Preface

15

Who This Book Is For

16

How This Book Is Organized

16

Chapter 1

17

Chapter 2

17

Chapter 3

18

Chapter 4

18

Chapter 5

18

Chapter 6

19

Chapter 7

19

Chapter 8

20

Chapter 9

20

Chapter 10

20

Acknowledgments

21

Conclusion

22

1 Introduction

25

1.1 Build, Run, and Optimize a Business

25

1.2 Challenges for Executives

30

1.3 Characteristics of a Successful, Data-Focused Organization

31

1.4 Data Strategy and Information Management Maturity Models

34

1.5 Data Integration

35

1.5.1 General Components of Data Integration

36

1.5.2 Data Integration and Evolution

41

1.6 Data Tiers in SAP

45

1.6.1 Transactional System

46

1.6.2 Operational Data Store

46

1.6.3 Enterprise Data Warehouse

46

1.6.4 Enterprise Analytics

47

1.6.5 Data Quality

48

1.6.6 Data Archiving

50

1.6.7 Security

52

1.7 SAP Business Technology Platform

52

1.8 SAP’s Data and Analytics Portfolio

56

1.8.1 SAP Master Data Governance

57

1.8.2 SAP S/4HANA Migration Cockpit

57

1.8.3 SAP Business Data Cloud

59

1.8.4 SAP HANA Cloud

61

1.8.5 SAP BW/4HANA

62

1.8.6 SAP Analytics Cloud

63

1.9 Enterprise Value and Data Fabric

63

1.9.1 Approaches to Unifying Data

64

1.9.2 SAP’s Component Map

67

1.9.3 Non-SAP Component Map

69

1.10 Bringing Them Together with SAP

71

1.10.1 Run the Business

71

1.10.2 Evolve the Business

72

1.11 Summary

74

2 Data Strategy

77

2.1 Overview

78

2.2 Defining a Data Strategy

79

2.2.1 Developing a Data Strategy

80

2.2.2 Importance of a Data Strategy

91

2.2.3 Characteristics of an Effective Data Strategy

96

2.2.4 Applying the Clean Core Philosophy Within Data Management

104

2.3 Data and Information Management Maturity Models

104

2.3.1 Methodology

105

2.3.2 Catalog of Models

112

2.3.3 Next Steps

118

2.4 SAP’s Advisory Methodologies

119

2.4.1 SAP Application Extension Methodology

120

2.4.2 SAP Integration Solution Advisory Methodology

121

2.4.3 SAP Data and Analytics Advisory Methodology

123

2.5 Summary

124

3 Overview of SAP Business Technology Platform

127

3.1 Fundamentals

128

3.1.1 Global Account

130

3.1.2 Directories

130

3.1.3 Subaccounts

131

3.1.4 Regions and Providers

131

3.1.5 Environments

133

3.1.6 Entitlements and Quotas

135

3.1.7 Services

138

3.2 Data and Analytics

139

3.2.1 Data Products

139

3.2.2 SAP Business Data Cloud Cockpit

140

3.2.3 SAP Datasphere

140

3.2.4 SAP Analytics Cloud

140

3.2.5 SAP Databricks

141

3.3 Application Development

141

3.3.1 SAP Build Apps

143

3.3.2 SAP Build Code

143

3.3.3 SAP Build Process Automation

145

3.3.4 SAP Build Work Zone

145

3.3.5 Joule Studio

146

3.4 Integration

148

3.4.1 SAP Cloud Integration

149

3.4.2 Migration Assessment

150

3.4.3 Integration Technology Guidelines

151

3.4.4 Extend Connectivity

153

3.4.5 SAP API Management

153

3.4.6 Trading Partner Management

155

3.4.7 SAP Integration Suite, Advanced Event Mesh

156

3.4.8 Access Data in Classic SAP Business Suite with OData Provisioning

157

3.5 Automation

158

3.5.1 SAP Build Process Automation

158

3.5.2 SAP Automation Pilot

159

3.6 Artificial Intelligence

160

3.6.1 SAP Business AI

160

3.6.2 SAP AI Core

161

3.6.3 SAP AI Launchpad

161

3.6.4 SAP AI Services

161

3.7 Use Cases

162

3.7.1 Goals

