Gestión y análisis de datos con SAP BTP
¿Cómo le ayuda SAP BTP a crear un panorama de datos empresarial cohesivo? ¡Descúbralo en este libro! Comience por desarrollar una estrategia de datos basada en la nube y aprenda a evaluar y mejorar la calidad de sus datos. A continuación, explore las funciones clave de SAP BTP para datos y análisis: almacenamiento de datos con SAP HANA Cloud, modelado y gestión de datos con SAP Business Data Cloud, y toma de decisiones basada en datos con SAP Analytics Cloud. Póngase a la vanguardia con las herramientas de IA de SAP BTP, incluyendo SAP AI Core y Joule. Con información sobre escenarios del mundo real como la detección de fraudes y las cadenas de suministro éticas, ¡esta es la guía que necesita!
- Explore la cartera de gestión de datos y análisis de SAP BTP
- Conozca los productos clave, incluidos SAP HANA Cloud, SAP Datasphere y SAP Analytics Cloud
- Conozca las capacidades de IA de SAP con SAP AI Launchpad y SAP AI Core
Aprenderás sobre:
- Estrategia de datos:
¡Tome el control de sus datos! Elabore una estrategia para sus datos utilizando las metodologías proporcionadas por SAP. Explore las soluciones de SAP Business Technology Platform para la gobernanza, la seguridad, la calidad, la integración y la migración de datos, entre otras.
- Análisis:
Descubra las capacidades de SAP Analytics Cloud y vea cómo permite la toma de decisiones estratégicas, operativas y tácticas. Conozca los modelos de IA disponibles y gestione sus activos de IA utilizando herramientas como SAP AI Core y SAP AI Launchpad.
- Almacenamiento y gestión de datos:
¡Examine sus opciones para almacenar y gestionar datos! Explore las características clave de SAP Datasphere, desde la conectividad hasta el intercambio y la colaboración. Eche un vistazo a las capacidades de almacenamiento de datos y de lago de datos de SAP HANA Cloud.
Aspectos Destacados:
- Estrategia de datos
- Calidad de los datos
- Almacenamiento de datos
- Estructura de datos
- Gestión de datos maestros
- SAP Analytics Cloud
- SAP HANA Cloud
- SAP Datasphere
- SAP Business Data Cloud
- Inteligencia artificial
- Escenarios de negocio
Ver Tabla de Contenidos Completa
Data Management and Analytics with SAP BTP
How does SAP BTP help you create a cohesive enterprise data landscape? Find out in this book! Start by developing a cloud-based data strategy and learning how to assess and improve the quality of your data. Then walk through key SAP BTP functions for data and analytics: storing data with SAP HANA Cloud, data modeling and management with SAP Business Data Cloud, and data-driven decision-making with SAP Analytics Cloud. Get on the cutting edge with SAP BTP’s AI tools, including SAP AI Core and Joule. With information on real-world scenarios like fraud detection and ethical supply chains, this is the guide you need!
- Explore SAP BTP’s data management and analytics portfolio
- Learn about key products, including SAP HANA Cloud, SAP Datasphere, and SAP Analytics Cloud
- Get to know SAP’s AI capabilities with SAP AI Launchpad and SAP AI Core
You'll learn about:
- Data Strategy:
Take control of your data! Construct a strategy for your data using SAP-provided methodologies. Explore SAP Business Technology Platform’s solutions for data governance, security, quality, integration, migration, and more.
- Analytics:
Walk through the capabilities of SAP Analytics Cloud and see how it enables strategic, operational, and tactical decision-making. Understand the available AI models and handle your AI assets using tools like SAP AI Core and SAP AI Launchpad.
- Data Storage and Management:
Examine your options for storing and managing data! Explore key features of SAP Datasphere, from connectivity to sharing and collaboration. Take a look at the data storage and data lake capabilities of SAP HANA Cloud.
