revisionstechniken stützen sich auf automatisierte Analysen betrieblicher Daten. Mit diesem Buch erlernen Sie schrittweise alle Grundlagen der IDEA-Programmierung. Zahlreiche Übungsaufgaben und Beispiele begleiten Sie von der Entwicklung erster Programme bis zur Gestaltung komplexer Analyse-Workflows. 
Beginnende und fortgeschrittene IDEA-Anwender werden gleichermaßen angesprochen. 
Standardaufgaben der Skriptprogrammierung sowie automatisierte Aufzeichnungsverfahren ermöglichen ohne besondere Entwicklungskenntnisse die Erstellung automatischer Analyseverfahren für den Prüfungsalltag.
Ambitionierte Prüfer erhalten eine methodische Einführung in die Sprachelemente und alle wichtigen Programmiertechniken von IDEA-Skript. Sie werden strukturiert von der ersten Prüfungsidee bis zu komplexen Datenimport- und Datenauswertungstechniken geleitet. Technisch versierte IDEA-Anwender können sich mit Entwicklungen auseinandersetzen, die das Anwendungsspektrum der Prüfsoftware wesentlich erweitern. Hierzu gehört unter anderem die innovative Verknüpfung mit der Office-Software. Darüber hinaus werden interessante Auswertungsverfahren für typische Prüffelder der in- und externen Revision vorgestellt.

Das Buch enthält zahlreiche direkt einsetzbare Funktionen und Techniken, die Sie unmittelbar in eigene Skriptprogramme einbinden können. Sie werden Ihnen über eine begleitende Website digital zur Verfügung gestellt und reduzieren den erforderlichen Entwicklungsaufwand erheblich

Moderne, effiziente Revisionstechniken stützen sich auf automatisierte Analysen betrieblicher Daten. Mit diesem Buch erlernen Sie schrittweise alle Grundlagen der IDEA-Programmierung. Zahlreiche Übungsaufgaben und Beispiele begleiten Sie von der Entwicklung erster Programme bis zur Gestaltung komplexer Analyse-Workflows. 
Beginnende und fortgeschrittene IDEA-Anwender werden gleichermaßen angesprochen. 
Standardaufgaben der Skriptprogrammierung sowie automatisierte Aufzeichnungsverfahren ermöglichen ohne besondere Entwicklungskenntnisse die Erstellung automatischer Analyseverfahren für den Prüfungsalltag.
Ambitionierte Prüfer erhalten eine methodische Einführung in die Sprachelemente und alle wichtigen Programmiertechniken von IDEA-Skript. Sie werden strukturiert von der ersten Prüfungsidee bis zu komplexen Datenimport- und Datenauswertungstechniken geleitet. Technisch versierte IDEA-Anwender können sich mit Entwicklungen auseinandersetzen, die das Anwendungsspektrum der Prüfsoftware wesentlich erweitern. Hierzu gehört unter anderem die innovative Verknüpfung mit der Office-Software. Darüber hinaus werden interessante Auswertungsverfahren für typische Prüffelder der in- und externen Revision vorgestellt.

Das Buch enthält zahlreiche direkt einsetzbare Funktionen und Techniken, die Sie unmittelbar in eigene Skriptprogramme einbinden können. Sie werden Ihnen über eine begleitende Website digital zur Verfügung gestellt und reduzieren den erforderlichen Entwicklungsaufwand erheblich.

