Managing customer data spatially

1

Managing customer data spatially

Fifth Annual GIS 2007 (Melbourne)Serena Coetzee

University of Pretoria2 May 2007

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South Africa & Tshwane

Afrikaans, EnglishIsiZuluIsiXhosaSiSwatiNdebeleSouthern

SothoNorthern

SothoTsongaSeTswanaVenda

Pretoria (executive)

Bloemfontein (judicial)

Cape Town (legislative)

4

South Africa

∙ 45 million people∙ 9 provinces∙ 262 municipalities

∙ 6 metropolitan municipalities

∙ 7 million land parcels∙ 6,3 million in (formal) urban areas

∙ 40% in Gauteng∙ 25% in the Western Cape∙ 16% in Kwa-Zulu Natal

∙ 500,000 sectional title properties∙ Largest address database: 3.5 million

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University of Pretoria (Tukkies)

∙ 1906 Transvaal University College∙ University of Pretoria

∙ 38 000 residential students ∙ 28 000 undergraduates∙ 10 000 post-graduates∙ 47% male, 53% female

∙ 2 000 international students from 60 countries

∙ Faculties∙ Economics & Management Sciences∙ Humanities∙ Health Sciences∙ Engineering, the Built Environment

& Information Technology∙ Natural & Agricultural Sciences∙ Education∙ Law∙ Theology∙ Veterinary Sciences

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History and Research Interests

ReGIS, Autodesk World

Spatial Datasets, PropertySPI

GI Standards

NAD on the gridCan we establish a virtual NAD for South

Africa in the form of a data grid?

+ + =

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Overview

Managing customer data spatially∙ Why manage customer data spatially?∙ Spatially enabling customer data

∙ Planning∙ Spatial Information Strategy∙ Customer address data model∙ Master address database

∙ Implementation∙ Integrate the address data model∙ Transform customers into spatial customers∙ Coping with uncertainty

∙ Operation∙ The address data life cycle

∙ Using spatial customer data

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∙ Why manage customer data spatially?∙ Spatially enabling customer data





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Why manage customer data spatially?

The Future of I.T.: What's on Tap for 2007 and Beyond

1. Customer Service Surges as a Top Priority for 2007

2. Business Intelligence Tops the Strategic Technology List

Source: www.cioinsight.com

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The 30 Most Important IT Trends for 2007

Technology

• The move to a new architecture marches on• Enterprise applications start losing their luster• Data quality demands attention• IT reluctantly embraces Web 2.0• IT innovation loses traction• Business process management services and

software will frustrate users• For business intelligence, the best is yet to come• IT organizations start going green

Source: www.cioinsight.com

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6

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“More than 25% of critical data used in large corporations is flawed due to

human data-entry error, customer profile changes (e.g. change of address),

poor processes and a lack of proper corporate data standards.”

The result: soiled statistics, faulty forecasting and

sagging sales

Source: Gartner Research quoted on www.cioinsight.com

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“Through 2007,

more than 50% of data-warehousing projects will experience limited

acceptance, if not outright failure,

because they will not proactively address data-quality issues.”

Source: Gartner Research quoted on www.cioinsight.com

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Source: www.gwsae.org

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The insurance industry is ready for the corporate wide proliferation of geographic

information systems as insurers rely on data that has a geographic

component to determine accurate underwriting, risk analysis and claims

management.

Employ Geographic Information Systems to Manage Risk for Property and Casualty Insurers, 11 October 2006,

Stephen Forte Source: www.gartner.com

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∙ Data quality∙ Address verification∙ Return to sender improvements

∙ Business intelligence for improved customer service∙ Routing and deliveries∙ Geo-marketing∙ Outlet planning∙ Demarcation (sales areas, etc.)

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Planning

Challenges∙ Buy-in on executive level∙ Continuous long term process∙ Customer’s perception of what his/her

address should be

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Planning

One of our strongest weapons is dialogue.