Key Highlights:
- Data strategy
- Data quality
- Data storage
- Data fabric
- Master data management
- SAP Analytics Cloud
- SAP HANA Cloud
- SAP Datasphere
- SAP Business Data Cloud
- Artificial intelligence
- Business scenarios
View Full Table of Contents
- Preface
- Who This Book Is For
- How This Book Is Organized
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- Chapter 10
- Acknowledgments
- Conclusion
- 1 Introduction
- 1.1 Build, Run, and Optimize a Business
- 1.2 Challenges for Executives
- 1.3 Characteristics of a Successful, Data-Focused Organization
- 1.4 Data Strategy and Information Management Maturity Models
- 1.5 Data Integration
- 1.5.1 General Components of Data Integration
- 1.5.2 Data Integration and Evolution
- 1.6 Data Tiers in SAP
- 1.6.1 Transactional System
- 1.6.2 Operational Data Store
- 1.6.3 Enterprise Data Warehouse
- 1.6.4 Enterprise Analytics
- 1.6.5 Data Quality
- 1.6.6 Data Archiving
- 1.6.7 Security
- 1.7 SAP Business Technology Platform
- 1.8 SAP’s Data and Analytics Portfolio
- 1.8.1 SAP Master Data Governance
- 1.8.2 SAP S/4HANA Migration Cockpit
- 1.8.3 SAP Business Data Cloud
- 1.8.4 SAP HANA Cloud
- 1.8.5 SAP BW/4HANA
- 1.8.6 SAP Analytics Cloud
- 1.9 Enterprise Value and Data Fabric
- 1.9.1 Approaches to Unifying Data
- 1.9.2 SAP’s Component Map
- 1.9.3 Non-SAP Component Map
- 1.10 Bringing Them Together with SAP
- 1.10.1 Run the Business
- 1.10.2 Evolve the Business
- 1.11 Summary
- 2 Data Strategy
- 2.1 Overview
- 2.2 Defining a Data Strategy
- 2.2.1 Developing a Data Strategy
- 2.2.2 Importance of a Data Strategy
- 2.2.3 Characteristics of an Effective Data Strategy
- 2.2.4 Applying the Clean Core Philosophy Within Data Management
- 2.3 Data and Information Management Maturity Models
- 2.3.1 Methodology
- 2.3.2 Catalog of Models
- 2.3.3 Next Steps
- 2.4 SAP’s Advisory Methodologies
- 2.4.1 SAP Application Extension Methodology
- 2.4.2 SAP Integration Solution Advisory Methodology
- 2.4.3 SAP Data and Analytics Advisory Methodology
- 2.5 Summary
- 3 Overview of SAP Business Technology Platform
- 3.1 Fundamentals
- 3.1.1 Global Account
- 3.1.2 Directories
- 3.1.3 Subaccounts
- 3.1.4 Regions and Providers
- 3.1.5 Environments
- 3.1.6 Entitlements and Quotas
- 3.1.7 Services
- 3.2 Data and Analytics
- 3.2.1 Data Products
- 3.2.2 SAP Business Data Cloud Cockpit
- 3.2.3 SAP Datasphere
- 3.2.4 SAP Analytics Cloud
- 3.2.5 SAP Databricks
- 3.3 Application Development
- 3.3.1 SAP Build Apps
- 3.3.2 SAP Build Code
- 3.3.3 SAP Build Process Automation
- 3.3.4 SAP Build Work Zone
- 3.3.5 Joule Studio
- 3.4 Integration
- 3.4.1 SAP Cloud Integration
- 3.4.2 Migration Assessment
- 3.4.3 Integration Technology Guidelines
- 3.4.4 Extend Connectivity
- 3.4.5 SAP API Management
- 3.4.6 Trading Partner Management
- 3.4.7 SAP Integration Suite, Advanced Event Mesh
- 3.4.8 Access Data in Classic SAP Business Suite with OData Provisioning
- 3.5 Automation
- 3.5.1 SAP Build Process Automation
- 3.5.2 SAP Automation Pilot
- 3.6 Artificial Intelligence