Continuous Auditing mit IDEA-Skript

Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free, web-based data science product for educational and non-commercial purposes. The power of SAS Studio lies in its visual, point-and-click user interface, which generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks.
The book includes a case study analyzing the data required to predict the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples, including analyses of stock, oil, and gold prices, crime, marketing, and healthcare. You will see data science in action and how easily it can be performed using complicated tasks and visualizations in SAS Studio.
You will learn, step by step, how to perform visualizations, including creating maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks.
&nbsp;
What You Will Learn&nbsp;
<ul>
Become familiar with the SAS Studio IDE.
<li>How to create essential visualizations.</li>
<li>Know the fundamental statistical analysis required in most data science and analytics reports.</li>
<li>Clean the most common dataset problems</li>
<li>Learn linear and logistic regression for data prediction and analysis.</li>
<li>Write programs in SAS.</li>
<li>How to analyze data and get insights from it for decision-making.</li>
Learn character, numeric, date, time, and datetime functions and typecasting.
</ul>

From Clicks to Code

Learn Data Science Using SAS Studio

Chapman & Hall/CRC Texts in Statistical Science

From Alan Turing breaking the Enigma code to ChatGPT reshaping how we work and create, artificial intelligence has always been powered by a single unifying principle: learning from data under uncertainty. This book reveals the mathematical machinery behind modern AI, weaving together Bayesian probability, statistical learning, and deep neural networks into a coherent intellectual framework. Whether you seek to understand why large language models hallucinate, how recommendation systems predict your preferences, or what makes reinforcement learning agents master complex games, this book equips you with both the theoretical foundations and practical intuitions that define the modern AI playbook.Key Features:<ul> <li>Builds Bayesian reasoning from first principles through historical examples like submarine search and WWII code-breaking</li> <li>Bridges classical statistics and deep learning, connecting linear regression to transformers</li> <li>Covers the complete AI stack: probability, decision theory, Gaussian processes, neural networks, CNNs, NLP, and LLMs</li> <li>Emphasizes uncertainty quantification-building systems that know what they don't know</li> <li>Practical applications across finance, healthcare, operations, and autonomous systems</li> <li>Emerging topics: AI agents, RLHF, and retrieval-augmented generation</li> </ul>Written for graduate students, data scientists, and quantitatively-minded practitioners, this book assumes comfort with calculus and basic probability while building sophisticated intuitions progressively. Business analysts will appreciate the decision-theoretic framing; engineers will value the architectural insights; researchers will find rigorous foundations for further study. Whether used as a course textbook, professional reference, or intellectual companion for understanding the AI revolution transforming every industry, this book delivers the rare combination of mathematical depth and accessible exposition that makes complex ideas genuinely understandable.

Foundations of Data Science

Bayes, AI, and Deep Learning

Asset Analytics

This book explores the transformative potential of business analytics in the modern business landscape, offering a practical guide to leveraging data for strategic decision-making. With companies generating vast amounts of data, there is a growing need for professionals who can interpret and analyze this information to derive actionable insights. This book equips readers with essential tools and techniques to become proficient in business analytics, emphasizing the application of data to solve real-world business problems and drive profitability. Encompassing various facets of a business, including supply chain analytics, HR analytics, influencer networks, Impact of machine learning and AR/VR, sensitivity analysis, data story telling etc. and even empowering customers with the help of data driven approach, this book tries to add value in managerial decision making. &nbsp;Focusing on the latest trends and advancements, this book demonstrates how analytics can provide a competitive edge by converting large datasets into meaningful insights. Through real-world examples and case studies, it shows how businesses can enhance market share and deliver greater returns to shareholders. Designed for business professionals, analysts, and decision-makers, this book is an essential resource for anyone looking to improve their company’s performance or advance their career with cutting-edge analytical skills.

Sharpening beyond Data

Business Analytics and Managerial Decision Making

Business Analytics for Supply Chains and Operations provides a concise and practical guide to solving management science problems analytically, with a special focus on supply chain challenges. It equips readers with essential tools and techniques to enhance business efficiency and decision-making.

A Management Science Perspective

Business Analytics for Supply Chains and Operations

In Networked Business, Paul Grefen returns with the third edition of his tried and tested BOAT framework for advanced e-business, covering the spectrum from business models via organization structures and digital system architectures to information technology.Today's successful organizations are complex; they are part of dynamic business networks built on digital channels, going far beyond traditional e-commerce. This book provides invaluable insights of modern digitally enabled networked business, going much further than the usual analysis of traditional e-business texts. It is heavily expanded and updated with the latest developments of digitally connected business, including new topics like value-oriented business, business optimization loops, (generative) artificial intelligence, and technology hype cycles.This essential text provides a compact roadmap to networked business for engineering, information systems or business students as well as professionals in the field.

