Data Warehousing and Data Mining for Telecommunications

282 Pages·1997·2.147 MB·English

by  Rob Mattison

Data Warehousing and
Data Mining for
Telecommunications
Chapter7 Screenshotsreprintedwithpermissionfrom MicrosoftCorporation,One Mi-
crosoftWay,Redmond, WA98052-6399
Chapter8 Screenshotsof PowerPlayreprintedwithpermissionfrom Cognos Corpora-
tion,OneBurlington BusinessCenter,67So.Bedford Street,Suite200W,Burlington,
MA01803-5164,Tel:[phone],http://www.cognos.com
Chapter9 Screenshotsreprintedwithpermissionfrom SPSS Inc.,444N. Michigan
Avenue,Chicago,IL60611-3962,Tel:[phone],Fax:[phone],
http://www.spss.com
Chapter10 Screenshotsof ModelMAX reprintedwithpermission from AdvancedSoft-
wareApplications. ModelMAX isaregisteredtrademarkof AdvancedSoftware Applica-
tions,333BaldwinRoad, Pittsburgh,PA15205,Tel: [phone],Fax:
412-429-0709,e-mail:[email protected]
Chapter11 MapInfo Professional(cid:153)screenshotsprovidedbyMapInfo Corporation.
©1985–1997MapInfoCorporation. Allrightsreserved.MapMarkerisaregisteredtrade-
markandMapInfoProfessional andStreetWorksaretrademarksofMapInfo Corpora-
tion,OneGlobalView,Troy,New York12180-8399,Tel: [phone],Fax:
518-285-6060,http://www.mapinfo.com
MapInfoCorporationmakesnoguaranteeor warrantywithregardtotheaccuracyofin-
formation suppliedhereinandacceptsno liabilityfor lossor damageincurredasaresult
of any relianceontheinformation.
AppendixA Screenshotsreprintedwithpermission from CNET-FranceTelecom and
SLP InfoWare Inc.
AppendixB Screenshotsreprintedwithpermissionfrom SeagateSoftware,
1095WestPenderStreet,Vancouver,BC, Canada V6E2M6,Tel:[phone],
Fax:[phone],http://www.seagatesoftware.com. SeagateandSeagateTechnology
areregisteredtrademarksof SeagateTechnology, Inc. SeagateSoftware isatrademarkof
SeagateTechnology, Inc.SeagateSoftware Information ManagementGroup, Seagate
CrystalReports,SeagateCrystalInfo,andSeagateHolosaretrademarksor registered
trademarksof SeagateTechnology, Inc.,or one ofitssubsidiaries.Allothertrademarks
and registeredtrademarksarepropertyof theirrespectiveowners.
For a completelistingof theArtechHouseComputerScience library,
turn tothebackof thisbook.
Data Warehousing and
Data Mining for
Telecommunications
Rob Mattison
Artech House
Boston • London
Libraryof CongressCataloging-in-Publication Data
Mattison, Rob.
Data warehousinganddataminingfor telecommunications/Rob Mattison.
p. cm.—(Artech ComputerScienceLibrary)
Includesbibliographicalreferencesandindex.
ISBN0-89006-952-2(alk. paper)
1. Telecommunications—Management. 2.DatabaseManagement. 3.Data
mining I.Title. II.Series
HE7661.M38 1997
384’.0285’574—dc21 97-22863
CIP
British LibraryCataloguingin PublicationData
Mattison, Rob M.
Data warehousingand dataminingfor telecommunications—(Artech Computer
ScienceLibrary)
1.Decision supportsystems 2.Telecommunications 3.Digitalcommunications
I.Title
384’.0285
ISBN0-89006-952-2
CoverandtextdesignbyDarrell Judd.Illustrationsby BrigitteKilger-Mattison.
CoverimagesfromSeagate SoftwareandSLP InfoWare.
© 1997ARTECH HOUSE,INC.
685 Canton Street
Norwood, MA02062
Allrightsreserved. Printedandbound intheUnitedStatesof America.Nopartof this
book maybereproducedor utilizedinany form or byany means,electronic or mechani-
cal,includingphotocopying,recording,orbyanyinformation storageand retrievalsys-
tem,withoutpermission inwriting from thepublisher.
Alltermsmentionedinthisbook thatareknown to betrademarksor servicemarkshave
beenappropriatelycapitalized.ArtechHouse cannot attestto theaccuracyof thisinforma-
tion.Useof aterminthisbook shouldnotberegardedasaffecting thevalidityof any
trademarkor servicemark.
InternationalStandardBookNumber: 0-89006-952-2
LibraryofCongressCatalog CardNumber:97-22863
10987654321
I would like to dedicate thisbookto my darlinggrandchildren,
JonathanYesulis,RaquelKuykendall,andAnthonyandNicoleCirrincione,
and to allof theotherchildrenwhowillbe leftto pick upthepieces
afterwemoveon.