- 3.6.1 SAP Business AI
- 3.6.2 SAP AI Core
- 3.6.3 SAP AI Launchpad
- 3.6.4 SAP AI Services
- 3.7 Use Cases
- 3.7.1 Goals
- 3.7.2 Solution
- 3.7.3 Overall Architecture and Integration
- 3.8 Summary
- 4 Data Quality
- 4.1 Introduction to Data Quality
- 4.1.1 Data Quality Terminology
- 4.1.2 Roles and Responsibilities in Data Quality
- 4.1.3 Categories of Data
- 4.1.4 Type of Data Quality Defects
- 4.1.5 Causes of Data Quality Defects
- 4.1.6 Data Quality Approach
- 4.1.7 Benefits Realized from Profiling and Cleansing
- 4.2 Data Profiling Approach
- 4.2.1 Introduction to Data Profiling
- 4.2.2 Data Profiling Fundamentals
- 4.2.3 Identify Data Risks and Issues with a Data Quality Assessment
- 4.2.4 When to Perform a Data Quality Assessment
- 4.2.5 Data Quality Assessment Process
- 4.2.6 Data Profiling Best Practices
- 4.2.7 Other Considerations
- 4.3 Data Cleansing Approach
- 4.3.1 Introduction to Data Cleansing
- 4.3.2 Data Cleansing Fundamentals
- 4.3.3 Correcting Data Quality Defects with Data Cleansing
- 4.3.4 Determining How Much Cleansing Is Enough
- 4.3.5 Data Cleansing Methodology
- 4.3.6 Data Cleansing Best Practices
- 4.3.7 Other Considerations
- 4.4 User Training and Enablement
- 4.4.1 SAP Learning Journeys
- 4.4.2 Training Courses
- 4.4.3 Other Learning Resources
- 4.5 Summary
- 5 Master Data Management
- 5.1 Overview
- 5.2 Key Features
- 5.2.1 Consolidation
- 5.2.2 Mass Processing
- 5.2.3 Central Governance
- 5.2.4 Data Quality Management
- 5.2.5 Process Analytics
- 5.2.6 Federated Master Data Governance
- 5.3 Architecture and Deployment Models
- 5.3.1 Choosing a Deployment Model
- 5.3.2 SAP Master Data Governance on SAP S/4HANA Cloud, Private Edition
- 5.3.3 SAP Master Data Governance, Cloud Edition
- 5.3.4 SAP S/4HANA Cloud Public Edition, Master Data Governance
- 5.4 Master Data Integration
- 5.5 Enabling a Business Data Fabric
- 5.6 User Training and Enablement
- 5.7 Summary
- 6 Data Storage with SAP HANA Cloud
- 6.1 Key Features
- 6.1.1 Data Storage Capabilities
- 6.1.2 Data Lake Capabilities
- 6.1.3 Real-Time Data Access
- 6.1.4 Native Multi-Model
- 6.1.5 Built-In Machine Learning, Predictive Analytics, and Search
- 6.1.6 Security
- 6.2 Use Cases
- 6.2.1 Integration with Business Intelligence Tools
- 6.2.2 Predictive Analytics
- 6.2.3 Transition to Cloud
- 6.3 User Training and Enablement
- 6.3.1 Training Courses
- 6.3.2 SAP Learning Journeys
- 6.3.3 Learning Resources
- 6.4 Summary
- 7 Data Fabric with SAP Business Data Cloud
- 7.1 Architecture
- 7.1.1 Source Systems
- 7.1.2 Foundation Services
- 7.1.3 Data Products
- 7.1.4 Delta Share Protocol
- 7.1.5 Onboarding
- 7.2 SAP Business Data Cloud Cockpit
- 7.3 SAP Datasphere
- 7.3.1 Architecture
- 7.3.2 Space and Connection Management
- 7.3.3 Modeling: Data Builder
- 7.3.4 Modeling: Business Builder
- 7.3.5 Data Integration Monitor
- 7.3.6 Catalog and Marketplace
- 7.4 SAP Business Warehouse and SAP BW/4HANA
- 7.5 SAP Databricks
- 7.6 SAP Analytics Cloud
- 7.7 Business Use Cases
- 7.7.1 Solution Overview