162

3.7.2 Solution

163

3.7.3 Overall Architecture and Integration

170

3.8 Summary

171

4 Data Quality

173

4.1 Introduction to Data Quality

174

4.1.1 Data Quality Terminology

175

4.1.2 Roles and Responsibilities in Data Quality

179

4.1.3 Categories of Data

187

4.1.4 Type of Data Quality Defects

190

4.1.5 Causes of Data Quality Defects

194

4.1.6 Data Quality Approach

198

4.1.7 Benefits Realized from Profiling and Cleansing

203

4.2 Data Profiling Approach

203

4.2.1 Introduction to Data Profiling

204

4.2.2 Data Profiling Fundamentals

208

4.2.3 Identify Data Risks and Issues with a Data Quality Assessment

209

4.2.4 When to Perform a Data Quality Assessment

211

4.2.5 Data Quality Assessment Process

212

4.2.6 Data Profiling Best Practices

215

4.2.7 Other Considerations

216

4.3 Data Cleansing Approach

219

4.3.1 Introduction to Data Cleansing

221

4.3.2 Data Cleansing Fundamentals

222

4.3.3 Correcting Data Quality Defects with Data Cleansing

234

4.3.4 Determining How Much Cleansing Is Enough

236

4.3.5 Data Cleansing Methodology

238

4.3.6 Data Cleansing Best Practices

248

4.3.7 Other Considerations

249

4.4 User Training and Enablement

250

4.4.1 SAP Learning Journeys

251

4.4.2 Training Courses

252

4.4.3 Other Learning Resources

253

4.5 Summary

254

5 Master Data Management

255

5.1 Overview

256

5.2 Key Features

262

5.2.1 Consolidation

263

5.2.2 Mass Processing

268

5.2.3 Central Governance

268

5.2.4 Data Quality Management

272

5.2.5 Process Analytics

276

5.2.6 Federated Master Data Governance

279

5.3 Architecture and Deployment Models

282

5.3.1 Choosing a Deployment Model

283

5.3.2 SAP Master Data Governance on SAP S/4HANA Cloud, Private Edition

286

5.3.3 SAP Master Data Governance, Cloud Edition

290

5.3.4 SAP S/4HANA Cloud Public Edition, Master Data Governance

290

5.4 Master Data Integration

292

5.5 Enabling a Business Data Fabric

293

5.6 User Training and Enablement

294

5.7 Summary

295

6 Data Storage with SAP HANA Cloud

297

6.1 Key Features

298

6.1.1 Data Storage Capabilities

298

6.1.2 Data Lake Capabilities

302

6.1.3 Real-Time Data Access

303

6.1.4 Native Multi-Model

303

6.1.5 Built-In Machine Learning, Predictive Analytics, and Search

310

6.1.6 Security

312

6.2 Use Cases

315

6.2.1 Integration with Business Intelligence Tools

315

6.2.2 Predictive Analytics

316

6.2.3 Transition to Cloud

316

6.3 User Training and Enablement

317

6.3.1 Training Courses

317

6.3.2 SAP Learning Journeys

318

6.3.3 Learning Resources

318

6.4 Summary

320

7 Data Fabric with SAP Business Data Cloud

321

7.1 Architecture

322

7.1.1 Source Systems

323

7.1.2 Foundation Services

323

7.1.3 Data Products

323

7.1.4 Delta Share Protocol

326

7.1.5 Onboarding

327

7.2 SAP Business Data Cloud Cockpit

329

7.3 SAP Datasphere

331

7.3.1 Architecture

331

7.3.2 Space and Connection Management

334

7.3.3 Modeling: Data Builder

338

7.3.4 Modeling: Business Builder

341

7.3.5 Data Integration Monitor

342

7.3.6 Catalog and Marketplace

343

7.4 SAP Business Warehouse and SAP BW/4HANA

345

7.5 SAP Databricks

347

7.6 SAP Analytics Cloud

350

7.7 Business Use Cases

353

7.7.1 Solution Overview

353

7.7.2 Data Integration and Modeling

