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Anonymous: Geschäftsmodellentwicklung
mit Business Intelligence. Digitale Tra

Geschäftsmodellentwicklung mit Business Intelligence. Digitale Transformation am Beispiel der Urbery GmbH

Masterarbeit aus dem Jahr 2026 im Fachbereich Ingenieurwissenschaften - Wirtschaftsingenieurwesen, Note: 2,3, AKAD University, ehem. AKAD Fachhochschule Stuttgart, Sprache: Deutsch, Abstract: Digitale Plattformen zählen zu den wichtigsten Geschäftsmodellen der digitalen Transformation. Dennoch stehen Unternehmen, Start-ups und Organisationen häufig vor der Herausforderung, tragfähige Plattform-Geschäftsmodelle systematisch zu entwickeln, da klassische Methoden die Dynamik, Mehrseitigkeit und Komplexität digitaler Plattformen nur eingeschränkt berücksichtigen. 

Diese Masterarbeit entwickelt ein modulares Framework zur Geschäftsmodellentwicklung für digitale Plattformen, das wissenschaftliche Fundierung mit praktischer Anwendbarkeit verbindet. Auf Basis etablierter Ansätze aus Geschäftsmodellforschung, Systemtheorie und Plattformökonomie entsteht ein flexibler Baukasten, der Organisationen dabei unterstützt, digitale Plattformmodelle strukturiert zu konzipieren, iterativ weiterzuentwickeln und an unterschiedliche Kontexte anzupassen. 

Die empirische Validierung durch Experten aus Wissenschaft und Praxis unterstreicht die Relevanz des entwickelten Ansatzes. Die Arbeit richtet sich insbesondere an Innovationsmanager, Strategieverantwortliche, Gründer, Forschende sowie Unternehmen, die digitale Plattformstrategien entwickeln oder bestehende Geschäftsmodelle transformieren möchten.

Konzeption eines modularen Frameworks zur Geschäftsmodellentwicklung für digitale Plattformen

Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day.

Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh.

What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve.

What You Will Learn

Design Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.

Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.

Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.

Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.

Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.

Operate with observability and SRE practices using Snowflake usage views and SLOs.

Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity.




Who This Book Is For

Data engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics).

Design, build, and operate a production-grade analytics platform on Snowflake. This practical guide shows how Snowflake architecture shapes modeling, ingestion, and transformation choices; how to engineer ELT pipelines for structured and semi-structured data; and how to make performance, workload, security, and cost decisions that stand up in real projects. The approach is engineering-first and scenario-driven, turning concepts into repeatable, auditable solutions teams can use day to day.Beyond feature coverage, the emphasis is operations: CI/CD for SQL and Snowpark code, monitoring and observability, least-privilege governance with roles and policies, cost guardrails, secure sharing and collaboration, and business continuity with Time Travel, cloning, and replication. You will learn Snowflake-specific techniques for pruning, selective clustering, streaming and CDC, and dynamic refresh.What makes this book especially useful is its end-to-end operating playbook: opinionated patterns, checklists, and guardrails that connect architecture, modeling, ingestion and ELT, governance and security, performance and cost, and the everyday practices of releasing and recovering safely. It focuses on concrete decisions and the trade-offs behind them, helping teams avoid legacy anti-patterns while building a reliable, auditable platform that is ready to evolve.What You Will Learn<ul><li>Design Snowflake architectures that align storage, compute, security, and governance into a coherent, scalable platform.</li><li>Model, load, and transform structured and semi-structured data using streams, tasks, MERGE, and SCD2 patterns.</li><li>Tune performance and control cost with micro-partition pruning, selective clustering, warehouse sizing, and workload isolation.</li><li>Implement least-privilege RBAC, masking and row access policies, auditing, and tag-driven governance.</li><li>Build reliable ELT pipelines and release safely with CI/CD, testing, cloning, and SWAP-based promotion.</li><li>Operate with observability and SRE practices using Snowflake usage views and SLOs.</li><li>Share and collaborate securely with Secure Data Sharing and Marketplace, and plan replication and DR for continuity.</li></ul>Who This Book Is ForData engineers; data warehouse and solution architects; analytics engineers; BI developers; advanced data analysts; DBAs moving from on-prem to cloud (intermediate level with SQL and warehousing basics).&nbsp;

Architecture, Modeling, ELT Pipelines, and Operations

Snowflake Data Warehouse Engineering

Artificial Intelligence, Machine Learning, Data Analytics and Automation for Business Management

