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data mining --- business analytics --- data-driven management decisions --- digital economy --- data quality and security --- data science
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Die Autor:innen zeigen in diesem Buch, wie man mit Analytics- und Artificial-Intelligence-Projekten echten (Mehr-)Wert schafft. Sie geben Ihnen an die Hand, was Sie wissen müssen, um Ihre Datenprojekte agil, effizient und nutzer:innenzentriert konzipieren und umsetzen zu können: Von den agilen Basics und den Grundlagen des Design Thinkings bis hin zu den Funktionsweisen von Artificial Intelligence und den ethischen, ökologischen und rechtlichen Implikationen von Big Data. Die Autor:innen entwickeln einen Leitfaden, der Ihnen hilft, zu Beginn Ihrer Datenprojekte die richtigen Fragen zu stellen und Ihnen zeigt, wie Sie Technologien und Daten so einsetzen, dass sie einen echten Mehrwert erzeugen. Das Fundament dafür bilden Data Thinking und agile Methoden, die die Autor:innen in alltägliche Analytics- und Data-Science-Projekte überführt und adaptiert haben. Mit zahlreichen Beispielen aus Daten- und Digital-Analytics-Projekten sowie Einblicken in die Praxis, wie man von der Idee zum Prototypen kommt. Aus dem Inhalt Agile Basics – Agile Prinzipien und Erfolgsfaktoren Vom Design Thinking zum Data Thinking – wie Design Thinking Datenprojekte besser macht Artificial Intelligence – was AI eigentlich ist und wie AI funktioniert Ethische, rechtliche und ökologische Implikationen – wie Data Analytics und AI doch kein Schreckgespenst werden Der Data Value Loop – Datenmehrwert agil und nutzer:innenzentriert Analytics in der Praxis – von der Konzeption über Tracking und Reporting bis zum Arbeitsmeeting im Alltag AI in der Praxis – Data Science und Agile, geht das überhaupt zusammen? Zwei exemplarische Projektdurchführungen Glossar Die Autorin und die Autoren Dr. Ramona Greiner studierte Philosophie und Kunstgeschichte. Seit 2017 arbeitet sie als Digital Analytics und Data Ethics Consultant bei der Münchner Unternehmensberatung FELD M. David Berger ist zertifizierter Product Owner und hat mehrjährige Erfahrung in der Leitung von Data- und Analytics-Projekten für global agierende Kunden. Dr. Matthias Böck promovierte in Bioinformatik und Machine Learning und arbeitet seit 2013 als Data Scientist bei der Münchner Unternehmensberatung FELD M.
Marketing. --- Business --- Strategic planning. --- Leadership. --- Business Analytics. --- Business Strategy and Leadership. --- Data processing.
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The ability to capture customer needs and to tailor the provided solutions accordingly, also defined as customer intimacy, has become a significant success factor in the B2B space - in particular for increasingly "servitizing" businesses. This book elaborates on the solution CI Analytics to assess and monitor the impact of customer intimacy strategies by leveraging business analytics and social network analysis technology. This solution thereby effectively complements existing CRM solutions.
Customer intimacy . --- Business analytics. --- Social networks. --- Networking, Social --- Networks, Social --- Social networking --- Social support systems --- Support systems, Social --- Interpersonal relations --- Cliques (Sociology) --- Microblogs --- Services --- Social Networks --- Business Analytics --- Customer Intimacy --- Strategy
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This project-oriented book presents a hands-on approach to identifying migration and performance issues with experience drawn from real-world examples. As you work through the examples, you will develop the skill, knowledge, and deep understanding of Snowflake tuning options and capabilities while preparing for later incorporation of additional Snowflake features as they become available. Your Snowflake platform will cost less to run and will improve your customer experience. Written by a seasoned Snowflake practitioner, this book is full of practical, hand-on guidance and advice specifically designed to further accelerate your Snowflake journey. Tuning the Snowflake Data Cloud provides you a pathway to success by equipping you with skill, knowledge, and expertise to improve your Snowflake experience. The book shows you how to leverage what you already know, adds what you don’t, all applied to delivering your Snowflake accounts. Read this book to embark on a voyage of advancement and equip your organization to deliver consistent Snowflake performance utilizing the toolkit developed here. What You Will Learn Recognize and understand the root cause of performance bottlenecks Know how to resolve performance issues Develop a deep understanding of Snowflake performance tuning options Reduce expensive mistakes, remediate poorly performing code Manage Snowflake costs .
Cloud computing. --- Big data. --- Database management. --- Cloud Computing. --- Business --- Database Management. --- Business Analytics. --- Big Data. --- Data processing.
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This book provides practical insights into applications of the state-of-the-art of Machine Learning and Artificial Intelligence (AI) for solving intriguing and complex problems in procurement and supply chain management. The application domain includes perishable food supply chain, steel price prediction, electric vehicle charging infrastructure design, contract price negotiation, reverse logistics network design, and demand forecasting. Further, the book highlights the advanced topics in the procurement field, like AI in green procurement and e-procurement in the pharma sector. Furthermore, the book covers applications of well-established methodologies such as heuristics, optimization, game theory, and MCDM based on the nature of the problem. The inclusion of the vaccine supply chain digital twin and blockchain-based procurement signals the significance of the book. This book is a comprehensive guide for industry professionals to understand the power of data analytics, enabling them to improve efficiency and effectiveness in the procurement and supply chain sectors.
Business logistics. --- Business --- Industrial procurement. --- Machine learning. --- Quantitative research. --- Supply Chain Management. --- Business Analytics. --- Procurement. --- Machine Learning. --- Data Analysis and Big Data. --- Data processing.
