Databaser

Take your organization's data maturity to the next level by operationalizing data governanceKey FeaturesDevelop the mindset and skills essential for successful data stewardshipApply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardshipFollow a step-by-step program to develop a data operating model and implement data stewardship effectivelyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement-it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency.From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You'll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You'll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you'll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship.By the end of this book, you'll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learnEnhance your job prospects by understanding the data stewardship field, roles, and responsibilitiesDiscover how to develop a data strategy and translate it into a functional data operating modelDevelop an effective and efficient data stewardship programGain practical experience of establishing a data stewardship initiativeImplement purposeful governance with measurable ROIPrioritize data use cases with the value and effort matrixWho this book is forThis book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started.Table of ContentsFrom Business Strategy to Data Strategy to Data StewardshipHow Data Stewardship can help your organizationGetting Started with the Data Stewardship ProgramDeveloping a Comprehensive Data Management StrategyPeople, Process, TechnologyEstablishing a Data Governance OrganizationData Steward Roles and ResponsibilitiesEffective Data StewardshipSupercharge Data Governance and Stewardship with GPTData Stewardship Best PracticesTheory versus Real LifeCase Studies

This book aims at meeting the challenge of getting along with today¿s unprecedented rate of innovation supported by disruptive digital technologies, which changed the perception of the productivity and effectiveness and opened a gateway to more than ever dynamic advances in solving the important societal challenges."Disruptive Information Technologies for a Smart Society" is the proceedings book of the 13th International conference for information society and technologies that brings together experts from various fields to discuss the latest advancements in software engineering, machine learning, digital water, and ICT for health and sport.The book and the conference serve as a platform for researchers of all career stages in technical sciences, especially Ph.D. students, practitioners, and industry experts in health care, energy, Fintech, AI, and other areas to share and learn on the cutting-edge technologies and stay at the forefront of these rapidly evolving fields.

This book focuses in detail on data science and data analysis and emphasizes the importance of data engineering and data management in the design of big data applications. The author uses patterns discovered in a collection of big data applications to provide design principles for hypothesis generation, integrating big data processing and management, machine learning and data mining techniques.The book proposes and explains innovative principles for interpreting hypotheses by integrating micro-explanations (those based on the explanation of analytical models and individual decisions within them) with macro-explanations (those based on applied processes and model generation). Practical case studies are used to demonstrate how hypothesis-generation and -interpretation technologies work. These are based on ¿social infrastructure¿ applications like in-bound tourism, disaster management, lunar and planetary exploration, and treatment of infectious diseases.The novel methods and technologies proposed in Hypothesis Generation and Interpretation are supported by the incorporation of historical perspectives on science and an emphasis on the origin and development of the ideas behind their design principles and patterns. Academic investigators and practitioners working on the further development and application of hypothesis generation and interpretation in big data computing, with backgrounds in data science and engineering, or the study of problem solving and scientific methods or who employ those ideas in fields like machine learning will find this book of considerable interest.

This book constitutes the revised selected papers of the 15th International Conference, MEDES 2023, Heraklion, Crete, Greece, May 5¿7, 2023The 29 full papers included in this volume were carefully reviewed and selected from 52 submissions. The papers focus on the following topics: Design Issues & BlockChain, Query Processing and Visualization, Learning Issues, Services and Systems, Business and Communication Technologies, Digital Twin and Security, Architecture Technologies, Time and Text Management.

¿This book offers a fresh perspective on smart tourism, introducing unique frameworks and insights with the potential to shape the industry's future. It explores the convergence of technology, tourism, and smart cities, emphasizing the use of smartphones, social media data, AI, blockchain, crowdsourcing, and crowdsensing to enhance tourism experiences.What sets it apart is its focus on practical solutions that require minimal infrastructure investments, making it accessible to a wide range of stakeholders. This book addresses knowledge gaps and proposes cutting-edge frameworks for smart tourism development. It is intended for researchers, academics, and professionals in tourism, technology, and urban planning.Key uses of this book include providing a comprehensive overview of the evolving smart tourism landscape and serving as a valuable resource for researchers and educators in this dynamic field.

