Business Cases for SAP HANA

1. Auflage 2014
ISBN: 978-1-4932-1036-7
Verlag: Rheinwerk Verlag

Buch, Englisch, 616 Seiten, Format (B × H): 152 mm x 229 mm

Reihe: SAP PRESS: englisch

Everybody knows that SAP HANA makes things go faster—but if you want to implement it for your company, you need to promise more than just speed. How will it improve business processes? How will it save money? How will it save time? In this book, find out what SAP HANA can really do. With case studies and high-level implementation plans from a multitude of industries, you'll learn how to build a convincing business case for any scenario. Go ahead, make your case.

Highlights:

- BRequirements of modern enterprise

- applications

- Scenarios for big data and SAP HANA

- Flexible planning models

- Travel cost reduction

- Customer behavior analysis

- Flexible and consistent data models

- Service-level management automation

- Sensor data evaluation

- Fraud management

- Health services

Galileo Press heißt jetzt Rheinwerk Verlag.

Autoren/Hrsg.

Mattern, Michael

Croft, Ray

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware SAP

Inhaltsverzeichnis

Introduction. 15

1. Big Data: More than Just Performance. 23

1.1. What Does Big Data Mean?. 25

1.2. How Do Benefits from Big Data Come About?. 41

1.3. Where Are Benefits from Big Data to Be Found?. 48

1.4. How Benefits Turn Into Shareholder Value. 58

1.5. Evaluating Business Cases. 74

2. SAP HANA: Capabilities and Limitations. 87

2.1. Big Data and SAP HANA. 90

2.2. Implementation Scenarios for SAP HANA. 143

2.3. Trends and Future Developments. 164

3. SAP HANA in Specific Industries and Business Processes. 173

3.1. Creating Shareholder Value with SAP HANA. 178

3.2. SAP HANA in Different Industries. 181

3.3. SAP HANA in (SAP’s) Business Processes. 196

3.4. The Case Studies in this Book. 200

4. Flexible Planning. 203

4.1. What Does “Planning” Mean?. 205

4.2. Scenario: Sales and Results Planning with a Multinational Tire Manufacturer. 208

4.3. Planning Errors: Costs, Risks, and Opportunities. 217

4.4. Solution: Monitoring Forecasts and Models in Real Time. 229

4.5. Implementation Scenario and Architecture with SAP HANA. 255

5. Reducing Travel Costs and Travel Times. 279

5.1. Time is Money. 281

5.2. Scenario: Travel Costs with an International Consulting Firm. 288

5.3. One-Dimensional Optimization: Costs, Risks, and Opportunities. 292

5.4. Solution: Induction Instead of Deduction. 296

5.5. Implementation Scenario and Architecture with SAP HANA. 307

6. Decision-Oriented Data Models. 323

6.1. Data Governance: Rhetoric and Reality. 325

6.2. Scenario: Determining Trade Margins in Retail. 333

6.3. Inconsistent Data Models: Costs, Risks, and Opportunities. 334

6.4. Solution: Generating Layers and Domains Automatically. 343

6.5. Implementation Scenario and Framework Architecture with SAP HANA. 372

7. Managing Customer Behavior. 387

7.1. Understand, Predict, and Manage Customer Behavior. 389

7.2. Scenario: Setting Prices in Gas Station Kiosks. 393

7.3. Static Customer Segmentation: Costs, Risks, and Opportunities. 394

7.4. Solution: Dynamic-Empirical Algorithms. 398

7.5. Implementation Scenario and Architecture with SAP HANA. 410

8. Analyzing Sensor Data Automatically and Generating Metadata. 425

8.1. Handling Sensor Data. 430

8.2. Scenario: Cooperation among Car Manufacturer, Telephone Company, and Insurance Firm. 440

8.3. Exchanging Data: Costs, Risks, and Opportunities. 441

8.4. Solution: Extracting and Managing Sensor-Specific Metadata in a Big Data Environment. 447

8.5. Implementation Scenario and Framework Architecture with SAP HANA. 469

9. Health Management as a Service. 487

9.1. Medical Sensor Data. 489

9.2. Scenario: Premium Services for the Elderly. 494

9.3. Monitoring Health: Costs, Risks, and Opportunities. 495

9.4. Solution: Big Data-Based Early Warning Systems. 500

9.5. Implementation Scenario and Framework Architecture with SAP HANA. 508

10. Detecting Fraud Automatically. 515

10.1. What Does Fraud Management Mean?. 518

10.2. Scenario: Theft in a Surface-Mining Operation. 522

10.3. Traditional Investigation Methods: Costs, Risks, and Opportunities. 522

10.4. Solution: Flexible Fraud Management Using a High-Performance Appliance. 526

10.5. Implementation Scenario and Data Architecture with SAP HANA. 538

11. Automating Service-Level Management. 557

11.1. IT-Related Services as a Commodity. 560

11.2. Scenario: Sizing an IT System. 564

11.3. Sizing: Costs, Risks, and Opportunities. 566

11.4. Solution: Data Linearization before Analysis. 572

11.5. Implementation Scenario and Architecture with SAP HANA. 580

11.6. Conclusion. 586



12. Discovering Potentials, Designing Data Architectures. 587

12.1. Speed Is Nothing but a Means to an End. 588

12.2. SAP HANA Implementation and Data Architectures. 590

12.3. Outlook: Fantasy, Creativity, Mindfulness, and Control over Data. 600

. The Authors. 603

. Index. 605