SAP Analytics Cloud: Predictive Analytics

Multilingual Book

🇺🇸 English (Original) 🇪🇸 Spanish (Translation)

✅ All languages are included in your purchase

Publisher SAP PRESS

Year / Edition 2024 / 1

Pages 315

Authors Antoine Chabert, David Serre

SAP Analytics Cloud: Predictive Analytics

Today’s organizations must be prepared for tomorrow’s events. Forecast future behavior in SAP Analytics Cloud with this comprehensive guide to predictive analytics! Start by learning about the data types, scenarios, and methods used in predictive analytics projects. Then follow step-by-step instructions to build, analyze, and apply predictive models to your business data using classification, time series forecasting, and regression analysis. Automate your models and dive into the data science with this all-in-one guide!

Learn to use SAP Analytics Cloud for predictive analytics
Build time series, classification, and regression models
Enrich SAP Analytics Cloud stories and planning processes with predictive models

You'll learn about:

Predictive Scenarios and Projects:
Understand the basics of predictive analytics in SAP Analytics Cloud: scenarios, data types, and actions. Then plan your predictive project, including identifying the key stakeholders and reviewing the methodology.
Build, Train, Analyze, and Apply:
Master predictive models from end to end. Create classification, time series, and regression models; then train them to identify business patterns. Analyze and apply the results of your models to data in SAP Analytics Cloud.
Practical Demonstrations:
See predictive analytics in action! Identify use cases for predictive modeling. For each data model, understand practical applications through curated examples with sample business data.

Key Highlights:

Predictive scenarios
Predictive forecasts
Data modeling
Planning
Time series model
Classification model
Regression model
Multi-actions
Data science
Stories and dashboards