This book is to provide a comprehensive reference for professionals in the field of data science and applications: artificial intelligence, big data, IoT, and blockchain. In summary, this book is expected to function as a helpful resource and manual, enabling readers to navigate the intricate domain of artificial intelligence, the Internet of things (IoT), and blockchain in smart environments. This book covers many topics related to integrating AI, IoT, blockchain, and smart environments. It begins by laying a solid foundation, introducing each technology's fundamental concepts and principles. Subsequent chapters explore applications and real-world use cases, demonstrating how AI, IoT, and blockchain can effectively address critical challenges within data science and applications.

This book presents various models and approaches, specific proposals, and recommendations on a whole range of areas for smart cities' development. This book proposes to look at the problem through a broad overview of all elements of a smart city as a single system: smart governance, smart buildings, smart transportation, smart tourism destinations, smart economy, and smart environment.The book is intended for both young and experienced scientists who want to get acquainted with the latest trends and data for using them in their research.

We are currently witnessing the emergence of a ¿third culture¿, driven by unprecedented developments that are changing the playing field. These include advances in AI, big data analysis and robotics. The traditional ¿two cultures¿ view (S. P. Snow) distinguishes between the sciences and the humanities. The wisdoms these two cultures offer are separated from each other, with little to no interaction or mutual comprehension. However, over the past two decades, and for the first time in human history, a new, third culture has appeared. This new culture, rooted in new technologies, not only pursues its own form of rationality but also supports advances in the original two cultures, deepening and expanding our individual and collective consciousness so that we can see more and do better. It will eventually give rise to new forms of consciousness based not on biochemical signals, but on copper wires and lithium chips. These new machine intelligences will change the world and force us to realise: we are no longer alone. The human species¿ position in the twenty-first century will be fundamentally redefined: not as a conductor leading the orchestra, but a single string player within it.

This book provides a comprehensive overview of how the course, content and outcome of policy making is affected by big data. It scrutinises the notion that big and open data makes policymaking a more rational process, in which policy makers are able to predict, assess and evaluate societal problems. It also examines how policy makers deal with big data, the problems and limitations they face, and how big data shapes policymaking on the ground. The book considers big data from various perspectives, not just the political, but also the technological, legal, institutional and ethical dimensions. The potential of big data use in the public sector is also assessed, as well as the risks and dangers this might pose. Through several extended case studies, it demonstrates the dynamics of big data and public policy. Offering a holistic approach to the study of big data, this book will appeal to students and scholars of public policy, public administration and data science, as well as those interested in governance and politics.

This book constitutes the 5th International Workshop, IoTBDH 2023, held in Birmingham, UK, during October 21¿25, 2023.The 7 full papers and 4 short papers included in this volume were carefully reviewed and selected from 33 submissions. They focus on the state-of-the-art research and applications in utilizing IoT and big data technology for healthcare by presenting efficient scientific and engineering solutions, addressing the needs and challenges for integration with new technologies, and providing visions for future research and development.

Dieses kompakte Werk zeigt praxisnah und fundiert die besondere Struktur von Verhandlungen auf. Gute Verhandlungen verlaufen spielerisch wie ein Theaterstück in drei Akten: die Begrüßung, der Austausch von Argumenten und im dritten Akt die Einigung. Werden Sie professioneller und vor allem besser im Führen von Verhandlungen. Stellen Sie sich auf die Spielweise Ihres Gegenübers in sorgfältiger Vorbereitung ein. Es geht dabei nicht nur um Sieg und Niederlage, sondern darum, das optimale Verhandlungsergebnis - idealerweise für beide Parteien - zu erzielen.

Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution.With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively.By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho this book is forThis book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.Table of ContentsExploring Data for Machine LearningLabeling Data for ClassificationLabeling Data for RegressionExploring Image DataLabeling Image Data Using RulesLabeling Image Data Using Data AugmentationLabeling Text DataExploring Video DataLabeling Video DataExploring Audio DataLabeling Audio DataHands-On Exploring Data Labeling Tools

Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey Features:- Learn practical data science combined with data theory to gain maximum insights from data- Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models- Explore actionable case studies to put your new skills to use immediately- Purchase of the print or Kindle book includes a free PDF eBookBook Description:Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What You Will Learn:- Master the fundamentals steps of data science through practical examples- Bridge the gap between math and programming using advanced statistics and ML- Harness probability, calculus, and models for effective data control- Explore transformative modern ML with large language models- Evaluate ML success with impactful metrics and MLOps- Create compelling visuals that convey actionable insights- Quantify and mitigate biases in data and ML modelsWho this book is for:If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you'll find this book useful. Familiarity with Python programming will further enhance your learning experience.Table of Contents- Data Science Terminology- Types of Data- The Five Steps of Data Science- Basic Mathematics- Impossible or Improbable - A Gentle Introduction to Probability- Advanced Probability- What are the Chances? An Introduction to Statistics- Advanced Statistics- Communicating Data- How to Tell if Your Toaster is Learning - Machine Learning Essentials- Predictions Don't Grow on Trees, or Do They?- Introduction to Transfer Learning and Pre-trained Models- Mitigating Algorithmic Bias and Tackling Model and Data Drift- AI Governance- Navigating Real-World Data Science Case Studies in Action

Level up your career by learning best practices for managing the data quality and integrity of your financial dataKey Features:Accelerate data integrity management using artificial intelligence-powered solutionsLearn how business intelligence tools, ledger databases, and database locks solve data integrity issuesFind out how to detect fraudulent transactions affecting financial report integrityBook Description:Data integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations.The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases.After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.What You Will Learn:Develop a customized financial data quality scorecardUtilize business intelligence tools to detect, manage, and resolve data integrity issuesFind out how to use managed cloud-based ledger databases for financial data integrityApply database locking techniques to prevent transaction integrity issues involving finance dataDiscover the methods to detect fraudulent transactions affecting financial report integrityUse artificial intelligence-powered solutions to resolve various data integrity issues and challengesWho this book is for:This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it'll help you grasp the more advanced topics covered in this book.

Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate dataKey FeaturesTransform data into a clean and trusted source of information for your organization using ScalaBuild streaming and batch-processing pipelines with step-by-step explanationsImplement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD)Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You'll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You'll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learnSet up your development environment to build pipelines in ScalaGet to grips with polymorphic functions, type parameterization, and Scala implicitsUse Spark DataFrames, Datasets, and Spark SQL with ScalaRead and write data to object storesProfile and clean your data using DeequPerformance tune your data pipelines using ScalaWho this book is forThis book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies. Table of ContentsScala Essentials for Data EngineersEnvironment SetupAn Introduction to Apache Spark and Its APIs - DataFrame, Dataset, and Spark SQLWorking with DatabasesObject Stores and Data LakesUnderstanding Data TransformationData Profiling and Data QualityTest-Driven Development, Code Health, and MaintainabilityCI/CD with GitHubData Pipeline OrchestrationPerformance TuningBuilding Batch Pipelines Using Spark and ScalaBuilding Streaming Pipelines Using Spark and Scala

We live and work in a world of change. Helping individuals and teams prepare for, respond to, and learn from change are critical for thriving. Managers and leaders at all levels play a vital role in developing talent, increasing performance, and supporting transitions and transformations. This book is about effectively coaching others in your role as a manager-coach. A manager-coach is a person who uses coaching-related knowledge, approaches, and skills to coach team members in the organization who report to them or who have sought their coaching. In 16 chapters, leaders at all levels, human resource professionals, and graduate students will find research-based, practical approaches to developing talent, improving performance, and supporting transformation. Topics include the change coaching process, theoretical foundtions of coaching, use of self in managerial coaching, six coaching skills, how to coach across differences, specialty coaching (peer, team, and executive), ethical considerations for coaching, and continuous development for manager-coaches. Provides models, frameworks and tools that can be used to coach team members.

