DDMA / T-Mobile: Datakwaliteit
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DDMA / T-Mobile: Datakwaliteit

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Jos Leber van T-Mobile over datakwaliteit n.a.v. de nominatie van de DQ Award 2006

Jos Leber van T-Mobile over datakwaliteit n.a.v. de nominatie van de DQ Award 2006

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  • 2 onderwerpen: 1. Wat doet Jos? 2. Data Management Maturity Model 4 Slides + 1 back up slide 10 minuten
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  • Begin van 2006 Dit was mijn maturity model Besef dat we van incidenten oplossen naar het voorkomen van incidenten moeten. 1. Verschuiven van operationele issues (DQM en Incidenten)  rechts = voorkomen van escallatie en preventie  structurele activiteiten. 2. Onder andere door meten. 3. International alignment  One company
  • Research gedaan naar diverse modellen Eerst een vragenlijst gemaakt met 75 vragen (benchmark – best practice) Vragen samengevat tot 5 hoofd componenten en dat zijn ……….. Vorige week 26 en 27 sept DQ forum conference in Den Haag met als resultaat ……

Transcript

  • 1. Event: DDMA Seminar Thema: Datakwaliteit Spreker: Jos Leber – T-Mobile Datum: 5 juni 2007 www.ddma.nl
  • 2. Data Quality Award 2006 Presentatie T-Mobile Seminar Data Kwaliteit DDMA 5 juni 2007 Jos Leber – Data Manager T-Mobile Netherlands BV DM
  • 3. 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
  • 4. Inconsistencies 2004
  • 5. Stappen tijdens het Phoenix project Go Live Q4 2005
    • 3. Tooling & Proces
    • Ontwikkeling Compare & Quality tools
    • Dagelijks DQM meetings
    • Rapportage aan management (IPB)
    • 2. Data Migratie/Acceptatie
    • DAT Data Acceptatie Test plan
      • Data Base Attributes List
      • Business rule book
    • Data Display Tests
    • Special Test Cases
    • Sanity Check
    • 1. Data Cleaning
    • Norm definitie
    • Data Cleaning (98 issues)
    • Criteria voor data cleaning
    • Data Mapping inventarisatie tbv standaard
    • Aftercare
    0. Definitie Scope Phoenix project Q1 2006 Q3 2005 Q2 2005 Q1 2005 Q4 2004
  • 6. 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?
    … SOURCE … SOURCE BSCS SOURCE BSCS SOURCE CLARIFY TARGET CLARIFY TARGET Target Database Definition Target Database Definition Migration Process 1. Extract data 2. Clean/Transform 3. Upload Migration Process 1. Extract data 2. Clean/Transform 3. Upload Configuration Data Data Mapping Rules Target Interface Designs Data Masters Configuration Data Data Mapping Rules Target Interface Designs Data Masters INTERMEDIATE DATABASE INTERMEDIATE DATABASE CLARIFY SOURCE CLARIFY SOURCE
  • 7. Stappen na het Phoenix project “ Road shows” Customer Services DQM Q3 2006
    • 3. Visie
    • Focus op structurele verbeteringen
    • Data Management vragen lijst 75
    • Data Management Maturity Model
    • 2. Data Quality Monitoring
    • Uitbreiden tool met 36 Quality criteria
    • DQM meetings (Clarify-BSCS )
    • DQM dashboard (rapportage 5 systemen)
    • Data Quality Norm & Target setting
    • 1. CRM Data Standaard
    • CRM Data Standard ontwikkeling
    • Glossary of Terms
    • Meelopen in projecten
    • Formaliseren data standaard
    • DSF Data Standards Forum
    0. Data Quality Management Q4 2006 Q2 2006 Q1 2006 Q4 2005 Q3 2005
  • 8. Data Definition Started in 2005 with
    • Business 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 standard Next is a contract data standard
    Based on this (customer) data standard and norm we can measure data quality
  • 9. The Scope of Data Definition
    • Out of scope:
    • Network data
    • Financial data
    • HRM data
    • In scope:
    • Relation data
    • Product data
    • Contract data
    Relation Data Management (Suspect/Prospect/Customer data) Product Data Management (Product/Service combinations, pricing, discounts) Contract Data Management (Service, rate-plan, time period, other conditions) CRM Data Financial Data HRM data Network Administration Data
  • 10. Technical concept DQ Dashboard BSCS Clarify Copy Copy Compare & Quality Database tuned queries Spot inconsistencies2x Persistent inconsistencies CSV Monthly Da i ly Active Compare Statistics (Excel) + details file Summary Sheet (Excel) IPB Report (Powerpoint) Quality issues Active Quality Statistics (Excel) + details file Weekly
  • 11. Data Quality Management Results Since June 2005 T-Mobile has taken a structural approach to Data Quality Management (DQM) Results since mid 2005 June 6 th 2005 November 17 th 2005 Result is: We have prevented ca 20.000 potential customer problems like incorrect invoices, incorrect network settings etc For 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 solved And ultimately higher customer satisfaction! June 6 th 2005
  • 12. Data Quality & Inconsistency Monitor BSCS – 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 %
  • 13. 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
    Data Quality Management From Reactive to Adaptive Data Management
    • ITT and UAT testing
    • End to end testing
    • Data Acceptance testing
    • Data Monitoring
    • Create incidents/problems
    • Work around scripts
    Proactive Adaptive Reactive Incident &problem management; Clean/ repair data when problems become visible Preventive testing & data inconsistency monitoring in order to proactively identify and correct errors /problems Make sure new projects and changes are in line with business and data model Find and fix the root cause Clean data manually or via script
  • 14. T-Mobile Enterprise Data Management Maturity model Service Mgmt Risk - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Problem Mgmt - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - - - - - - - - -- - - - - - - - - - - Data Administration -- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - - Data Management Reward - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -- - - - - - - - - - -
  • 15. Waar staan we nu?
    • Concept visie voor 2007:
    • Ownership
    • Problem Management
    • Product Master Data Management
    • Tools (ownership/part of new releases)
    • (Meta) Data Administration
  • 16. Conclusie Lessons 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 Cleaning Fix root causes
  • 17. 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
    + Adaptive Proactive Reactive