View Full Table of Contents

Preface
- Objective of This Book
- Target Audience
- Structure of This Book
  - Part I: Getting Started
  - Part II: Time Series Forecasting Models
  - Part III: Classification Models and Regression Models
- Acknowledgments
- Conclusion
I Getting Started
1 An Introduction to Predictive Analytics in SAP Analytics Cloud
- 1.1 The Importance of Predictive Analytics
  - 1.1.1 A Brief History of Predictive Analytics
  - 1.1.2 The Role of Predictive Analytics in Business
- 1.2 Predictive Analytics in SAP Analytics Cloud
  - 1.2.1 A Brief History of Predictive Analytics in SAP Analytics Cloud
  - 1.2.2 Business Intelligence, Enterprise Planning, and Augmented Analytics
- 1.3 Customer Use Cases
  - 1.3.1 Time Series Forecasting
  - 1.3.2 Classification and Regression
- 1.4 Summary
2 What Are Predictive Scenarios?
- 2.1 Introducing Predictive Scenarios
  - 2.1.1 Using and Securing Predictive Scenarios
  - 2.1.2 User Interface of Predictive Scenarios
- 2.2 The Different Types of Predictive Scenarios
- 2.3 The Predictive Ecosystem
  - 2.3.1 Datasets
  - 2.3.2 Planning Models
  - 2.3.3 Stories
  - 2.3.4 Multi Actions
  - 2.3.5 Data Actions
- 2.4 Summary
3 Predictive Analytics Projects
- 3.1 Predictive Analytics Project Stakeholders
  - 3.1.1 Business Owners
  - 3.1.2 Data Engineers
  - 3.1.3 Prediction Creators
  - 3.1.4 Story Designers
  - 3.1.5 Prediction Consumers
  - 3.1.6 Information Technology
- 3.2 How to Implement a Predictive Analytics Project
  - 3.2.1 Business Understanding
  - 3.2.2 Data Understanding
  - 3.2.3 Data Preparation
  - 3.2.4 Predictive Modeling
  - 3.2.5 Predictive Model Evaluation
  - 3.2.6 Delivering Predictions
- 3.3 Summary
II Time Series Forecasting Models
4 Introducing Time Series Forecasting Models
- 4.1 What Is Time Series Forecasting?
  - 4.1.1 Key Terms and Concepts
  - 4.1.2 Top-Down versus Bottom-Up Predictive Forecasting
- 4.2 Data Sources for Time Series Forecasting Models
  - 4.2.1 Data Sources
  - 4.2.2 Basic Data Preparation for Datasets
- 4.3 End-to-End Time Series Forecasting Workflows
  - 4.3.1 Time Series Forecasting Based on Planning Models
  - 4.3.2 Time Series Forecasting Based on Datasets
- 4.4 Summary
5 Using Predictive Forecasts in the Planning Process
- 5.1 Business Scenario
- 5.2 Planning Models
  - 5.2.1 Measures
  - 5.2.2 Dimensions
  - 5.2.3 Versions
  - 5.2.4 Currencies
- 5.3 Creating Time Series Forecasting Models with Predictive Planning
  - 5.3.1 Create a Predictive Scenario
  - 5.3.2 General Settings
  - 5.3.3 Predictive Goal
  - 5.3.4 Predictive Model Training
  - 5.3.5 Influencers
  - 5.3.6 Entities
- 5.4 Understanding Time Series Forecasting Models
  - 5.4.1 Assessing Model Performance
  - 5.4.2 Time Series Forecasting Model Reports
- 5.5 Improving Time Series Forecasting Models with Predictive Planning
  - 5.5.1 Finding the Optimal Training Dataset Size
  - 5.5.2 Filtering Entities
  - 5.5.3 Adding Influencers
- 5.6 Saving Predictive Forecasts
  - 5.6.1 Creating a Private Version
  - 5.6.2 Saving Forecasts into a Private Version
- 5.7 Including Predictive Forecasts in a Story
  - 5.7.1 Including Predictive Forecasts in a Table
  - 5.7.2 Including Predictive Forecasts in a Time Series Chart
- 5.8 Summary
6 Automating the Production of Predictive Forecasts
- 6.1 Introducing Multi Actions
- 6.2 Creating Predictive Steps in Multi Actions
- 6.3 Using Multi Actions
  - 6.3.1 Scheduling Multi Actions
  - 6.3.2 Triggering Multi Actions
- 6.4 Summary
7 Time Series Forecasting Models Using Datasets
- 7.1 Business Scenario
- 7.2 Creating and Editing Datasets
  - 7.2.1 Creating the Training Dataset
  - 7.2.2 Additional Dataset Considerations
- 7.3 Creating Time Series Forecasting Models on Datasets
  - 7.3.1 General Settings
  - 7.3.2 Predictive Goal
  - 7.3.3 Predictive Model Training
  - 7.3.4 Datasets versus Planning Models
- 7.4 Understanding Time Series Forecasting Models Based on Datasets
  - 7.4.1 Assessing Model Performance
  - 7.4.2 Time Series Forecasting Model Reports
- 7.5 Improving Time Series Forecasting Models Based on Datasets
  - 7.5.1 Finding the Optimal Training Data Size
  - 7.5.2 Filtering Entities
  - 7.5.3 Adding Influencers
- 7.6 Saving Predictive Forecasts
- 7.7 Including Predictive Forecasts in a Story
  - 7.7.1 Report on Predictive Forecasts in a Table
  - 7.7.2 Report on Predictive Forecasts in a Time Series Chart
- 7.8 Summary
8 Best Practices and Tips for Time Series Forecasting Models
- 8.1 Going Beyond 1,000 Entities
  - 8.1.1 Forecast by Batch of 1,000 Entities
