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Wednesday, February 15, 2012

Data Warehousing and Data Mining for Telecommunications






Rob Mattison
Artech House
Boston • London

Contents
Foreword xiii
Preface xvii
Chapter 1
Everything’s up to date in
Kansas City 1
1.1 The current industry composition 3
1.2 Why is telecommunications so
BIG? 4
1.3 Telecommunications: the major
driving economic force of the 21st
century 5
1.4 Knowledge management enablement—
the biggest factor of all 6
1.5 The ultimate environment 7
1.5.1 Failed excursions into the
new frontiers 7
1.6 Future directions 8
1.7 Telecommunications and
technological innovation 9
1.7.1 Peg counts 9
1.7.2 Business drives technological
innovation 9
1.8 The three strategic options 10
1.9 Customer intimacy—from “network
is king” to “customer is king” 10
1.9.1 Marketing as the driving force 11
1.10 Operational efficiency—being the
low-cost provider of choice 11
1.11 Technical proficiency—being the
best at what you do 12
1.12 Conclusion 12
Chapter 2
Why warehousing and
how to get started 13
2.1 Background of data warehousing 14
2.1.1 The history of the data
warehousing phenomenon 14
2.1.2 Data warehousing—in a
nutshell 16
2.1.3 What is a data warehouse? 17
2.2 Data mining 18
2.2.1 Why should one seriously consider
using these approaches? 20
2.3 Why are these approaches so
exceptionally valuable to
telecommunications firms? 20
2.3.1 Data intensity 20
2.3.2 Analysis dependency 21
2.3.3 Competitive climate 21
2.3.4 Technological change at a very
high rate 21
2.3.5 Historical precedent 22
2.4 Organizing the process 22
2.4.1 An inventory of the existing
computer systems and other
technological infrastructure 22
2.4.2 A roadmap and an approach for
how to deploy data warehouses
in general 23
2.4.3 A roadmap for understanding how
to diagnose and develop a plan for
identifying the best things to put
into the warehouse and which data
mining tools to use 23
Chapter 3
The knowledge management view of
business and warehousing 25
3.1 The knowledge management
revolution 26
3.1.1 Knowledge management
principles 27
3.1.2 The organizational footprint and
what it tells us about knowledge
transformation processes 29
3.2 Efficiency optimization—optimize
the silo or optimize the whole 32
3.2.1 Which type of warehouse is better,
or which is the right one? 34
3.2.2 The warehouse alternative 37
3.2.3 A third alternative 37
3.3 The corporate global warehouse
model 38
3.3.1 Developing a truly usable global
architecture model 40
3.3.2 An alternative foundation:
the value chain 42
3.3.3 The key to value chain
delivery 45
3.4 Overall strategy for development
(one piece at a time, fitting into the
overall architecture) 45
3.4.1 The growing warehouse
example 47
3.4.2 Ownership of knowledge
issues 49
Chapter 4
The telecommunications
value chain 51
4.1 The knowledge roadmap
solution 52
4.2 Steps in the process of deriving a
business’ value chain 52
4.3 Telecommunications functions
and systems 53
4.3.1 Creation (new product develop-
ment and exploitation) 54
4.3.2 Acquisition (acquiring the “right”
to do business) 54
4.3.3 Network infrastructure planning
and development (creating the
“phone system”) 55
4.3.4 Network infrastructure
maintenance (maintaining the
“phone system”) 56
4.3.5 Provisioning (setting up customer
services) 57
4.3.6 Activation (activating customer
services) 57
4.3.7 Service order processing 58
4.3.8 Billing (tracking service and
invoicing the customer) 58
4.3.9 Marketing (identifying prospects/
channels, advertising) 59
4.3.10 Customer service (keeping the
customer happy) 59
4.3.11 Sales (establishing and maintaining
customer relationships) 61
4.3.12 Finance and accounting 61
4.3.13 Credit management 61
4.3.14 Operations (network and
business) 62
4.3.15 A comprehensive value chain 62
4.4 Organizational structure and the
value chain 63
4.4.1 Typical organizational structure:
medium-sized cellular firm 64
4.4.2 Typical organizational structure:
large telecommunications
firms 66
4.5 Allocating the business units to the
value chain and the knowledge
management process 66
