Inhaltsverzeichnis

Alle Kapitel aufklappen
Alle Kapitel zuklappen
Introduction
15
1 Big Data: More than Just Performance
23
1.1 What Does Big Data Mean?
25
1.1.1 In-Memory Databases as a Key Technology
28
1.1.2 What Else Do You Need For Big Data?
32
1.1.3 Is it All Just About Performance?
39
1.2 How Do Benefits from Big Data Come About?
41
1.2.1 Gaining New Insights, Making Better Decisions
43
1.2.2 Using Sophisticated Tools Properly
44
1.2.3 Realize, Decide, and—Above All—Act!
47
1.3 Where Are Benefits from Big Data to Be Found?
48
1.3.1 Real Time versus Batch
49
1.3.2 Improving Existing Business Processes
50
1.3.3 Implementing New Business Processes
54
1.4 How Benefits Turn Into Shareholder Value
58
1.4.1 The Concept of Shareholder Value
59
1.4.2 Value Drivers
61
1.4.3 How to Identify Value Drivers
65
1.5 Evaluating Business Cases
74
2 SAP HANA: Capabilities and Limitations
87
2.1 Big Data and SAP HANA
90
2.1.1 Big Data without or before SAP HANA
91
2.1.2 What Does SAP HANA Consist of?
103
2.1.3 The Difference between SAP HANA and Big Data
114
2.2 Implementation Scenarios for SAP HANA
143
2.2.1 Replication Scenarios
144
2.2.2 Integration Scenarios
155
2.2.3 Transformation Scenarios
161
2.3 Trends and Future Developments
164
2.3.1 Technology Trends
164
2.3.2 Ideas Are Becoming the Critical Success Factor
170
3 SAP HANA in Specific Industries and Business Processes
173
3.1 Creating Shareholder Value with SAP HANA
178
3.1.1 Implementing Big Data Solutions Faster and Cheaper
178
3.1.2 Real-Time Automation
180
3.2 SAP HANA in Different Industries
181
3.2.1 Working with SAP Solution Explorer
182
3.2.2 Industry-Specific Potential Benefits
187
3.2.3 Cross-Industry Potential Benefits
194
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
4.1.1 Planning, Modeling, and Forecasting
206
4.1.2 Business Planning
208
4.2 Scenario: Sales and Results Planning with a Multinational Tire Manufacturer
208
4.2.1 Forecasts and Models with Sales, Results, and Cost Planning
211
4.2.2 Exchange Rate Forecasts with RFT
214
4.2.3 Models for Production, Results, and Financial Planning with RFT
215
4.3 Planning Errors: Costs, Risks, and Opportunities
217
4.3.1 Problem: Risks with Forecasting and Modeling
217
4.3.2 Numerical Example
223
4.3.3 Conclusion: Keeping an Open Mind
227
4.4 Solution: Monitoring Forecasts and Models in Real Time
229
4.4.1 Related Value Maps in SAP Solution Explorer
229
4.4.2 Functional Requirements
232
4.4.3 Building Blocks of the Solution
234
4.4.4 Potential Benefits and Value Drivers
251
4.5 Implementation Scenario and Architecture with SAP HANA
255
4.5.1 Implementation Scenario and Framework Architecture
255
4.5.2 Data Architecture
259
5 Reducing Travel Costs and Travel Times
279
5.1 Time is Money
281
5.1.1 Costs for Travel-Related Services/Allowances
284
5.1.2 Travel-Related Opportunity Costs
286
5.1.3 Soft Travel Costs
287
5.2 Scenario: Travel Costs with an International Consulting Firm
288
5.2.1 Collecting Ideas
290
5.2.2 Strategic Decisions
291
5.3 One-Dimensional Optimization: Costs, Risks, and Opportunities
292
5.3.1 Problem: Politics and Organizational Psychology
292
5.3.2 Numerical Example
293
5.3.3 Conclusion: Juggling Numbers versus Reality
295
5.4 Solution: Induction Instead of Deduction
296
5.4.1 Related Value Maps in SAP Solution Explorer
297
5.4.2 Functional Requirements
299
5.4.3 Building Blocks of the Solution
300
5.4.4 Potential Benefits and Value Drivers
303
5.5 Implementation Scenario and Architecture with SAP HANA
307
5.5.1 Implementation Scenario and Framework Architecture
307
5.5.2 Data Architecture
311
6 Decision-Oriented Data Models
323
6.1 Data Governance: Rhetoric and Reality
325
6.1.1 What Is Data Governance?
326
6.1.2 Challenge: Data Volume, Speed, Agility
329
6.1.3 Gap between Data and Metadata
331
6.2 Scenario: Determining Trade Margins in Retail
333
6.3 Inconsistent Data Models: Costs, Risks, and Opportunities
334
6.3.1 Problem: Different Formulas
335
6.3.2 Problem: No Single Point of Truth
336
6.3.3 Numerical Example
337
6.3.4 Conclusion: Types of Data Model Inconsistencies
341
6.4 Solution: Generating Layers and Domains Automatically
343
6.4.1 Related Value Maps in SAP Solution Explorer
350
6.4.2 Functional Requirements
352
6.4.3 Building Blocks of the Solution
364
6.4.4 Potential Benefits and Value Drivers
368
6.5 Implementation Scenario and Framework Architecture with SAP HANA
372
6.5.1 Implementation Scenario and Framework Architecture
372
6.5.2 Data Architecture
377
7 Managing Customer Behavior
387
7.1 Understand, Predict, and Manage Customer Behavior
389
7.1.1 Example: Demand Curve
389
7.1.2 Better Models through More Parameters
391
7.1.3 Dynamic or (Inter) Temporal Customer Segmentation
392