Nelson Mandela

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Planning

∙ Understand and articulate the benefits of spatial customer data

∙ Convince non-technical executives about the benefits of spatial address data

∙ Associate the benefits to an identified risk or business event

Ready to start…

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Planning

Spatial Information Strategy

Infrastructure (hardware & networks)

Spatial reference data

Software

Con

tract

s

Pro

cess

Business data $$$$

PeoplePeople

Reference Data

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Handling the present(TPS)

Preparing for the future(BI, data mining, DSS,

EIS, MIS, OLAP)

Remembering the past(databases and data warehouse)

Planning

Source: Watson

Data

New business systems

Transactions

People People & &

technologytechnology

Address capturing

, for delivery

Spatial Analysis

Address Structuri

ng & Cleaning

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Planning

Source: DM Functional Framework by DAMA

Data ManagementFunctions

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Planning

∙ Who is responsible for customer address?∙ Information (CIO)∙ Analytics (GIS)∙ Development (IT)∙ Business (CRM)

∙ Decide why you need spatial customer data

∙ Design the address data model

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Planning

Purpose: Address Verification

28.273632-

25.792344

101 Koljander AvenueNewlandsPretoriaGauteng

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Planning

Purpose: Deliveries

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Planning

Purpose: Customer profiling

28.270885-

25.790764

101 Koljander AvenueNewlandsPretoriaGauteng

45 Nutmeg AvenueNewlandsPretoriaGauteng

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Cow of many -well milked and badly fed

Spanish proverb

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Planning: Address data models

Geographic Information – Address standardSANS1883

Address = StreetAddress | BuildingAddress |IntersectionAddress | …

StreetAddress = StreetAddressPart, Locality

StreetAddressPart = [CompleteStreetNumber | StreetNumberRange], CompleteStreetName

Locality = PlaceName, [TownName],[MunicipalityName], [Province],[SAPOPostcode], [Country] | [CountryCode]

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Geographic Information – Rural and urban addressingAS/NZS 4819:2003

An urban address includes, in order, the following:∙ Sub-dwelling (flat/unit) number or identifier∙ Level number of sub-dwelling∙ Private road name (if applicable)∙ Utility name (if applicable)∙ Address site name (if applicable)∙ Single urban address number or urban address number range∙ Road name∙ Locality∙ State/territory∙ Postcode (optional)∙ Country

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Organization for the Advancement of Structured Information Standards (OASIS)∙ www.oasis-open.org ∙ Members

∙ Over 5,000 Members from 100+ countries of OASIS

∙ Software vendors, industry organizations, governments, universities and research centers, individuals

∙ Co-operation with other standards bodies∙ Best known for web services, e-business,

security and document format standards∙ Open and royalty-free standards

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OASIS Customer Quality Information TC∙ http://www.oasis-open.org/committees/ciq ∙ Chairman: Ram Kumar, Mastersoft, Australia∙ XML Specifications ∙ for defining, representing, interoperating and

managing party information ∙ name, address, party specific information

including party relationships∙ open, vendor neutral, industry and application

independent, ∙ "Global" (international)∙ Extensible Address Language (xAL) to define a

party’s address(es)

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Par ty Rel ated

Data

Data Quality (Data Parsing, Data Matching / de-duping, data standardization, data migration, data validation, data enhancement)

Data Quality (Data Parsing, Data Matching / de-duping, data standardization, data migration, data validation, data enhancement)

Customer Segmentation

Customer Segmentation

Party Identification/ Recognition

Party Identification/ Recognition

Postal ServicesPostal Services

Customer Profiling

Customer Profiling

Customer Relationships

Customer Relationships

Customer ViewsCustomer Views e-business/ e-commercee-business/ e-commerce

Other Specific Applications (e.g.

Health, Tax, manufacturing, retail, finance, automotive,

justice, etc)

Other Specific Applications (e.g.

Health, Tax, manufacturing, retail, finance, automotive,

justice, etc)

Customer Analytics, Interaction ChannelsCustomer Analytics, Interaction Channels

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cd xNAL

xNAL

Record Postal label

Address

+ Address

+ Address

+ AdministrativeArea

+ Country

+ FreeTextAddress

+ Locality

+ Location Coordinates

+ PostalDeliveryPoint

+ Postcode

+ PostOffice

+ Premises

+ RuralDelivery

+ Thoroughfare

(from CIQ Data Model)

Addressee

XOR

Name

- Free Text Name Line

+ Organisation Name

+ PartyName

+ PartyNameType

+ Person Name

+ Subdivision

(from CIQ Data Model)

1..*1..*

1..*

xNAL (xNL + xAL) Model

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Planning: Address data modelscd Address

«type»Address

- Type: - Usage: - Date Valid From: - Date Valid To: - Postal ID:

FreeTextAddress

Country

- Name: - Code:

Administrativ eArea

- Name: - Type: Locality

- Name: - Type:

Thoroughfare

- Name: - Number: - Type:

Premises

- Name: - Number: - Type:

