DDMA Data Quality Award 2010 - Presentatie T- Mobile Netherlands - Jos Leber


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DDMA Data Quality Award 2010 - Presentatie T- Mobile Netherlands - Jos Leber

  1. 1. Event: DDMA Data Quality Awards 2010 Spreker: Jos Leber – T-Mobile Netherlands Datum: 14 oktober 2010, Klooster Noordwijk www.ddma.nl 21/10/2010 Page 1
  2. 2. How to get Data Quality Management started? T-Mobile Netherlands Jos Leber Sr. Data Manager Date: October 14th 2010 21/10/2010 Page 2
  3. 3. Contents Introduction T-Mobile How does Data Quality start? What is Data Quality within T-Mobile Netherlands Data Quality monitoring and Tools Cost of poor Data Quality TMNL Data Quality mission statement Data Management Maturity Models 21/10/2010 Page 3
  4. 4. T-Mobile biedt het wereldwijde netwerk. Gestart in 1999 als Sinds 2003 T-Mobile Toonaangevend bedrijf in mobiele communicatie. Een van de drie strategische business units van Deutsche Telekom. Wereldwijd bijna 151 miljoen mobiele communicatie klanten. In Nederland in 2009 een jaaromzet van 1,807 miljard euro en ruim 2000 medewerkers. Qua Data Quality Management: Meer dan 100 systemen En 60 tools 21/10/2010 Page 4
  5. 5. T-Mobile internationaal netwerk. • T-Mobile Amerika • T-Mobile Engeland • T-Mobile Duitsland • T-Mobile Tsjechië • T-Mobile Hongarije • T-Mobile Oostenrijk • T-Mobile Kroatië • T-Mobile Slovenië • T-Mobile Macedonië • T-Mobile Montenegro • ERA Polen • OTE groep (Griekenland, Roemenië, Bulgarije, Albanië) Daarnaast heeft T-Mobile roaming afspraken met ruim 400 roaming partners in meer dan 185 landen. 21/10/2010 Page 5
  6. 6. Complexity of Mobile communications • Ca 100 different barrings and SMS services • Block dialing all 0900 numbers • Block 0900 for 18+ numbers • Block downloading games etc • Block in cases of bad debt • Voice mail on / off? • “Nummer weergave” • Etc, etc • Family plan • Mobile to Mobile (business customers • Information technology • Network technology 21/10/2010 Page 6
  7. 7. The start for data quality in 2001 21/10/2010 Page 7
  8. 8. Bestandscan NEN 5825 Standard for street and city names naam-gegevens titel(s) 000 % voorletter(s) 100 % voorvoegsel(s) 021 % achternaam 100 % achtervoegsel(s) 000 % volledige zakelijke naam 001 % AW-gegevens straatnaam 100 % postbus 000 % huis/postbusnummer 100 % huisnummer-toevoeging 015 % postcode 100 % w oonplaats 100 % telefoon-gegevens netnummer 000 % abonneenummer 000 % net- en abonneenummer 042 % 21/10/2010 Page 8
  9. 9. Amount 0 04/12/2003 08/01/2004 22/01/2004 05/02/2004 19/02/2004 04/03/2004 11/03/2004 18/03/2004 01/04/2004 07/04/2004 14/04/2004 21/04/2004 28/04/2004 05/05/2004 12/05/2004 19/05/2004 Date 26/05/2004 02/06/2004 09/06/2004 16/06/2004 Open / Solved cases 23/06/2004 30/06/2004 Customer Data Inconsistencies (2004) 14/07/2004 28/07/2004 Differences between the CRM and Billing system 11/08/2004 25/08/2004 08/09/2004 22/09/2004 06/10/2004 20/10/2004 Open Cases Solved cases Page 9 21/10/2010
  10. 10. Steps during the Phoenix project Q4 2004 Q1 2005 Q2 2005 Q3 2005 Q4 2005 Q1 2006 0. Definitie Scope Phoenix Go Live project 1. Data Cleaning Norm definition Data Cleaning (‘X’ issues) Criteria for data cleaning Data Mapping versus data standard Aftercare 2. Data Migration DAT Data Acceptance Test plan Data Base Attributes List Business rule book Data Display Tests Special Test Cases Sanity Check 3. Tooling & Process Compare & Quality tools development Daily DQM meetings Reporting to management (IPB) 21/10/2010 Page 10
  11. 11. 21/10/2010 Page 11
  12. 12. Data Quality definition Data are of high quality if they are fit for their intended uses in operations, decision making, and planning (after Joseph Juran) Data that are fit for use are Free of defect: Posses desired features: - accessible - relevant - accurate - comprehensive - timely - proper level of detail - complete - easy to read - consistent with other sources etc - easy to interpret etc What is Data Quality for T-Mobile Netherlands? Definition of Data Quality according to a simple keyword: A.C.C.U. Actual is data still ‘up-to-date’ ? (e.g. Outdated data is corrected to the new/changed data standards) Correct Data is filled in within the confirmed standards (e.g. empty or not in the agreed format (Numeric, NEN conform etc)) Complete is any information missing? Unique is it unique, no duplicate relations (within a single system) What is Inconsistency? The same information different in two or more systems Data are only of high quality if those who use them say so. 21/10/2010 Page 12