Öffentliche Interventionen im Sinne des Künstlers Christian Hasucha sind im besten Sinne des Wortes irritierende Eingriffe in den öffentlichen Raum. Es sind "Deplatzierungen des Gewöhnlichen", die nicht nur unsere Sehgewohnheiten aufbrechen. Auch Begriffe wie die von Zeit, Raum und Habitat werden von ihm "verrückt" und zum Fließen gebracht. Die reich bebilderte Dokumentation bietet Denkanregungen allererster Güte!

Unveiling insights, unleashing potential: Navigating the depths of data warehousing and mining for a data-driven tomorrow¿¿DESCRIPTION Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets.The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. By the end of this book, you will be equipped with the skills and knowledge to confidently translate business questions into actionable strategies, extracting valuable insights for informed decisions.WHAT YOU WILL LEARN¿ Designing and building efficient data warehouses¿ Handling diverse data types for comprehensive insights¿ Mastering various data mining techniques¿ Translating business questions into mining strategies¿ Techniques for pattern discovery and knowledge extractionWHO THIS BOOK IS FORFrom aspiring data analysts, data professionals, IT managers, to business intelligence practitioners, this book caters to a diverse audience.

This book discusses and addresses anomaly detection in the context of artificial intelligence and machine learning advancements. Building on the existing literature, this thorough and timely work is an invaluable resource. It highlights various problems, offers workable solutions to those problems, and allows academic and professional researchers and practitioners to engage in new technologies linked to anomaly detection. This book demystifies the challenges and presents solutions for detecting and understanding network anomalies. Whether you are a seasoned network professional or an enthusiast keen on cyber security, this volume promises insights that will fortify our connected futures. Join us in navigating the complexities of modern networks and championing a safer, more transparent digital era.

A Guardian book to look out for in 2024An insightful exploration of the nature of inequality by the internationally bestselling author of Capital in the Twenty-First Century.In his newest work, Thomas Piketty explores how social inequality manifests itself very differently depending on the society and epoch in which it arises. History and culture play a central role, inequality being strongly linked to various socio-economic, political, civilisational, and religious developments. So it is culture in the broadest sense that makes it possible to explain the diversity, extent, and structure of the social inequality that we observe every day.Piketty briefly and concisely presents a lively synthesis of his work, taking up such diverse topics as education, inheritance, taxes, and the climate crisis, and provides exciting food for thought for a highly topical debate: Does natural inequality exist?

Dieses Buch zeigt auf, welche Herausforderungen die hochkomplexe ESG-Regulierung an die Kommunikation von Unternehmen stellt ¿ und wie diese gemeistert werden können. Mit Beginn des Jahres 2024 werden die Anforderungen an die Umwelt-, Sozial- und Governance-Berichterstattung deutlich erhöht. Die Corporate Sustainability Due Diligence Directive (CSDDD oder CS3D) der EU verlangt von mehr Unternehmen aller Größen detaillierte Erklärungen zur Vereinbarkeit des Geschäftsmodells mit ESG-Standards.Diese neue Ära der Unternehmensberichterstattung muss von Experten aus der Unternehmenskommunikation, der Kapitalmarktkommunikation und dem Rechnungswesen gemeinsam angegangen werden, um die Überzeugungskraft, die Reichweite und das krisenfeste Narrativ der ESG-Berichterstattung zu stärken. Die Autoren erläutern, welche Aspekte und Perspektiven hierbei besonders entscheidend sind und wie Unternehmensverantwortliche ihr Reden und Handeln immer wieder neu gewichten und austarieren können.