Contents
Foreword xiii 1.6 Future directions 8
1.7 Telecommunicationsand
technologicalinnovation 9
Preface xvii
1.7.1 Peg counts 9
1.7.2 Business drives technological
innovation 9
Chapter 1
1.8 The threestrategicoptions 10
Everything’supto date in
1.9 Customerintimacy—from“network
KansasCity 1
isking”to “customer isking” 10
1.1 The currentindustrycomposition 3 1.9.1 Marketingas the drivingforce 11
1.2 Whyistelecommunicationsso 1.10 Operational efficiency—beingthe
BIG? 4 low-costproviderof choice 11
1.3 Telecommunications:themajor 1.11 Technicalproficiency—beingthe
drivingeconomic forceof the21st bestatwhatyou do 12
century 5
1.12 Conclusion 12
1.4 Knowledge managementenablement—
thebiggestfactorof all 6
Chapter 2
1.5 The ultimateenvironment 7
1.5.1 Failedexcursionsintothe Whywarehousingand
new frontiers 7 how to get started 13
vii
viii Data WarehousingandDataMiningforTelecommunications
2.1 Background of datawarehousing 14 3.1.1 Knowledgemanagement
2.1.1 Thehistoryofthe data principles 27
warehousing phenomenon 14 3.1.2 Theorganizational footprintand
2.1.2 Data warehousing—in a whatittells usaboutknowledge
nutshell 16 transformationprocesses 29
2.1.3 Whatis a datawarehouse? 17
3.2 Efficiencyoptimization—optimize
2.2 Datamining 18 thesilo or optimizethewhole 32
2.2.1 Whyshould oneseriouslyconsider 3.2.1 Whichtypeofwarehouseisbetter,
using theseapproaches? 20 or which is the right one? 34
3.2.2 Thewarehouse alternative 37
2.3 Whyaretheseapproachesso
3.2.3 A third alternative 37
exceptionallyvaluableto
telecommunicationsfirms? 20 3.3 The corporate globalwarehouse
2.3.1 Data intensity 20 model 38
2.3.2 Analysis dependency 21 3.3.1 Developinga trulyusable global
2.3.3 Competitive climate 21 architecturemodel 40
2.3.4 Technologicalchange at a very 3.3.2 An alternative foundation:
highrate 21 the valuechain 42
2.3.5 Historicalprecedent 22 3.3.3 Thekeytovaluechain
delivery 45
2.4 Organizingtheprocess 22
2.4.1 Aninventoryofthe existing 3.4 Overallstrategyfor development
computer systemsandother (one pieceatatime,fitting intothe
technologicalinfrastructure 22 overallarchitecture) 45
2.4.2 A roadmapand an approachfor 3.4.1 Thegrowing warehouse
how todeploy data warehouses example 47
in general 23 3.4.2 Ownership ofknowledge
2.4.3 A roadmapforunderstandinghow issues 49
todiagnose anddevelop a plan for
identifyingthe bestthings toput
Chapter 4
intothe warehouse and which data
mining toolstouse 23 The telecommunications
valuechain 51
4.1 The knowledgeroadmap
Chapter 3
solution 52
Theknowledge management viewof
4.2 Stepsintheprocessof deriving a
businessandwarehousing 25
business’valuechain 52
3.1 The knowledgemanagement 4.3 Telecommunicationsfunctions
revolution 26 and systems 53
Contents ix
4.3.1 Creation (newproduct develop- 4.5.1 Aligningthevaluechain and
mentand exploitation) 54 the organization—large
4.3.2 Acquisition(acquiringthe “right” megacorporation 68
todobusiness) 54 4.5.2 Aligningthevaluechain with the
4.3.3 Networkinfrastructureplanning informationsystems 71
and development(creatingthe 4.5.3 Kingpin systems:the beginningof
“phone system”) 55 computersystemsalignment 72
4.3.4 Networkinfrastructure 4.5.4 Alignment problemsand their
maintenance(maintaining the symptoms 74
“phone system”) 56 4.5.5 Datawarehousing as an
4.3.5 Provisioning (settingup customer alternative 76
services) 57 4.5.6 Datawarehousing as a migration
4.3.6 Activation(activatingcustomer path 77
services) 57 4.5.7 Thefully aligned model—
4.3.7 Service orderprocessing 58 a summary 77
4.3.8 Billing (tracking serviceand
invoicing thecustomer) 58
Chapter 5
4.3.9 Marketing(identifyingprospects/
channels,advertising) 59 Building the warehouse—
4.3.10 Customerservice(keepingthe one step ata time 79
customer happy) 59 5.1 Challengestoinfrastructure
4.3.11 Sales (establishingandmaintaining design 80
customer relationships) 61
