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Event: DDMA SeminarThema: DatakwaliteitSpreker: Jos Leber – T-MobileDatum: 5 juni 2007

www.ddma.nl

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Data Quality Award 2006Presentatie T-Mobile Seminar Data Kwaliteit DDMA 5 juni 2007

Jos Leber – Data Manager T-Mobile Netherlands BV

DM

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Van Data Cleaning projecten naar Data (Quality) Management Rol tijdens het Phoenix project (A4) Stappen na het project 2005 – 2006 Drie belangrijke stappen nader toegelicht:

Ontwikkeling van een CRM data standaard Ontwikkeling van tools om te meten en proces Data Management Maturity Model

Waar staan we nu? Conclusie en Lessons learned

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Inconsistencies 2004

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

1800000

2000000

Number

04/1

2/20

03

08/0

1/20

04

22/0

1/20

04

05/0

2/20

04

19/0

2/20

04

04/0

3/20

04

11/0

3/20

04

18/0

3/20

04

01/0

4/20

04

07/0

4/20

04

14/0

4/20

04

21/0

4/20

04

28/0

4/20

04

05/0

5/20

04

12/0

5/20

04

19/0

5/20

04

26/0

5/20

04

02/0

6/20

04

09/0

6/20

04

16/0

6/20

04

23/0

6/20

04

30/0

6/20

04

14/0

7/20

04

28/0

7/20

04

11/0

8/20

04

25/0

8/20

04

08/0

9/20

04

22/0

9/20

04

06/1

0/20

04

20/1

0/20

04

Date

Open cases

Open CasesSolved cases

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Stappen tijdens het Phoenix projectQ4

2004Q1

2005Q2

2005Q3

2005Q4

2005Q1

20060. Definitie Scope Phoenix project

1. Data CleaningNorm definitieData Cleaning (98 issues)Criteria voor data cleaningData Mapping inventarisatie tbv standaardAftercare

2. Data Migratie/AcceptatieDAT Data Acceptatie Test plan

Data Base Attributes List Business rule book

Data Display TestsSpecial Test CasesSanity Check

3. Tooling & Proces Ontwikkeling Compare & Quality toolsDagelijks DQM meetingsRapportage aan management (IPB)

Go Live

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…SOURCE

…SOURCE

BSCS SOURCEBSCS

SOURCECLARIFYTARGETCLARIFYTARGET

Target DatabaseDefinition

Target DatabaseDefinition

Migration Process

1. Extract data2. Clean/Transform3. Upload

Migration Process

1. Extract data2. Clean/Transform3. Upload

Configuration Data Data Mapping Rules

Target Interface Designs Data Masters

Configuration Data Data Mapping Rules

Target Interface Designs Data Masters

INTERMEDIATEDATABASE

INTERMEDIATEDATABASE

CLARIFYSOURCECLARIFYSOURCE

Data Migration Process

Data Migration Acceptance

• Completeness all customers. How can we be sure and measure that all the customers are migrated?

• Completeness customer record How can we be sure and measure that all data records per customer are completely migrated?

• Correctness customer data.How can we be sure and measure that the attributes are migrated correctly from the “old” databases in the new Clarify structure?

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Stappen na het Phoenix projectQ3

2005Q4

2005Q1

2006Q2

2006Q3

2006Q4

20060. Data Quality Management

1. CRM Data Standaard CRM Data Standard ontwikkeling Glossary of Terms Meelopen in projecten Formaliseren data standaard DSF Data Standards Forum

2. Data Quality Monitoring Uitbreiden tool met 36 Quality criteria DQM meetings (Clarify-BSCS) DQM dashboard (rapportage 5 systemen) Data Quality Norm & Target setting

3. Visie Focus op structurele verbeteringen Data Management vragen lijst 75 Data Management Maturity Model

“Road shows”

Customer ServicesDQM

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Data DefinitionStarted in 2005 withBusiness Glossary of Terms: “speak the same language”

to be used as input for IS projects and other internal communication

In a Data standard attributes (fields) are defined e.g. bank account nbr:

how it is named for what purpose do we use and maintain this attribute It’s length it’s validation rules

In 2005 a customer data standard was developed in order to clean data (Phoenix). In 2007 we focus on a Product data standardNext is a contract data standard

Based on this (customer) data standard and norm we can measure data quality

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The Scope of Data Definition

Out of scope:Network dataFinancial dataHRM data

In scope:Relation dataProduct dataContract data

Relation Data Management

(Suspect/Prospect/Customer data)

Product Data Management(Product/Service

combinations, pricing, discounts)

Cont

ract

Data

Manag

emen

t

(Servi

ce, ra

te-pla

n, tim

e

perio

d, oth

er con

dition

s)

CRM Data

Financial DataHRM data

Network Administratio

n Data

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Technical concept DQ Dashboard

BSCS

Clarify

Copy

Copy

Compare &

Quality

Database

tuned queries

Spot inconsistencies2x

Persistent inconsisten

cies CSV

Bi Weekly Quality Monitoring

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

12 jan2006

19 jan2006

26 jan2006

05 feb2006

09 feb2006

22 feb2006

09 mar2006

27 mar2006

06 apr2006

Perc

enta

ge C

usto

mer

s w

ith a

n in

cons

iste

ncy

% active Customers with an inconsistency issue without customer impact% active Customers with an inconsistency issue with direct customer impactTarget 0.50%