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Long description: Erfolgsfaktoren für BI-Architekturen Umfassendes und anwendungsbezogenes Handbuch Einsatz von neuen Technologien wie EAI, Virtualisierung sowie Cloud- und Data-Lake-Architekturen Mit vielen Praxisbeispielen aus der BI & Analytics-Welt Sowohl regulatorische Vorgaben als auch gesteigerte Anforderungen seitens der Fachanwender haben in den letzten Jahren zu immer komplexeren Business-Intelligence- und Analytics-Landschaften geführt, die es zu entwickeln und betreiben gilt. So setzt sich eine heute übliche Architektur aus zahlreichen Einzelkomponenten zusammen, deren Zusammenspiel und funktionale Abdeckung als wesentlicher Erfolgsfaktor für zugehörige BIA-Initiativen zu werten ist.Dieses Buch setzt sich das Ziel, die derzeit gebräuchlichen Architekturmuster zu beschreiben und dabei einen Überblick über die aktuell verwendeten Technologien zu liefern. Dabei werden nicht nur die architektonischen Frameworks der großen Produktanbieter aufgegriffen, sondern darüber hinaus Lösungen für konkrete Anwendungsfälle präsentiert. Biographical note: Prof. Dr. Peter Gluchowski leitet den Lehrstuhl für Wirtschaftsinformatik, insb. Systementwicklung und Anwendungssysteme, an der Technischen Universität in Chemnitz und konzentriert sich dort mit seinen Forschungsaktivitäten auf das Themengebiet Business Intelligence & Analytics. Er beschäftigt sich seit mehr als 25 Jahren mit Fragestellungen, die den praktischen Aufbau dispositiver bzw. analytischer Systeme zur Entscheidungsunterstützung betreffen. Seine Erfahrungen aus unterschiedlichsten Praxisprojekten sind in zahlreichen Veröffentlichungen zu diesem Themenkreis dokumentiert. Frank Leisten ist passionierter Berater für datengetriebene Vorhaben mit modernen Technologien. Seine Expertise in den Funktionen des Datenmanagements sowie jahrelange Praxiserfahrung in verschiedenen IT-Domänen und Rollen kommen seinen Kunden bei der Orchestrierung sowie der kulturellen und strategischen Entwicklung ihrer Transformationen zugute. Dr. Gero Presser ist Mitgründer und Geschäftsführer bei der QuinScape GmbH, einem Dortmunder IT-Dienstleistungsunternehmen mit 170 Mitarbeitern und Fokus auf Data & Analytics. Er organisiert die Meetup-Gruppe Business Intelligence & Analytics Dortmund mit über 1.000 Mitgliedern und ist Vorsitzender des TDWI Roundtable Ruhrgebiet.
Datenschutz --- Datenmanagement --- IT-Governance --- Software-Architektur --- Metadaten --- Business Intelligence --- Datenqualität --- Analytics --- Datenqualitätsmanagement --- Digitale Transformation --- Business Analytics --- Datenpflege --- BIA-Ökosystem --- BIA-Landschaft --- Data Lake --- Cloud-Architekturen --- Datenvirtualisierung
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Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance. Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoringmodels and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized. Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide.
Machine learning. --- Engineering --- Artificial intelligence --- Business --- Mathematical statistics --- Machine Learning. --- Data Engineering. --- Data Science. --- Business Analytics. --- Statistics and Computing. --- Data processing.
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Service has a unique ability to create experiences that build profitable relationships with customers. Based on a service-centered perspective, this book analyzes the challenges of creating excellent customer experiences, including the management of technology and new media. It describes how customers coproduce and cocreate their experiences, and how these activities influence business revenues and costs. Customer Experience refers to the sensory, cognitive, emotional, social, and behavioral dimensions of all activities that connect the customer and the organization over time across touchpoints and channels. It encompasses all activities involving the customer where the organization is the focal object, including prepurchase activities (such as exposure to a website ad), and purchase, consumption, and engagement behaviors (blogging, sharing photos). The book takes a deep dive into the psychology of customers, revealing the conceptual building blocks of customer experiences and how they build relationships over time. These ideas provide a business perspective on how customer-focused service strategies generate cash flows, including the role of pricing.
Customer services. --- big data --- business analytics --- cocreation --- customer equity --- customer experience --- customer journey --- customer lifetime value --- customer retention --- design --- digital media --- innovation --- loyalty --- relationship --- satisfaction --- service
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This book is a collection of studies that explores the adoption, applications, and implications of emerging technologies in business. Given that emerging technologies have the potential to significantly disrupt and transform existing business models, the premise is to demonstrate how theories are translated into practice. Readers will gain insights into operating processes and business models, the diffusion of innovation in business and industry, and how humans interact with emerging technologies.
Technological innovations. --- Business --- Marketing research. --- Business intelligence. --- Business information services. --- Economics. --- Innovation and Technology Management. --- Business Analytics. --- Market Research and Competitive Intelligence. --- Business Information Systems. --- Data processing.
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This book presents selected proceedings of DEA45: International Conference on Data Envelopment Analysis, which was held September 4-6, 2023, at Surrey Business School, University of Surrey, Guildford, UK. It contains theoretical and empirical papers on Data Envelopment Analysis (DEA) and related fields with a focus on performance measurement and management. It discusses the latest research and developments and their application in various areas such as regulation, agriculture, education, financial and health services. The book is of interest to both researchers and practitioners working on or utilizing the DEA method for examining efficiencies across various organizations. .
Operations research --- Operations research. --- Business --- Production management. --- Business information services. --- Big data. --- Operations Research and Decision Theory. --- Business Analytics. --- Operations Management. --- IT in Business. --- Big Data. --- Data processing.
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