Technics publications jan 2026 (10 risultati)

Taschenbuch. Condizione: Neu. Neuware -Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business.You'll get a clear, practical definition of a data product as a 'well-defined, reusable, governed, and user-oriented data asset,' plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ('compliance by design', catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap. …

Taschenbuch. Condizione: Neu. Neuware - Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business.You'll get a clear, practical definition of a data product as a 'well-defined, reusable, governed, and user-oriented data asset,' plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ('compliance by design', catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap. …

Taschenbuch. Condizione: Neu. Neuware -Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business.You'll get a clear, practical definition of a data product as a 'well-defined, reusable, governed, and user-oriented data asset,' plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ('compliance by design', catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap 150 pp. Englisch. …

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 332 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business.You'll get a clear, practical definition of a data product as a 'well-defined, reusable, governed, and user-oriented data asset,' plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ('compliance by design', catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap 150 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business.You'll get a clear, practical definition of a data product as a 'well-defined, reusable, governed, and user-oriented data asset,' plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ('compliance by design', catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap 150 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Take charge of AI in your organization with practical, step-by-step risk tools that help you spot problems early, make confident decisions, and become the trusted person others turn to when AI is on the line.This comprehensive guide bridges the gap between traditional enterprise risk management and the emerging challenges of artificial intelligence. Written specifically for risk managers in large organizations, it transforms abstract AI concepts into actionable frameworks, real-world practices, and proven strategies.The book addresses the fundamental shift facing risk professionals today: AI is no longer a future consideration but a present reality that demands new approaches to identification, assessment, and mitigation. Rather than replacing existing risk management principles, this guide shows how to evolve them for an AI-enabled world.Each chapter opens with a realistic scenario drawn from actual enterprise experiences, illustrating the human and organizational dimensions of AI risk. These stories ground complex concepts in relatable situations, making the technical accessible and the theoretical practical.The content balances governance frameworks with technical understanding, emphasizing the NIST AI Risk Management Framework while incorporating insights from EU AI Act, ISO standards, and industry-specific regulations. Readers will find detailed case studies from healthcare, financial services, manufacturing, and technology sectors, demonstrating how leading organizations have successfully integrated AI risk management into their operations.Throughout the book, Key Concepts boxes provide deeper explanations of complex topics and direct readers to authoritative resources for continued learning. Visual elements including risk matrices, decision trees, assessment frameworks, and process flows enhance comprehension and provide ready-to-adapt templates.This guide equips risk managers with the knowledge, tools, and confidence to lead their organizations through the AI transformation-not as passive observers of technological change, but as strategic enablers of responsible innovation. 274 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Transform your AI governance from a compliance burden into a competitive advantage.In a world where artificial intelligence determines welfare eligibility, credit limits, and even medical treatment, the difference between innovation and disaster lies in one thing: responsible implementation. The Responsible AI Implementation Workbook turns abstract ethics and complex regulations into a practical, step-by-step framework that anyone from project managers to executives can follow to make AI systems lawful, ethical, and trustworthy.This hands-on workbook goes far beyond theory. Built around Australia's AI Technical Standard and its 42 actionable statements, it provides ready-to-use templates, checklists, and implementation tools for embedding AI governance into every stage of the lifecycle-from design to decommissioning. Each chapter walks you through what the requirement means, why it matters, and how to apply it, using real-world case studies from global failures like Robodebt, Horizon, and the Dutch childcare benefits scandal. The result is a clear roadmap for building AI systems that work as intended and can prove they do.You'll learn how to translate the 42 statements into measurable safeguards: how to establish data quality frameworks, run fairness and bias testing, deploy monitoring systems that detect drift, and create decommissioning plans that maintain accountability long after launch. With its adaptable tools and risk-based approach, the workbook helps you start where you are-whether you need a single compliance quick win or a full governance overhaul.By integrating governance into daily workflows, organizations gain not only regulatory readiness but also trust, resilience, and sustainable competitive edge. The workbook's structure,&nbspLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 332 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results.See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business.You'll get a clear, practical definition of a data product as a 'well-defined, reusable, governed, and user-oriented data asset,' plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability.Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ('compliance by design', catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmapLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 150 pp. Englisch.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Take charge of AI in your organization with practical, step-by-step risk tools that help you spot problems early, make confident decisions, and become the trusted person others turn to when AI is on the line.This comprehensive guide bridges the gap between traditional enterprise risk management and the emerging challenges of artificial intelligence. Written specifically for risk managers in large organizations, it transforms abstract AI concepts into actionable frameworks, real-world practices, and proven strategies.The book addresses the fundamental shift facing risk professionals today: AI is no longer a future consideration but a present reality that demands new approaches to identification, assessment, and mitigation. Rather than replacing existing risk management principles, this guide shows how to evolve them for an AI-enabled world.Each chapter opens with a realistic scenario drawn from actual enterprise experiences, illustrating the human and organizational dimensions of AI risk. These stories ground complex concepts in relatable situations, making the technical accessible and the theoretical practical.The content balances governance frameworks with technical understanding, emphasizing the NIST AI Risk Management Framework while incorporating insights from EU AI Act, ISO standards, and industry-specific regulations. Readers will find detailed case studies from healthcare, financial services, manufacturing, and technology sectors, demonstrating how leading organizations have successfully integrated AI risk management into their operations.Throughout the book, Key Concepts boxes provide deeper explanations of complex topics and direct readers to authoritative resources for continued learning. Visual elements including risk matrices, decision trees, assessment frameworks, and process flows enhance comprehension and provide ready-to-adapt templates.This guide equips risk managers with the knowledge, tools, and confidence to lead their organizations through the AI transformation-not as passive observers of technological change, but as strategic enablers of responsible innovation.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 272 pp. Englisch.…