Postcode

- Identifier:

RuralDeliv ery

- Identifier:

PostalDeliveryPoint

- Identifier: PostOffice

- Identifier:

Location Coordinates

- Identifiers:

«hierarchy»

«hierarchy»

«hierarchy»

0..1

0..1

0..1

0..1

0..1

0..1

0..1

0..1

0..10..1

0..1

xAL Model

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∙ Customer’s perception and preferences∙ 14 Castle Pine Crescent (English)∙ 14 Castle Pine Singel (Afrikaans)

∙ 477 Chopin Street, Glenstantia (Post Office)∙ 477 Chopin Street, Constantia Park (Surveyed)

∙ 17 Glenvista Street, Woodhill (colloquial)∙ 17 Glenvista Street (erf 672), Pretoriuspark Ext

8 (registered at the deeds office)

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Planning: Master address database

∙ Source: official vs unofficial∙ Maintenance cycle∙ Coverage∙ Data model∙ Level of detail

∙ Address∙ Address Range∙ Street∙ Suburb∙ Postcode and/or post office∙ Region ∙ Country

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Cadastral Addresses∙ Based on cadastral boundaries∙ Street numbers sourced from relevant official bodies

∙ Link street address to property information∙ owner, price, bond information, etc.

∙ Accommodates for anomalies (panhandle, skip numbers)∙ Address verification, routing, deliveries, customer profiles

2 4 8 10

12A

12B16

GORDON STREET

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Address Range ∙ Street numbers surveyed at street corners∙ Street numbers evenly allocated in between

∙ Includes street numbers that do not exist∙ Cannot link the street address to property information∙ Routing, deliveries, customer profiles∙ Not good enough for address verification