Charting Digitally Enabled Business Collaboration

Networked Business

Contributions to Management Science

This book leverages statistical analysis, data mining, and machine learning techniques to address managerial and socioeconomic problems. With the advent of modern technologies, massive amount of data, especially big data, proliferate from business transactions and users. Consequently, there is an ever-increasing demand for analyzing the data and gaining valuable insights. This book comprises 15 chapters: the first ten chapters cover methods from Statistics and Econometrics, while the next five chapters delve into selected Machine Learning techniques. By bringing together the expertise of eminent researchers from reputed universities worldwide, this volume provides a cohesive guide to understanding and applying data science methodologies to real-world problems.

The book assumes basic knowledge of probability and statistics. Each chapter presents a blend of theoretical insights and practical case studies, ensuring that readers not only learn the techniques but also see their relevance and implementation in real-world scenarios. The chapters not only cover the theoretical underpinnings in a student-friendly language but also provide step-by-step guides for implementation using various software tools such as R, Python, Matlab, and SPSS. This is to instill confidence in the reader to apply such techniques to real-life problems. The book is designed for a broad spectrum of readership - empirical economists, business analysts, and post-graduate students aiming to learn and practice data science. Moreover, the book is designed in such a way that it can be used as a practical reference book for one semester-long Data Science course.

This book leverages statistical analysis, data mining, and machine learning techniques to address managerial and socioeconomic problems. With the advent of modern technologies, massive amount of data, especially big data, proliferate from business transactions and users. Consequently, there is an ever-increasing demand for analyzing the data and gaining valuable insights. This book comprises 15 chapters: the first ten chapters cover methods from Statistics and Econometrics, while the next five chapters delve into selected Machine Learning techniques. By bringing together the expertise of eminent researchers from reputed universities worldwide, this volume provides a cohesive guide to understanding and applying data science methodologies to real-world problems.The book assumes basic knowledge of probability and statistics. Each chapter presents a blend of theoretical insights and practical case studies, ensuring that readers not only learn the techniques but also see their relevance and implementation in real-world scenarios. The chapters not only cover the theoretical underpinnings in a student-friendly language but also provide step-by-step guides for implementation using various software tools such as R, Python, Matlab, and SPSS. This is to instill confidence in the reader to apply such techniques to real-life problems. The book is designed for a broad spectrum of readership - empirical economists, business analysts, and post-graduate students aiming to learn and practice data science. Moreover, the book is designed in such a way that it can be used as a practical reference book for one semester-long Data Science course.

Concepts, Techniques, and Applications

Data Science for Modeling Managerial and Socioeconomic Problems

Automating Government: How AI Will Revolutionise Public Services is a timely and authoritative guide for political leaders, senior managers, and public officials confronting the challenges and opportunities of artificial intelligence. Through dozens of global case studies and clear analysis, it explores how AI can transform public services, such as predictive healthcare, automated social services, and real-time transport systems, leading to improved outcomes and lower costs.While the potential is considerable, the implications are far from straightforward. The book addresses the following critical questions: What will happen to jobs? Can we trust machines to make decisions about people's lives? Will AI reduce inequality or entrench it further?Rich in practical insight and grounded in real-world examples, Automating Government sets out a roadmap for responsible, effective AI adoption in the public sector. It challenges readers to see AI not simply as a tool for efficiency but as a means to build a more capable, inclusive and accountable state fit for the demands of the twenty-first century.

How AI Will Revolutionise Public Services

Automating Government

Nutzung von Business Intelligence zur Identifikation und Entwicklung digitaler Geschäftsmodelle

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