5.2 The functional componentsof a
4.3.12 Financeand accounting 61 warehouse environment 84
4.3.13 Credit management 61
5.2.1 Acquisition 85
4.3.14 Operations (networkand
5.2.2 Storage 87
business) 62
5.2.3 Access 88
4.3.15 A comprehensivevaluechain 62
5.2.4 Theoperational
4.4 Organizational structureand the infrastructure 89
valuechain 63 5.2.5 Thephysical infrastructure 89
4.4.1 Typical organizational structure:
5.3 The step-by-step, cost-justified
medium-sized cellular firm 64 approach 89
4.4.2 Typical organizational structure:
5.3.1 Whatis a valueproposition? 90
large telecommunications
5.3.2 Gatheringvalue propositions 90
firms 66
5.4 Howdoyoubuilda warehouse? 92
4.5 Allocatingthebusinessunitsto the
valuechainand theknowledge
managementprocess 66
x DataWarehousingandDataMiningforTelecommunications
Chapter 6 7.1 Operational efficiency—an
overview 109
Valuepropositions in
7.2 Salesmonitoringandcontrol 110
telecommunications 95
7.3 Auniversalproblem 111
6.1 Miningtoolsandvaluedelivery 96
7.4 UsingMicrosoftQueryandExcelto
6.1.1 Operationalmonitoring and
do salestracking 111
control 96
7.4.1 Thesales database 112
6.1.2 Discoveryand exploration 97
7.5 Managing morecomplicated
6.2 Value propositions byfunctional
needs 115
area 98
7.6 Alternativemethodsof
6.2.1 Marketingvaluepropositions
accessingdata 115
(historical/cross-silo/
discovery) 99
6.2.2 Credit valuepropositions 99 Chapter 8
6.2.3 Customerservicevalue
Salesand productmanagement:
propositions—(real-timeand
advanced operationalmonitoring
historical/cross-silo/
usingCOGNOSPowerPlay 117
operationalmonitoring) 100
8.1 Monitoring complexbusiness
6.2.4 Sales valuepropositions 101
organizations 118
6.2.5 Networkplanningvalue
8.1.1 Determining the differentlevelsat
propositions 101
whichtoreport 119
6.2.6 Networkmaintenance value
8.1.2 Preparing the data foruse 121
propositions 103
6.2.7 Creation 103 8.2 Exploringsalesandproduct
6.2.8 Activationandprovisioning and performance 121
serviceorderprocessing 104 8.3 Additional PowerPlayfeatures 124
6.2.9 Billing (historical/single-silo/ 8.3.1 Alerts 125
discoveryand monitoring) 104 8.3.2 Schedulers 126
6.2.10 Operations 104
8.4 Summary 127
6.3 Conclusions 105
6.3.1 Knowledge management
Chapter 9
approach 105
Customer intimacy:
an introduction usingSPSS 129
Chapter 7
9.1 An introduction to analytical
Simplesalesanalysis: mining 130
anintroduction to operational moni-
9.2 Statisticalanalysis—optionsand
toring usingMicrosoftQuery 107
objectives 131
9.3 Descriptiveapproaches 133
Contents xi
9.4 Inferentialapproaches—regression 11.2 Using geographicalinformationto
analysis 137 solvetelecommunications
9.5 Conclusionsonstatistical problems 158
analysis 140 11.3 Cellsite analysiswithMapInfo
Professional 159
11.4 Marketanalysiscapabilities 162
Chapter 10
11.5 Viewingalocalmarketingreater
Predictingcustomerbehavior: detail 164
anintroduction to neural
11.6 Accessibilityto fiber analysis 165
networks 143
11.7 Workingwith theunderlying
10.1 Unravelingcomplex
database 167
situations 144
11.8 Conclusion 168
10.2 Howcananeuralnetworkhelp
withmarketing? 145
Appendix A
10.3 Step-by-stepuseofaneural
network 145 Real world warehousing: France
10.3.1 What does thetraining reporttell TelecomandSTATlab tools 169
us? 146
10.3.2 Creating andinterpretingthe
Appendix B
gains table 147
10.3.3 Analyzing thegains chart 149 The businesscase
forbusinessintelligence 211
10.3.4 Makingmarketing programsas
profitableas possible 151
Appendix C
10.4 Applying themodelto
prospects 152
SPSS 239
10.5 Conclusion on neural
networks 152
Appendix D
The DecisionWORKSsuite
Chapter 11
from Advanced Software
Engineering andcompetitive analysis Applications 245
support: anintroduction to geographi-
calsystemsandMapInfo 155
Glossary 251
11.1 An introduction to MapInfo
Professional 156
11.1.1 MapInfotelecommunications Selected bibliography 257
offerings 156