2 4 8 106GORDON STREET

12 14 16

GORDON STREET2 16

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Street

W aterkloof R idge

W aterkloof

Lynnw ood G len

Menlo P ark

W aterkloof G len

New lands

W aterkloof He ights

Hartebeestpoort 362- J r

MenlynAshlea Gardens

W aterkloof P ark

Alphen P ark

Maroe lana

Lynnw ood P arkHaze lw ood

De Beers

RA

YM

ON

D S

TR

EE

T

N1

NA

TIO

NA

L F

RE

EW

AY

GARSFONTEIN (M30) AVENUE

DELPHINUS S

TREET

ERIDANUS S

TREET GE

N L

OU

IS B

OTH

A (M

33) D

RIV

E

TAU

RU

S AVEN

UE

JEREMY STREET

KO

RA

NN

AB

ER

G R

OA

D

GRUS STREET

GARSTFONTEIN (M30) ROAD

MALDON ROAD

MARIAN ROAD

ALBERT ADAMSON STREET

HIGH STREET

TW

EN

TY

FIR

ST

ST

RE

ET

SE

LD

ER

Y A

VE

NU

E

BO

GE

Y S

TR

EE

T

BANKET ROAD

GARY AVENUE

TUCKER AVENUE

RO

AD

SADIE STREET

SNOWDROP AVENUE

LONG STREET

SIDNEY R

OADT

WE

NT

Y T

HIR

D S

TR

EE

T

TW

EN

TY

SE

CO

ND

ST

RE

ET

EIG

HT

EE

NT

H S

TR

EE

T

TW

EN

TY

FO

UR

TH

ST

RE

ET

TW

EN

TY

FIF

TH

ST

RE

ET

KAFUE STREET

JULIUS JEPPE STREET

ILKEY ROAD

TREVOR GETHING STREET

NUTMEG AVENUE

MARIGOLD AVENUE

N1

ST

RE

ET

INGERSOL ROAD

SPROKIE AVENUE

ANTHONY STREET

INNES ROAD

COGHILL R

OAD

LANDI STREETGRASKOP ROAD

DUNHILL STREET

JACQUELINE DRIVE

FOR

BES R

EEF RO

AD

OLIVIER STREET

NICOLSON STREET

KARIBA STREETALBERT ROAD

CLARK STREET

EDW ARD STREET

MILNER STREET

GOLF STREET

MAIN STREET

TEGAN STREET

OUTENIQUA AVENUE

RINA VAN ZYL STREET

ROSLYN AVENUE

PALM

PLA

CE

ST

RE

ET

IDOL ROAD

JOHN SCOTT STREETD

EL

PH

I ST

RE

ET

LOIS

AVEN

UE

ADINA ROAD

ELVERAM STREET

JAS

ON

RO

AD

LUNAR AVENUE JES

SIE

CO

LL

INS

ST

RE

ET

GLENW OOD ROAD

ANTON VAN WOUW

STREET

HE

LO

MA

ST

RE

ET

SID

NE

Y S

TR

EE

T

SERENE STREET

THE KOPPIE ROAD

TW

EN

TY

SIX

TH

ST

RE

ET

ARIES S

TREET

DELY ROAD

CHARLES (M11) STREET

ESSENHOUT STREET

CO

RO

BA

Y A

VE

NU

E

PETRONELLA STREET

KOELMAN ROAD

BUTTON PLACE

KA

ST

EE

L R

OA

D

EU

GE

NE

BR

AS

LE

R S

TR

EE

T

MONTY STREET

GLEN MANOR AVENUE

COPSE LANE

W AY

COETZEE STREET

TEGAN

WIN

DSOR ROAD

MA

TR

OO

SB

ER

G R

OA

D

FALDA ROAD

VICTORIA STREET

0 .2 .4 .6

Kilometers

LegendSuburbStreetHighwayMain Road

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Suburb or Region

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Postcode and/or post office

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∙ Mapping to customer address data model∙ Plan for the future

∙ Master address database independent∙ Increasing levels of detail∙ Accessibility by all departments∙ Tools

∙ Knowledge Management∙ What address information is available?∙ How do I access the address information?∙ What can I do with the address information?∙ What tools are available?∙ How is the address captured?

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Implementation: Integrate data model

∙ Address is not an attribute of the customer!∙ Link an address entity/object to the customer

Customer ID 198374

Name Mr Smith

AddressLine1 14 Collins Street

AddressLine2 Hatfield

AddressLine3 South Africa

Postcode 0083

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tblAddress

PK pkAddressID

tblCustomer

PK pkCustomerID

FK1 pkAddressID

tblBranch

PK pkBranchID

FK1 pkAddressID

tblATM

PK pkATMID

FK1 pkAddressID

tblSupplier

PK pkSupplierID

FK1 pkAddressID

tblOutlet

PK pkOutletID

FK1 pkAddressID

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Source: GINIE project

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It is a capital mistaketo theorize

before one has data.

Sir Arthur Conan Doyle, “A Scandal in Bohemia”,

The Adventures of Sherlock Holmens 1891

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Implementation: Transform customers

∙ Start with bulk transformation∙ Refine addresses further∙ Limit manual intervention∙ Decide on thresholds∙ Store the linked ID + the original address!∙ Call centre involvement

∙ Cost is a factor∙ Call centre training

∙ Whenever contact is made∙ Update customers who are in contact

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Refinement Process

Original Customer Addresses

etc.

100% Correct Addresses



<100% Correct Addresses

<80% Correct Addresses

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ThresholdsOriginal customer address % Master database

2340Sekanama Street Albarnie Pretoria 84.0 7497

SELALA STREET NALEDI PRETORIA

Talitha STREET Derdepoort Pretoria 84.1 160

TSAMMA STREET DOORNPOORT PRETORIA

Atterbury ROAD Centurion Centurion 84.1 160

AMCOR ROAD

CENTURION CENTRAL CENTURION

4

Cecile ROAD Doringkloof Centurion 84.1 163

CECILE STREET DORINGKLOOF CENTURION

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Pitfalls: 100 Rubida Street, Die Wilgers

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Pitfalls:2 Protea Road,Sandown R

IVO

NIA

RO

AD

GRAYSTO

N DRIVE

SOUTH ROAD

KATHERINE STREET

WEST STREET

PR

OT

EA

RO

AD

PR

OTE

A P

LAC

E

Sandown

Chislehurston

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Implementation: Coping with uncertainty

∙ Flag uncertain records∙ Type of uncertainty∙ As much information as possible∙ Contact details∙ Status history

∙ Uncertainty resolution∙ Pick ‘n Pay HomeShopping: next day∙ eBucks: next week∙ Invoices: end of the month

∙ Business value∙ Evaluate the cost benefit∙ Does improved accuracy add to customer service?∙ Does improved quality add to customer service?

