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