In providing an in-depth exploration of cutting-edge technologies and how they are used to support data-driven marketing strategies and empower organizations to make the right decisions, Predictive Analytics and Generative AI for Data-Driven Marketing Strategies includes real-world case studies and examples from diverse marketing domains. This book demonstrates how predictive analytics and generative AI have been successfully applied to solve marketing challenges and drive tangible results. This book showcases emerging trends in predictive analytics and generative AI for marketing, and their potential impact on the future of data-driven marketing. This book is meant for professionals and scholars to gather the skills and resources to use predictive analytics and generative AI effectively for marketing strategies.This book: ¿ Examines the different predictive analytics models and algorithms, such as regression analysis, decision trees, and neural networks, and demonstrates how they may be utilized to get insightful conclusions from marketing data.¿ Includes generative AI techniques, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), showcasing how these techniques can generate synthetic data for marketing insights and decision-making.¿ Highlights the importance of data-driven marketing choices and illustrates how generative AI and predictive analytics may be quite useful in this context.¿ Integrates the principles of data science with marketing concepts, offering a cohesive understanding of how predictive analytics and generative AI can power data-driven marketing decisions.¿ Presents the recent advances in predictive analytics and generative AI and discusses how they can affect the area of data-driven marketing.

Predictive Analytics and Generative AI for Data-Driven Marketing Strategies

Digital Sustainability: Navigating Entrepreneurship in the Information Age explores the intersection of technology and sustainability, offering a panoramic view of innovative strategies and solutions for building a more environmentally conscious and socially responsible future. From exploring the transformative potential of blockchain technology in sustainable supply chains to harnessing the power of Artificial Intelligence (AI) and machine learning for environmental monitoring and conservation, each chapter presents cutting-edge insights and practical applications. The book highlights the ethical implications of entrepreneurship and data privacy, focusing on the potential of AI and machine learning for sustainable resource utilization and decision-making processes. Delving into areas such as renewable energy integration, data privacy, cybersecurity, IoT entrepreneurship, smart cities, and beyond, this book equips entrepreneurs, policymakers, and researchers with the knowledge and tools needed to drive meaningful change in the digital era. With a rich tapestry of case studies, future perspectives, and actionable insights, this book offers a roadmap for entrepreneurs, engineers, business professionals, and those interested in technology and sustainability, focusing on redefining business models, fostering innovation, and creating a more connected, sustainable world.

Navigating Entrepreneurship in the Information Age

Digital Sustainability

Remember the financial crisis behind the Great Recession? A big part of what went wrong was poor risk data management. Regulators didn't see the systemic risk building up because they couldn't track all the contractual obligations between firms. We can't let that happen again. Banks today need to stay ahead of the curve with legal data management. Legal Data, Digital Assets and Smart Contracts for Capital Markets gives you exactly what you need to make that happen.Here's what you'll get with this book:<ul> <li>A clear picture of how current legal data practices</li> <li>The systems, structures, and strategies you need to manage risk effectively and make the most of your business opportunities</li> <li>A deep dive into regulations around risk aggregation, netting, collateral enforceability, and more</li> <li>Practical advice on legal data technology, systems, and how to handle migration</li> <li>If you're working in the capital market space, you really need to get a solid handle on managing it.</li> </ul>The text breaks down what legal data means for financial institutions and shows how banks can use it to run their business better and keep risk under control. This book is perfect for anyone in IT, project management, or data governance. It walks you through what's working now, what should be working, and how to get there. Let's be honest-a lot of banks are dealing with the same headaches when it comes to legal data management. This a clear roadmap for the processes, systems, and governance you need is going to make your life a lot easier.At the end of the day, the legal contracts between firms contain serious obligations that keep financial markets running. If you're not treating those terms as valuable data assets, you're leaving yourself exposed to unnecessary risk and missing out on business opportunities. Legal Data, Digital Assets and Smart Contracts for Capital Markets gives you the critical information you need plus actionable steps to put it all into practice.