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1A

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Cleaning

Operation: The address data life cycle

Capturing

Matching & Verification

Delivery & Analysis

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Cleaning

Capturing

Matching & Verification

Delivery & Analysis


PO Box

Private Bag 15

Postnet

Building

StrNo

Str Name

Suburb Die Wilgers

City Pretoria

Code 0041

Province Gauteng

Type PrivateBag

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AddressCapturing

AddressCleaning

AddressGeocoding &Verification

AddressDelivery &Analysis

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Get it right the first time∙ Search facility∙ Consistent address capturing∙ Capture verified/valid addresses

∙ Add coordinate while capturing∙ Comply to postal delivery requirements

while capturing

∙ List ∙ old & new names∙ language alternatives

AddressCapturing

AddressCleaning



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AddressCapturing

AddressCleaning



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Get it right the first time∙ Address data model∙ Automate as much as possible∙ Check for alternative, old & new

names∙ Complete partial addresses (e.g.

province)∙ Split address types

AddressCapturing

AddressCleaning



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AddressCapturing

AddressCleaning



Line 1 Celtis Plaza

Line 2 1085 Schoeman St

Suburb Hatfield

City Pretoria

Code 0083

POBox

Private Bag

Postnet

Building Celtis Plaza

StrNo 1085

Str Name Schoeman Street

Suburb Hatfield

City Pretoria

Code 0083

Province Gauteng

Type Building

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AddressCapturing

AddressCleaning



Line 1

Line 2 P/Box 14134

Suburb

City Hatfield

Code 0028

PO Box 14134

Private Bag

Postnet

Building

StrNo

Str Name

Suburb Hatfield

City Pretoria

Code 0028

Province Gauteng

Type POBox

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Get it right the first time∙ Automate as much as possible∙ Accuracy required?∙ Coordinate reference system to be

used∙ Use as many datasets as possible

AddressCapturing

AddressCleaning



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AddressCapturing

AddressCleaning



Address verificationPOBox

Private Bag

Postnet

Building

StrNo 101

Str Name Koljander Avenue

Suburb Newlands

City Pretoria

Code

Province Gauteng

Type Street

28.273632-

25.792344

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AddressCapturing

AddressCleaning



Deliveries

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AddressCapturing

AddressCleaning



Suburb or RegionPOBox

Private Bag

Postnet

Building

StrNo 101

Str Name Koljander Avenue

Suburb Newlands

City Pretoria

Code

Province Gauteng

Type Street

28.270885-

25.790764

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Get it right the first time∙ Understand source of addresses∙ Understand business challenges∙ Overlay with other datasets:

∙ Other businesses, competitors∙ Public transport & road network∙ Demographics: Census, LSM, etc.

AddressCapturing

AddressCleaning



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Effective information management must begin by thinking about how people use information –

not with how people use machines.

Thomas Davenport, Harvard Business Review,

1994

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Address data in South Africa

Street Address:

9 Glenvista Street

Woodhill

Pretoria

(City of Tshwane)

Gauteng

PO Box Address:

PO Box 153

Woodhill

(Kromdraai)

0081

Postal Street Address:

9 Glenvista Street

Woodhill

(Kromdraai)

0081

Deeds Office ERF Description:

Proclaimed Town: Pretorius Park Ext 8

Erf: 676,0

Deeds Office: Pretoria (T)

Surveyor General ERF Description:

Minor Region: PRETORIUS PARK EXT 8

Major Region: JR

Erf: 676

Portion: 0

SG Code: T0JR02050000067600000Building:

Glen Hills No 6

Glenvista Street

Woodhill

Pretoria

Gauteng

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Using spatial customer data

∙ Delivery∙ Mail∙ Geo-marketing∙ Outlet Planning

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DeliveryPick ‘n Pay HomeShopping

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Delivery

∙ Pick ‘n Pay∙ Largest retailer in South Africa∙ Groceries, toiletries, clothing, electrical

appliances, and more∙ Started an Internet shopping company in 2002∙ www.picknpay.co.za

∙ Challenge∙ Integration with existing online shopping site∙ Integration with new logistics software for

deliveries∙ Not-found addresses: 72 hour turnaround time∙ Conversion of existing customers∙ Client provides logistics software to Pick ‘n Pay

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0 6 12 18

Kilometers

LegendMunicipalitySuburbHighwayMain RoadSilver Lakes

City of Tshwane Metropolitan Municipality

Moretele Local Municipality

Nokeng tsa TaemaneLocal Municipality

City of Johannesburg Metropolitan Municipality

Local Municipalityof Madibeng

KungwiniLocal Municipality

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Delivery

Cambio process

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Delivery

Reflection∙ Working with the public…∙ Customer’s perception vs master

database∙ Do not rely on the address ID only∙ Coping with uncertainty

∙ Simplify the process∙ Not-found customers∙ Customer notifications∙ Cost∙ Training