- 7.7.2 Data Integration and Modeling
- 7.7.3 C-Level Dashboards
- 7.7.4 Machine Learning Applications
- 7.7.5 Governance and Scalability
- 7.7.6 Conclusion
- 7.8 Summary
- 8 Data-Driven Decision-Making with SAP Analytics Cloud
- 8.1 Introduction
- 8.1.1 Data and Analytics Strategy
- 8.1.2 Business Cases for SAP Analytics Cloud
- 8.1.3 SAP Analytics Cloud Value Proposition
- 8.2 SAP Analytics Cloud Capabilities
- 8.2.1 Analytical Capabilities
- 8.2.2 Planning Capabilities
- 8.2.3 AI Capabilities
- 8.2.4 Integration with Microsoft Office
- 8.3 Decision-Making with SAP Analytics Cloud
- 8.3.1 Operational
- 8.3.2 Tactical
- 8.3.3 Strategic
- 8.3.4 On-the-Go
- 8.4 Sharing, Collaborating, and Exporting
- 8.4.1 Sharing and Publishing Content
- 8.4.2 Commenting and Discussing
- 8.4.3 Exporting Content
- 8.5 Business Content and Accelerators
- 8.5.1 Packages, Content, and Templates
- 8.5.2 Getting Business Content
- 8.5.3 Business Content Usage
- 8.6 User Training and Enablement
- 8.6.1 Training Courses
- 8.6.2 SAP Learning Journeys
- 8.6.3 Learning Resources
- 8.7 Best Practices
- 8.8 Summary
- 9 Artificial Intelligence for Data and Analytics
- 9.1 AI Concepts
- 9.1.1 AI Models
- 9.1.2 AI Workflow and Interface
- 9.1.3 Additional AI Considerations
- 9.2 AI in SAP Solutions
- 9.2.1 Joule
- 9.2.2 Embedded AI
- 9.2.3 AI Foundation
- 9.3 Integration with SAP Products
- 9.4 SAP Joule Assistance During Implementation
- 9.5 User Training and Enablement
- 9.5.1 SAP Discovery Center
- 9.5.2 SAP Tutorials
- 9.6 Summary
- 10 Business and Integration Scenarios
- 10.1 Sustainable Enterprise
- 10.1.1 Significance of Addressing the Challenge
- 10.1.2 Overview of Potential Solutions
- 10.1.3 Components Contributing to the Solution
- 10.2 Ethical Supply Chain
- 10.2.1 Significance of Addressing the Challenge
- 10.2.2 Overview of Potential Solutions
- 10.2.3 Components Contributing to the Solution
- 10.3 Managing Private and Secure Data
- 10.3.1 Significance of Addressing the Challenge
- 10.3.2 Overview of Potential Solutions
- 10.3.3 Components Contributing to the Solution
- 10.4 Fraud Detection
- 10.4.1 Significance of Addressing the Challenge
- 10.4.2 Overview of Potential Solutions
- 10.4.3 Components Contributing to the Solution
- 10.5 Predictive Maintenance
- 10.5.1 Significance of Addressing the Challenge
- 10.5.2 Overview of Potential Solutions
- 10.5.3 Components Contributing to the Solution
- 10.6 Data Quality Firewall
- 10.6.1 Significance of Addressing the Challenge
- 10.6.2 Overview of Potential Solutions
- 10.6.3 Components Contributing to the Solution
- 10.7 Guided End-User Priorities and Application Navigation
- 10.7.1 Significance of Addressing the Challenge
- 10.7.2 Overview of Potential Solutions
- 10.7.3 Components Contributing to the Solution
- 10.8 Custom Operating Guides and User Documentation
- 10.8.1 Significance of Addressing the Challenge
- 10.8.2 Overview of Potential Solutions
- 10.8.3 Components Contributing to the Solution
- 10.9 Summary
- The Authors
- Index