354

7.7.3 C-Level Dashboards

355

7.7.4 Machine Learning Applications

356

7.7.5 Governance and Scalability

357

7.7.6 Conclusion

358

7.8 Summary

359

8 Data-Driven Decision-Making with SAP Analytics Cloud

361

8.1 Introduction

362

8.1.1 Data and Analytics Strategy

363

8.1.2 Business Cases for SAP Analytics Cloud

363

8.1.3 SAP Analytics Cloud Value Proposition

364

8.2 SAP Analytics Cloud Capabilities

366

8.2.1 Analytical Capabilities

366

8.2.2 Planning Capabilities

376

8.2.3 AI Capabilities

383

8.2.4 Integration with Microsoft Office

390

8.3 Decision-Making with SAP Analytics Cloud

391

8.3.1 Operational

391

8.3.2 Tactical

392

8.3.3 Strategic

394

8.3.4 On-the-Go

395

8.4 Sharing, Collaborating, and Exporting

396

8.4.1 Sharing and Publishing Content

396

8.4.2 Commenting and Discussing

396

8.4.3 Exporting Content

397

8.5 Business Content and Accelerators

398

8.5.1 Packages, Content, and Templates

399

8.5.2 Getting Business Content

402

8.5.3 Business Content Usage

403

8.6 User Training and Enablement

403

8.6.1 Training Courses

403

8.6.2 SAP Learning Journeys

404

8.6.3 Learning Resources

404

8.7 Best Practices

405

8.8 Summary

407

9 Artificial Intelligence for Data and Analytics

409

9.1 AI Concepts

410

9.1.1 AI Models

411

9.1.2 AI Workflow and Interface

419

9.1.3 Additional AI Considerations

425

9.2 AI in SAP Solutions

431

9.2.1 Joule

432

9.2.2 Embedded AI

433

9.2.3 AI Foundation

435

9.3 Integration with SAP Products

440

9.4 SAP Joule Assistance During Implementation

442

9.5 User Training and Enablement

445

9.5.1 SAP Discovery Center

445

9.5.2 SAP Tutorials

448

9.6 Summary

449

10 Business and Integration Scenarios

451

10.1 Sustainable Enterprise

452

10.1.1 Significance of Addressing the Challenge

452

10.1.2 Overview of Potential Solutions

452

10.1.3 Components Contributing to the Solution

452

10.2 Ethical Supply Chain

454

10.2.1 Significance of Addressing the Challenge

454

10.2.2 Overview of Potential Solutions

454

10.2.3 Components Contributing to the Solution

454

10.3 Managing Private and Secure Data

455

10.3.1 Significance of Addressing the Challenge

455

10.3.2 Overview of Potential Solutions

456

10.3.3 Components Contributing to the Solution

456

10.4 Fraud Detection

457

10.4.1 Significance of Addressing the Challenge

458

10.4.2 Overview of Potential Solutions

458

10.4.3 Components Contributing to the Solution

458

10.5 Predictive Maintenance

460

10.5.1 Significance of Addressing the Challenge

460

10.5.2 Overview of Potential Solutions

461

10.5.3 Components Contributing to the Solution

461

10.6 Data Quality Firewall

462

10.6.1 Significance of Addressing the Challenge

463

10.6.2 Overview of Potential Solutions

463

10.6.3 Components Contributing to the Solution

464

10.7 Guided End-User Priorities and Application Navigation

465

10.7.1 Significance of Addressing the Challenge

465

10.7.2 Overview of Potential Solutions

466

10.7.3 Components Contributing to the Solution

467

10.8 Custom Operating Guides and User Documentation

468

10.8.1 Significance of Addressing the Challenge

468

10.8.2 Overview of Potential Solutions

469

10.8.3 Components Contributing to the Solution

469

10.9 Summary

471