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Customer’s perception…

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Customer’s perception…

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MaileBucks

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Mail

∙ eBucks∙ Rewards program∙ eBucks are earned for shopping and paying

bills ∙ No membership fee (free)∙ Ten eBucks equals one Rand: eB10 = R1 ∙ www.ebucks.com

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Mail

∙ Challenge∙ Integrate an address capturing interface into

existing FNB Online and eBucks website∙ Object-oriented database without SQL

interface∙ UNIX environment∙ No changes to the address data model allowed∙ Call centre training∙ Client in Information Management

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Mail

∙ Reflection∙ Many address types (result is free format)∙ Building names (work address)∙ Process refinement

∙ which addresses are really important?∙ Website integration

∙ Developer training∙ IT personnel – high turnover

∙ Moving target∙ Initial data cleaning∙ Then door-to-door delivery of “Welcome package”∙ Then postal rebates

∙ “Trip into space” - competition

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Geo-marketingMultiChoice

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Geo-marketing

∙ MultiChoice∙ Entertainment Television (mainly satellite)∙ DStv, DStv Indian and DStv Portuguesa ∙ Contract channels from various broadcasters,

sell them to the public (subscribers)∙ More than 1.3 million subscribers∙ Series Channel, Movies, History, National

Geographic, Discovery, Sport, CNN, BBC, Cartoon Network, Boomerang, M-TV, etc.

∙ Part of the MIH group (Naspers)∙ Africa, Mediterranean & Asia (50 countries)∙ Internet & television subscribers

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Geo-marketing

∙ Challenge∙ Annual study to find

∙ ‘gaps’ in the MultiChoice footprint∙ areas that should be targeted with marketing

campaigns to get subscribers∙ Client in Agency Management

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Geo-marketing

Woodhill

Moreletapark

WOODHILL

OLYMPUS AH

MOOIKLOOF

GarsfonteinOlympus AH

Mooikloof

OPEN SPACE

GARSFONTEIN

ZWAVELPOORT

MORELETA PARK

0 .4 .8 1.2

Kilometers

LegendStatistics SALiving Standards (LSM)StreetWoodhill (Statistics SA)Woodhill (LSM)

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Geo-marketing

Background: market potential Dots: market penetration

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Geo-marketing

∙ Reflection∙ Addresses had to be structured, cleaned and

verified every year∙ Slow turnaround∙ Address capturing process is now being updated

and integrated (faster results)

∙ Aligning demographical & customer address data

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Outlet planningDaily Sun

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Outlet planning

Northern Cape

Western Cape

Eastern Cape

Free State KwaZulu-Natal

Limpopo

North W est

Mpumalanga

Gauteng

∙ Daily Sun∙ Biggest daily newspaper in South Africa∙ Target market

∙ predominantly black∙ English literate ∙ Minimum high school education∙ working - the economic core of South Africa

∙ 400 000+ sales in Gauteng,Limpopo, Mpumalanga,Northwest Province

∙ Also KwaZulu-Natal, Free State and Eastern Cape

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Outlet planning

∙ Challenges∙ Identify the gaps in the Daily Sun footprint∙ Compare street to outlet sales∙ Compare sales volumes to e.g. traffic data∙ Client in Distribution Management

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Outlet planning

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Outlet planning

∙ Reflection∙ Outlets in rural areas with descriptive

addresses∙ Outlets are moving around∙ Map reading skills∙ Address capturing process now being

integrated

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Acknowledgements

AfriGIS for use of their data and case studies

The Computer Science department at the University of Pretoria for their support

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More interesting reading…

• Address Markup Languages, http://xml.coverpages.org/namesAndAddresses.html

• AfriGIS, www.afrigis.co.za• CIO Insight, www.cioinsight.com • The Data Management Organization, www.dama.org • Geographic Information Network in Europe (GINIE),

www.ec-gis.org/ginie/ • Ireland’s GeoDirectory, www.geodirectory.ie • OASIS, www.oasis-open.org • PSMA Australia, www.psma.com.au• Richard T.Watson, Data Management Databases and

Organizations, John Wiley & Sons, Inc, Fifth Edition, 2006

• University of Pretoria, www.cs.up.ac.za

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Serena CoetzeeUniversity of [email protected]

+27 82 464 4294

Managing customer data spatially

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