Legal Data, Digital Assets and Smart Contracts for Capital Markets

The second edition of Advanced Data Science and Analytics with Python reflects the rapid transformation of artificial intelligence in recent years. While preserving its practical, modular structure, this edition significantly expands coverage of the techniques shaping modern AI practice.The deep learning chapter has been substantially broadened to include reinforcement learning and generative adversarial networks, alongside a fully developed exploration of transformer architectures. Generative AI now takes centre stage, with dedicated coverage of self-attention, BERT, GPT, large language model evaluation and API-based interaction. Emerging agentic systems are introduced as part of the evolving AI landscape. Natural language processing has been enhanced with word embeddings, contextual representations and vector search, while network analysis now includes graph representation learning and embedding techniques. The chapter on data product deployment has been strengthened with modern Core ML workflows and new coverage of on-device Foundation Models, bridging experimentation and production.Fully updated for the contemporary Python ecosystem, this edition equips practitioners with the tools and architectural understanding required to design, build and deploy intelligent systems in today's AI-driven world.Jesús Rogel-Salazar is a lead data scientist, founder of RogueLoop, working for companies such as Ortus Technologies, Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Imperial College Business School, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Advanced Data Science and Analytics with Python

Die digitale Transformation hat die Rahmenbedingungen im Marketing neu definiert. Während herkömmliche Werbekanäle und lineare Kommunikationsstrategien an Wirksamkeit verlieren, sind digitale Instrumente und Kanäle, datengestützte Prozesse und interaktive Formate immer relevanter.
Dieses Lehrbuch richtet sich an Studierende der Wirtschaftswissenschaften, für die Kenntnisse im Digitalen Marketing heute unverzichtbar sind. Schritt für Schritt führt es in die wichtigsten Grundlagen des Digitalen Marketings ein: Digitale Marktforschung, Suchmaschinenoptimierung, Content-Management-Systeme, App-Marketing, KI-Marketing, Retail Media, Social Media Marketing, Influencer Marketing und vieles mehr.
Eine klare Struktur mit zahlreichen Aufzählungen und Schaubildern sorgt dafür, dass schnell ein Verständnis für Digitales Marketing entwickelt wird. Viele realistische Beispiele aus unterschiedlichen Branchen ermöglichen den sofortigen Transfer der Marketing-Theorie in die Praxis. Zur Überprüfung des Wissensstands bietet das Lehrbuch einen eLearning-Kurs mit 300 Lernfragen und Lösungen.

Die digitale Transformation hat die Rahmenbedingungen im Marketing neu definiert. Während herkömmliche Werbekanäle und lineare Kommunikationsstrategien an Wirksamkeit verlieren, sind digitale Instrumente und Kanäle, datengestützte Prozesse und interaktive Formate immer relevanter.
Dieses Lehrbuch richtet sich an Studierende der Wirtschaftswissenschaften, für die Kenntnisse im Digitalen Marketing heute unverzichtbar sind. Schritt für Schritt führt es in die wichtigsten Grundlagen des Digitalen Marketings ein: Digitale Marktforschung, Suchmaschinenoptimierung, Content-Management-Systeme, App-Marketing, KI-Marketing, Retail Media, Social Media Marketing, Influencer Marketing und vieles mehr.
Eine klare Struktur mit zahlreichen Aufzählungen und Schaubildern sorgt dafür, dass schnell ein Verständnis für Digitales Marketing entwickelt wird. Viele realistische Beispiele aus unterschiedlichen Branchen ermöglichen den sofortigen Transfer der Marketing-Theorie in die Praxis. 


 utb+:
 Begleitend zum Buch steht ein E-Learning-Kurs mit 300 Lernfragen und Lösungen für die Prüfungsvorbereitung zur Verfügung. Erhältlich über utb.de.

Lehrbuch mit eLearning-Kurs

Digitales Marketing Schritt für Schritt

An Integrative Perspective and Readings

Value Creation Management

International Series in Operations Research & Management Science

This book focuses on the innovative use of multi-source heterogeneous data to address key issues in public transit systems. Readers will explore advanced methodologies for data mining, problem diagnosis, mechanism analysis, and stochastic optimization tailored to the public transit context.

This book stands out by integrating the latest in data processing techniques, pattern recognition, and mechanism analysis, providing a comprehensive framework for systematic diagnosis and optimization of transit systems. By examining both microscopic and macroscopic perspectives, it offers insights into passenger behavior, system reliability, and personalized transit solutions.

The unique approach of this book lies in its holistic perspective, emphasizing the study of patterns, problem diagnosis, mechanism analysis, and model construction through the use of multi-source heterogeneous public transit data. It bridges the gap between multi-source data analysis, Bayesian network technology, and synergistic optimization models, serving as a crucial resource for researchers, practitioners, and policymakers who aim to enhance the efficiency, reliability, and passenger satisfaction of public transit systems.