Dieses essential gibt einen aktuellen Überblick über die Hintergründe der im Juli 2023 erstmals veröffentlichten China-Strategie der Bundesregierung und über die südostasiatischen Länder, die als Standortergänzung für eine ¿China+1¿-Strategie in Frage kommen. Spätestens seit 2020 wird die starke Abhängigkeit der deutschen Wirtschaft zunehmend zum Problem. Es empfiehlt sich kein ¿Decoupling¿, sondern ein ¿De-Risking¿: der Auf- und Ausbau von Beziehungen zu weiteren Ländern in der Region, um zu diversifizieren und damit Risiken zu mindern. Dieses Buch will aufzeigen und erklären: Was sind die Gründe der neuen China-Strategie und welche Länder kommen als Standortergänzungen in Frage?Ein Buch für alle, die in China unternehmerisch tätig sind und über strategische Standortergänzungen und -alternativen nachdenken.

Dieses essential zeigt, welche Bedeutung digitale Barrierefreiheit für die Unternehmenskommunikation hat und wie diese die Ansprache Verbrauchern verändern wird. Das ab dem 28. Juni 2025 geltende Barrierefreiheitsstärkungsgesetz (BFSG) wird zum Beschleuniger dieser Entwicklung, mit der sich künftig viele Unternehmen auseinandersetzen müssen. Die renommierte Kommunikationswissenschaftlerin Gabriele Horcher zeigt auf, wie die neuen gesetzlichen Anforderungen effizient umgesetzt werden können und wie künstliche Intelligenz dabei helfen kann. Darüber hinaus stellt sie Strategien für Berater und Dienstleister vor, die ihre Kunden optimal unterstützen wollen.Ein auf den Punkt geschriebenes Buch für Entscheidungsträger, Kommunikations-, Compliance- und E-Commerce-Verantwortliche, die alles Wichtige über die spezifischen Herausforderungen der digitalen Barrierefreiheit erfahren und von den neuen Chancen profitieren wollen.

Unter Anwendung der Frame-Theorie wird in diesem Buch die Rolle von nicht-verbalisiertem Vorwissen in der interkulturellen Markenkommunikation untersucht. Eine starke Marke ist ein sowohl komplexes als auch fiktionales Konstrukt, das sich neben verschiedenen Oberflächenmerkmalen auch aus Assoziationen, Empfindungen und Hintergrundwissen zur Marke zusammensetzt. Mit einer kognitiv-linguistischen Vorgehensweise wird anhand von vier Fallbeispielen analysiert, welche unterschiedlichen Kommunikationsstrategien etablierte Unternehmen zur interkulturellen Positionierung von Marken nutzen und inwieweit diese sich in der Wahrnehmung der Rezipienten widerspiegeln. Dabei wird von einem holistischen Denkansatz ausgegangen, bei dem Sprachwissen und Weltwissen eine untrennbare Einheit darstellen und der Fokus liegt darauf, zu verstehen, wie abstraktes und künstlich erzeugtes Wissen um eine Marke kommunikativ etabliert werden kann und welche Rolle die landeskulturelle Bedingtheit hierbei spielt.

Das Buch befasst sich mit einem expliziten Bedarf, der in einer großen Anzahl von Interviews mit Führungskräften auf Vorstandsebene geäußert wurde: der Bewältigung bedeutender Transformationen im privaten und öffentlichen Sektor. Das Buch beschreibt, welche Arten von Transformationen berücksichtigt werden müssen, warum Transformationen in unserer Zeit entscheidend sind, welche Auslöser sie haben und wie sie am besten aus theoretischer und praktischer Sicht - technisch und mit allen mit Menschen verbundenen Soft Facts - gemanagt werden können. Das Buch, das zahlreiche Anwendungsfälle enthält, wurde von einer internationalen Gemeinschaft von Praktikern, Experten und Akademikern aus verschiedenen geografischen Regionen, Ländern, öffentlichen und privaten Organisationen, Branchen und Kulturen verfasst. Dies garantiert die Breite und Vielfalt der entwickelten Erkenntnisse und den Wert der präsentierten Anwendungsfälle.