7.2 Scenario: Setting Prices in Gas Station Kiosks
393
7.3 Static Customer Segmentation: Costs, Risks, and Opportunities
394
7.3.1 Problem: Walking on Thin Ice/Data
395
7.3.2 Numerical Example
396
7.3.3 Conclusion: Cause–Effect Relationships are Irrelevant
397
7.4 Solution: Dynamic-Empirical Algorithms
398
7.4.1 Related Value Maps in SAP Solution Explorer
399
7.4.2 Functional Requirements
401
7.4.3 Building Blocks of the Solution
405
7.4.4 Potential Benefits and Value Drivers
409
7.5 Implementation Scenario and Architecture with SAP HANA
410
7.5.1 Implementation Scenario and Framework Architecture
410
7.5.2 Data Architecture
416
8 Analyzing Sensor Data Automatically and Generating Metadata
425
8.1 Handling Sensor Data
430
8.1.1 Sensor Data are Heterogeneous
432
8.1.2 Interpreting Sensor Data within Their Context
438
8.2 Scenario: Cooperation among Car Manufacturer, Telephone Company, and Insurance Firm
440
8.3 Exchanging Data: Costs, Risks, and Opportunities
441
8.3.1 Problem: Partners Have Different Requirements
442
8.3.2 Numerical Example
444
8.3.3 Conclusion: Semantically Neutral Metadata
445
8.4 Solution: Extracting and Managing Sensor-Specific Metadata in a Big Data Environment
447
8.4.1 Related Value Maps in SAP Solution Explorer
448
8.4.2 Functional Requirements
452
8.4.3 Building Blocks of the Solution
461
8.4.4 Potential Benefits and Value Drivers
465
8.5 Implementation Scenario and Framework Architecture with SAP HANA
469
8.5.1 Implementation Scenario and Framework Architecture
470
8.5.2 Data Architecture
477
8.5.3 Applying the Concept to Other Case Studies
484
9 Health Management as a Service
487
9.1 Medical Sensor Data
489
9.1.1 Invasive and Noninvasive Sensors
490
9.1.2 Options for Data Transfer
491
9.1.3 Specific Challenges with Medical Applications
492
9.2 Scenario: Premium Services for the Elderly
494
9.3 Monitoring Health: Costs, Risks, and Opportunities
495
9.3.1 Problem: Legal and Financial Risks
496
9.3.2 Problem: Algorithms Challenging to Develop
496
9.3.3 Numerical Example
497
9.3.4 Conclusion: Mitigating Risks
498
9.4 Solution: Big Data-Based Early Warning Systems
500
9.4.1 Related Value Maps in SAP Solution Explorer
501
9.4.2 Functional Requirements
503
9.4.3 Building Blocks of the Solution
505
9.4.4 Potential Benefits and Value Drivers
506
9.5 Implementation Scenario and Framework Architecture with SAP HANA
508
9.5.1 Implementation Scenario and Framework Architecture
508
9.5.2 Data Architecture
510
9.5.3 Applying the Concept to Other Case Studies
513
10 Detecting Fraud Automatically
515
10.1 What Does Fraud Management Mean?
518
10.1.1 Corruption in Purchasing: Caffeine Withdrawal and Exploding Coffee Machines
519
10.1.2 Detecting Irregularities with Hindsight
520
10.1.3 Detecting Irregularities in the Act
520
10.1.4 Predicting Irregularities
521
10.2 Scenario: Theft in a Surface-Mining Operation
522
10.3 Traditional Investigation Methods: Costs, Risks, and Opportunities
522
10.3.1 Problem: Inexplicable Increase of Extraction Costs
522
10.3.2 Break Room versus Heavy Industry
524
10.3.3 Numerical Example
525
10.3.4 Conclusion: Using New Technologies
526
10.4 Solution: Flexible Fraud Management Using a High-Performance Appliance
526
10.4.1 Related Value Maps in SAP Solution Explorer
526
10.4.2 Functional Requirements
528
10.4.3 Building Blocks of the Solution
531
10.4.4 Potential Benefits and Value Drivers
536
10.5 Implementation Scenario and Data Architecture with SAP HANA
538
10.5.1 Implementation Scenario and Framework Architecture
539
10.5.2 Data Architecture
544
11 Automating Service-Level Management
557
11.1 IT-Related Services as a Commodity
560
11.1.1 IT Services and Business Process Outsourcing
561
11.1.2 Customers and IT Service Providers Speak Different Languages
562
11.1.3 IT Systems are Complicated and Complex
563
11.2 Scenario: Sizing an IT System
564
11.3 Sizing: Costs, Risks, and Opportunities
566
11.3.1 Problem: Complexity Makes Modeling Difficult
568
11.3.2 SAP Solution Manager as a Sensor
569
11.3.3 Business Considerations
571
11.3.4 Conclusion: Better Approximation to Reality, More Flexibility
572
11.4 Solution: Data Linearization before Analysis
572
11.4.1 Related Value Maps in SAP Solution Explorer
573
11.4.2 Functional Requirements
574
11.4.3 Building Blocks of the Solution
576
11.4.4 Potential Benefits and Value Drivers
578
11.5 Implementation Scenario and Architecture with SAP HANA
580
11.5.1 Implementation Scenario and Framework Architecture
580
11.5.2 Data Architecture
582
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
12.2.1 Implementation Scenarios
590
12.2.2 General Recommendations for Data Architecture
595
12.3 Outlook: Fantasy, Creativity, Mindfulness, and Control over Data
600
The Authors
603
Index
605