This book focuses on the innovative use of multi-source heterogeneous data to address key issues in public transit systems. Readers will explore advanced methodologies for data mining, problem diagnosis, mechanism analysis, and stochastic optimization tailored to the public transit context.This book stands out by integrating the latest in data processing techniques, pattern recognition, and mechanism analysis, providing a comprehensive framework for systematic diagnosis and optimization of transit systems. By examining both microscopic and macroscopic perspectives, it offers insights into passenger behavior, system reliability, and personalized transit solutions.The unique approach of this book lies in its holistic perspective, emphasizing the study of patterns, problem diagnosis, mechanism analysis, and model construction through the use of multi-source heterogeneous public transit data. It bridges the gap between multi-source data analysis, Bayesian network technology, and synergistic optimization models, serving as a crucial resource for researchers, practitioners, and policymakers who aim to enhance the efficiency, reliability, and passenger satisfaction of public transit systems.

Problem Diagnosis and Stochastic Optimization

Harnessing Multi-Source Heterogeneous Data for Public Transit

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

Was steckt hinter den Algorithmen der künstlichen Intelligenz?

Künstliche Intelligenz und maschinelles Lernen halten zunehmend Einzug, in betrieblichen Anwendungen wie in der Forschung. Nicht alle Anwender und Anwenderinnen verfügen jedoch über einen mathematisch-naturwissenschaftlichen Hintergrund.

Dieses Buch richtet sich an Studierende, Promovierende, Anwenderinnen und Anwender aller Fachrichtungen sowie an alle, die verstehen möchten, was hinter maschinellem Lernen und KI steckt. Es vermittelt ein grundlegendes Verständnis der zentralen Ideen hinter maschinellem Lernen (ML). Die zugrundeliegenden Konzepte stehen im Vordergrund, und diese werden anhand konkreter Verfahren wie Entscheidungsbäumen, linearer und logistischer Regression, neuronalen Netzen sowie Clusteranalyse veranschaulicht. Fallstudien mit Beispielen in der Programmiersprache R zeigen exemplarisch praktische Anwendungsfelder. Ergänzende Übungen laden zur Vertiefung ein.

Das Buch richtet sich ausdrücklich auch an Leser und Leserinnen ohne spezielle mathematische Vorkenntnisse und ist mit Grundwissen aus der Schulmathematik verständlich. Für die praktischen Übungen und Anwendungsbeispiele sind Grundkenntnisse in R erforderlich.

Die Produktfamilie WissensExpress bietet Ihnen Lehr- und Lernbücher in kompakter Form. Die Bücher liefern schnell und verständlich fundiertes Wissen.

Was steckt hinter den Algorithmen der künstlichen Intelligenz?Künstliche Intelligenz und maschinelles Lernen halten zunehmend Einzug, in betrieblichen Anwendungen wie in der Forschung. Nicht alle Anwender und Anwenderinnen verfügen jedoch über einen mathematisch-naturwissenschaftlichen Hintergrund.Dieses Buch richtet sich an Studierende, Promovierende, Anwenderinnen und Anwender aller Fachrichtungen sowie an alle, die verstehen möchten, was hinter maschinellem Lernen und KI steckt.&nbsp;Es vermittelt ein grundlegendes Verständnis der zentralen Ideen hinter maschinellem Lernen (ML). Die zugrundeliegenden Konzepte stehen im Vordergrund, und diese werden anhand konkreter Verfahren wie Entscheidungsbäumen, linearer und logistischer Regression, neuronalen Netzen sowie Clusteranalyse veranschaulicht. Fallstudien mit Beispielen in der Programmiersprache R zeigen exemplarisch praktische Anwendungsfelder. Ergänzende Übungen laden zur Vertiefung ein.Das Buch richtet sich ausdrücklich auch an Leser und Leserinnen ohne spezielle mathematische Vorkenntnisse und ist mit Grundwissen aus der Schulmathematik verständlich. Für die praktischen Übungen und Anwendungsbeispiele sind Grundkenntnisse in&nbsp;R erforderlich.
 Die Produktfamilie 
 WissensExpress
 &nbsp;bietet Ihnen Lehr- und Lernbücher in kompakter Form. Die Bücher liefern schnell und verständlich fundiertes Wissen.

verständlich erklärt - mit Fallstudien in R

Grundprinzipien des maschinellen Lernens

Geschäftsmodellentwicklung mit Business Intelligence. Digitale Transformation am Beispiel der Urbery GmbH

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