Monthly

DailyActive

Compare Statistics (Excel)

+ details file

Summary Sheet(Excel)

IPB Report(Powerpoint)

Quality issues

Active Quality Statistics (Excel)

+ details fileWeekly

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Data Quality Management ResultsSince June 2005 T-Mobile has taken a structural approach to Data Quality Management (DQM)

Results since mid 2005

June 6th

2005

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

Bi-weekly data quality monitor

Perce

ntage

of ac

tive cu

stome

rs

% active Customers with any data quality issueTarget 0.50%

November 17th 2005

Result is:We have prevented ca 20.000 potential customer problems like incorrect invoices, incorrect network settings etcFor the customer this means no disruption in using our products and services.For Customer Services this means fewer calls and fewer complaints. For IT this means less incidents to be solvedAnd ultimately higher customer satisfaction!

June 6th 2005

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Data Quality & Inconsistency MonitorBSCS – Clarify Inconsistencies

Ca 5.800 Payment Methods were missing in Clarify for new customers. Is fixed in 6.3.3 and scripts have cleaned ca 500 per day. Impact was that the data is not visible on My T-Mobile and Clarify.

All remaining customer data has been cleaned by the 2 FTE’s in Breda. The total number of inconsistencies is 828 = 0,0674 % There are ca 30 customers with direct impact = 0,002 %

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

2005-11-17

2005-12-15

2006-01-19

2006-02-23

2006-03-23

2006-04-20

2006-05-19

2006-06-22

2006-07-19

2006-08-02

2006-08-17

2006-08-31

2006-09-14

2006-10-10

Bi weekly Quality Monitoring

Perc

enta

ge C

usto

mer

s w

ith a

n in

cons

iste

ncy

% active Customers with an inconsistency issue without customer impact

% active Customers with an inconsistency issue with direct customer impact

Target 0.50%

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Our approach to manage data quality is to continue the operative cleaning started with Phoenix and in parallel establish a conceptual data management to reduce the required cleaning effort

Current focus is on reactive data-management. Trouble shooting when problems get identified

•Develop business model•Logical data model•Technical data model•Data Distribution matrix•Glossary of terms•Data standard•GUI design standards •Interface architecture•Business & validation rules•Contact/channel matrix •Monitor data quality•KPI’s for data quality in SLA & PM’s

Proactive AdaptiveReactive

Incident &problem management; Clean/

repair data when problems become

visible

Preventive testing & data inconsistency monitoring in

order to proactivelyidentify and correct errors

/problems

Make sure new projects and changes

are in line with business and data

modelFind and fix the root cause

Data Quality Management From Reactive to Adaptive Data Management

Clean data manually or via script

•ITT and UAT testing•End to end testing•Data Acceptance testing

•Data Monitoring•Create incidents/problems•Work around scripts

0%

1%

2%

3%

4%

5%

6%

7%

J an Feb Mar Apr May J un J ul Aug Sep Oct Nov Dec

Clarify QualityMy T-MobileBSCS-HLR ADB-BSCSClarify - BSCS inconsistency

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T-Mobile Enterprise Data Management Maturity model

Risk

Rew

ard

LOW

LOW

HIGH

HIGH

Level 1 Level 2 Level 3 Level 4 Level 5

People, Process, Technology Adoption

Service Mgmt Risk - - - - - - - - - - - - - - - - - - - -

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Problem Mgmt- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - -- - - - - - - - - - - Data Administration-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - Data Management

Reward- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -

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Waar staan we nu?

Concept visie voor 2007: Ownership Problem Management Product Master Data Management Tools (ownership/part of new releases) (Meta) Data Administration

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ConclusieLessons learned Data (Management) is een business verantwoordelijkheid Data Management is gebaseerd op een partnership met IT Data Kwaliteit moet geborgd worden in de organisatie Data Management Maturity Model als roadmap

Keuze Cultuur verandering Stap voor stap

Data CleaningFix root causes

+

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Data moet niet alleen gecleaned worden maar ook gemanaged ! We hebben het momentum van een project gebruikt om met data management te starten.

Het “phoenix project” is gebruikt om het CRM (Clarify) call centre systeem opnieuw en juist in te richten

Alle customer data te cleanen

Gelijktijdig een customer data standaard te ontwikkelen

Tools te ontwikkelen om dagelijks te kunnen meten = weten en te rapporteren aan een centrale management groep

Een proces in te richten in de gebruikersorganisatie om de bewaking van data kwaliteit te borgen

Kwaliteitsnormen vast te stellen en aan te scherpen

Een eigen T-Mobile data management maturity model te ontwikkelen dat als roadmap kan worden gebruikt

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%

0.60%

2005-11-17

2005-12-15

2006-01-19

2006-02-23

2006-03-23

2006-04-20

2006-05-19

2006-06-22

2006-07-19

2006-08-02

2006-08-17

2006-08-31

2006-09-14

2006-10-10

2006-11-08

Bi w eekly Quality Monitoring

Per

cent

age

Cus

tom

ers

with

an

inco

nsis

tenc

y

% active Customers with an inconsistency issue without customer impact% active Customers with an inconsistency issue with direct customer impact

Target 0.50%

+

Ada

ptiv

ePr

oact

ive

Rea

ctiv

e