  - 8.1.2 Aggregate to Predict and Disaggregate to Plan and Report
  - 8.1.3 Compared Approaches
- 8.2 Handling Time Series with Missing Data
- 8.3 Considering Time Granularities
  - 8.3.1 Using Datasets
  - 8.3.2 Using Planning Models
  - 8.3.3 Additional Recommendations
- 8.4 Creating Ad Hoc Performance Indicators
  - 8.4.1 Saving Forecasts for Past Periods
  - 8.4.2 Custom Performance Indicators
- 8.5 Generating What-If Simulations
- 8.6 Forecasting Data at the Right Level
  - 8.6.1 Maximizing Forecasting Accuracy
  - 8.6.2 Data Quality Is Key
  - 8.6.3 Predictive Planning versus Planning
  - 8.6.4 Scalability
  - 8.6.5 Key Takeaways
- 8.7 Summary
9 The Data Science behind Time Series Forecasting Models
- 9.1 End-to-End Process
- 9.2 Additive Modeling Technique
  - 9.2.1 Trend
  - 9.2.2 Cycles
  - 9.2.3 Influencers
  - 9.2.4 Fluctuations
- 9.3 Exponential Smoothing
  - 9.3.1 Simple Exponential Smoothing
  - 9.3.2 Double Exponential Smoothing
  - 9.3.3 Triple Exponential Smoothing
  - 9.3.4 Damped Trend
- 9.4 Predictive Model Selection
- 9.5 Summary
III Classification Models and Regression Models
10 Introducing Classification Models and Regression Models
- 10.1 What Is Classification?
  - 10.1.1 Key Terms and Concepts
  - 10.1.2 Binary Classification
  - 10.1.3 Predicted Category and Probability
- 10.2 What Is Regression?
  - 10.2.1 Key Terms and Concepts
  - 10.2.2 Predictions
- 10.3 Data Sources for Classification Models and Regression Models
  - 10.3.1 Data Sources
  - 10.3.2 Basic Data Preparation for Datasets
- 10.4 End-to-End Workflow
- 10.5 Summary
11 Creating Classification Insights to Enrich Stories
- 11.1 Business Scenario
- 11.2 Using Datasets with Classification Models
  - 11.2.1 Training Dataset
  - 11.2.2 Application Dataset
  - 11.2.3 Predictions Dataset
- 11.3 Creating Classification Models
  - 11.3.1 Create a Predictive Scenario
  - 11.3.2 General Settings
  - 11.3.3 Predictive Goal
  - 11.3.4 Influencers
- 11.4 Understanding Classification Models
  - 11.4.1 Assessing Model Performance
  - 11.4.2 Influencer Contributions
  - 11.4.3 Confusion Matrix
  - 11.4.4 Profit Simulation
- 11.5 Improving Classification Models
  - 11.5.1 Basic Checks
  - 11.5.2 Improving Model Accuracy
  - 11.5.3 Improving Model Robustness
  - 11.5.4 The Quality versus Robustness Trade-Off
- 11.6 Applying Classification Models
- 11.7 Enriching Stories with Classification Insights
  - 11.7.1 Prediction Probability
  - 11.7.2 Prediction Explanations
- 11.8 Summary
12 Creating Regression Insights to Enrich Stories
- 12.1 Business Scenario
- 12.2 Using Datasets with Regression Models
  - 12.2.1 Training Dataset
  - 12.2.2 Application Dataset
  - 12.2.3 Predictions Dataset
- 12.3 Creating Regression Models
  - 12.3.1 Create a Predictive Scenario
  - 12.3.2 General Settings
  - 12.3.3 Predictive Goal
  - 12.3.4 Influencers
- 12.4 Understanding Regression Models
  - 12.4.1 Assessing Model Performance
  - 12.4.2 Influencer Contributions
- 12.5 Improving Regression Models
  - 12.5.1 Improving Model Accuracy
  - 12.5.2 Improving Model Robustness
  - 12.5.3 The Quality versus Robustness Trade-Off
- 12.6 Applying Regression Models
- 12.7 Enriching Stories with Regression Insights
  - 12.7.1 Predicted Value
  - 12.7.2 Prediction Explanations
- 12.8 Summary
13 The Data Science behind Classification Models and Regression Models
- 13.1 Fitting a Predictive Function
- 13.2 Prerequisites to Generating a Model
- 13.3 Evaluating the Model Performance
  - 13.3.1 Performance Indicators for Classification Models
  - 13.3.2 Performance Indicators for Regression Models
  - 13.3.3 Evaluating the Future Performance of the Model
- 13.4 End-to-End Automated Modeling
  - 13.4.1 Automated Data Encoding
  - 13.4.2 Data Partitions
  - 13.4.3 Gradient Boosting
  - 13.4.4 Model Performance Evaluation
- 13.5 Generating Predictive Insights
  - 13.5.1 Predicted Category
  - 13.5.2 Prediction Explanations
- 13.6 Summary
14 Conclusion
- 14.1 Lessons Learned
- 14.2 The Future of Predictive Scenarios in SAP Analytics Cloud
- 14.3 Your Next Steps
The Authors
Index

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SAP Analytics Cloud: Predictive Analytics

Name: SAP Analytics Cloud: Predictive Analytics
Brand: SAP
SKU: BOOK-SAPPRESS-EBOOK-2024-9781493224791
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SAP Analytics Cloud: Predictive Analytics

You'll learn about:

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SAP Analytics Cloud: Predictive Analytics

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