4.5.1 Aligning the value chain and
the organization—large
megacorporation 68
4.5.2 Aligning the value chain with the
information systems 71
4.5.3 Kingpin systems: the beginning of
computer systems alignment 72
4.5.4 Alignment problems and their
symptoms 74
4.5.5 Data warehousing as an
alternative 76
4.5.6 Data warehousing as a migration
path 77
4.5.7 The fully aligned model—
a summary 77
Chapter 5
Building the warehouse—
one step at a time 79
5.1 Challenges to infrastructure
design 80
5.2 The functional components of a
warehouse environment 84
5.2.1 Acquisition 85
5.2.2 Storage 87
5.2.3 Access 88
5.2.4 The operational
infrastructure 89
5.2.5 The physical infrastructure 89
5.3 The step-by-step, cost-justified
approach 89
5.3.1 What is a value proposition? 90
5.3.2 Gathering value propositions 90
5.4 How do you build a warehouse? 92
Chapter 6
Value propositions in
telecommunications 95
6.1 Mining tools and value delivery 96
6.1.1 Operational monitoring and
control 96
6.1.2 Discovery and exploration 97
6.2 Value propositions by functional
area 98
6.2.1 Marketing value propositions
(historical/cross-silo/
discovery) 99
6.2.2 Credit value propositions 99
6.2.3 Customer service value
propositions—(real-time and
historical/cross-silo/
operational monitoring) 100
6.2.4 Sales value propositions 101
6.2.5 Network planning value
propositions 101
6.2.6 Network maintenance value
propositions 103
6.2.7 Creation 103
6.2.8 Activation and provisioning and
service order processing 104
6.2.9 Billing (historical/single-silo/
discovery and monitoring) 104
6.2.10 Operations 104
6.3 Conclusions 105
6.3.1 Knowledge management
approach 105
Chapter 7
Simple sales analysis:
an introduction to operational moni-
toring using Microsoft Query 107
7.1 Operational efficiency—an
overview 109
7.2 Sales monitoring and control 110
7.3 A universal problem 111
7.4 Using Microsoft Query and Excel to
do sales tracking 111
7.4.1 The sales database 112
7.5 Managing more complicated
needs 115
7.6 Alternative methods of
accessing data 115
Chapter 8
Sales and product management:
advanced operational monitoring
using COGNOS PowerPlay 117
8.1 Monitoring complex business
organizations 118
8.1.1 Determining the different levels at
which to report 119
8.1.2 Preparing the data for use 121
8.2 Exploring sales and product
performance 121
8.3 Additional PowerPlay features 124
8.3.1 Alerts 125
8.3.2 Schedulers 126
8.4 Summary 127
Chapter 9
Customer intimacy:
an introduction using SPSS 129
9.1 An introduction to analytical
mining 130
9.2 Statistical analysis—options and
objectives 131
9.3 Descriptive approaches 133
9.4 Inferential approaches—regression
analysis 137
9.5 Conclusions on statistical
analysis 140
Chapter 10
Predicting customer behavior:
an introduction to neural
networks 143
10.1 Unraveling complex
situations 144
10.2 How can a neural network help
with marketing? 145
10.3 Step-by-step use of a neural
network 145
10.3.1 What does the training report tell
us? 146
10.3.2 Creating and interpreting the
gains table 147
10.3.3 Analyzing the gains chart 149
10.3.4 Making marketing programs as
profitable as possible 151
10.4 Applying the model to
prospects 152
10.5 Conclusion on neural
networks 152
Chapter 11
Engineering and competitive analysis
support: an introduction to geographi-
cal systems and MapInfo 155
11.1 An introduction to MapInfo
Professional 156
11.1.1 MapInfo telecommunications
offerings 156
11.2 Using geographical information to
solve telecommunications
problems 158
11.3 Cellsite analysis with MapInfo
Professional 159
11.4 Market analysis capabilities 162
11.5 Viewing a local market in greater
detail 164
11.6 Accessibility to fiber analysis 165
11.7 Working with the underlying
database 167
11.8 Conclusion 168
Appendix A
Real world warehousing: France
Telecom and STATlab tools 169
Appendix B
The business case
for business intelligence 211
Appendix C
SPSS 239
Appendix D
The DecisionWORKS suite
from Advanced Software
Applications 245
Glossary 251
Selected bibliography 257
About the author 261
Index 263

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