  13. 13. Example of a data standard In a Data standard attributes (or fields) are defined for e.g. Dutch Postcode: how it is named for what purpose do we use and maintain this attribute what is the master? It’s length it’s validation rules Entity ADDRESS Standard Name Standard NL Attribute name Screen name Description and objective Norm Y/N Measured Format Master Mandatory Rules and values Comment name Logical Data Model Clarify Y/N? Clarify Postcode Postcode ZIPcode Postcode The Postal code of the formal physical Text20 x Yes Capitals location where a Customer is NL Postcode is stored as dddd AA (with settled/established. single space) [1-9][0-9]{3}s[A-Z]{2} In case of a foreign Postal code (ZIP0 the format is free text with a maximum of 20 characters. Y Y Overruled Afwijzing Overruled (not displayed) Indicates whether the postcode check Boolean x No (overschrijven) is overruled by Supervisor Y N X-coordinate X-coördinaat X_X_COORDINATE Coordinates x/y Geological x-coordinate of the location Number x No Selected fromGeo-tool table X-coordinate is specified using the identified by postcode and house ‘abc’ notation. According to this number; is used to calculate the standard the X-coordinate can be location of the “Home Zone” in the maximally 6 digits long GSM network (unconfirmed). Y-coordinate Y-coördinaat X_Y_COORDINATE Coordinates x/y Geological y-coordinate of the location Number x No Selected from ‘xyx’-tool table Y-coordinate is specified using the identified by postcode and house ‘abc’ notation. According to this number; is used to calculate the standard the Y-coordinate can be location of the “Home Zone” in the maximally 7 digits long GSM network (unconfirmed). 21/10/2010 Page 13
  14. 14. Data Monitoring and Data Inspection tools 0.60% Percentage Custom ers with an 0.50% inconsistency 0.40% 0.30% 0.20% 0.10% 0.00% 2005- 2005- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 2006- 11-17 12-15 01-19 02-23 03-23 04-20 05-19 06-22 07-19 08-02 08-17 08-31 09-14 10-10 11-08 Bi weekly Quality Monitoring % active Customers with an inconsistency issue without customer impact % active Customers with an inconsistency issue with direct customer impact Target 0.50% To measure is to know Meten = weten Messen ist Wissen 21/10/2010 Page 14
  15. 15. Technical concept DQ Dashboard Active Compare Statistics (Excel) Daily + details file used System to correct data A Active Quality Weekly Statistics (Excel) Extract + Persistent details file inconsistenci tuned Compar es CSV queries e Quality issues Extract & % active Customers with % active Customers with a Quality aantal met a blocking data quality non-blocking data quality Percentage Customers with Datum # active customers impact error aantal zonder impact error Target 6.00% 2008-02-08 592496 13279 2.2412% 22365 3.7747% 0.50% an inconsistency 5.00% Monthly 2008-03-17 581846 12240 2.1036% 15708 2.6997% 0.50% 4.00% 2008-04-23 730445 13451 1.8415% 14149 1.9370% 0.50% 3.00% 2008-05-14 735347 12390 1.6849% 8871 1.2064% 0.50% 2.00% Databas Spot 2008-06-17 2008-07-23 747582 761830 13897 13258 1.8589% 1.7403% 12229 12264 1.6358% 1.6098% 0.50% 0.50% 1.00% 0.00% 08 08 08 17 3 08 14 7 08 23 08 11 1 08 08 8 08 22 08 22 08 20 4- 2 20 6- 1 20 8- 2 20 9- 1 2008-08-11 766990 11818 1.5408% 8490 1.1069% 0.50% 20 2- 20 3- 20 5- 20 7- 20 8- 20 9- 20 0- 20 1- 2- -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 08 08 08 08 08 2008-08-21 767645 5607 0.7304% 8991 1.1712% 0.50% 20 2008-09-08 768004 4424 0.5760% 5984 0.7792% 0.50% e % active Customers w ith a non-blocking data quality error 2008-09-18 768244 3628 0.4722% 2624 0.3416% 0.50% inconsistenci % active Customers w ith a blocking data quality error 2008-10-22 768690 3034 0.3947% 1581 0.2057% 0.50% Target 2008-11-22 769156 2988 0.3885% 961 0.1249% 0.50% 2008-12-08 769156 2905 0.3777% 883 0.1148% 0.50% System es2x Summary Sheet Report B (Excel) (Powerpoint) 21/10/2010 Page 15
  16. 16. Compare statistics & details file examples Compare statistics Compare statistic active customers 0 01-08-10 08-08-10 15-08-10 21-08-10 29-08-10 Delta Impact Customer records compared 1 2190973 2195402 2199179 2203809 2211593 7784 Inconsistencies for category Customer 2 1823 1773 1766 1760 3366 1606 Customer.type 5 8 8 8 8 8 0 Customer.account_number 6 118 118 120 120 1694 1574 Verkeerd reknr op acc giro of A.I. Customer.payment_method 7 96 99 100 97 122 25 Verkeerde betaalwijze acc.giro of A.I. Customer.status 8 33 39 39 38 39 1 Klant inactive in system A and active in system B Customer.name 9 508 505 505 504 501 -3 Customer.billing_address.street 10 224 218 213 213 215 2 post niet naar juiste adres (SWL, Factuur) Customer.billing_address.housenr 11 178 173 171 171 172 1 post niet naar juiste adres (SWL, Factuur) Customer.billing_address.housenr_add 12 62 63 63 63 62 -1 post niet naar juiste adres (SWL, Factuur) Customer.billing_address.city 13 256 255 254 254 256 2 post niet naar juiste adres (SWL, Factuur) Customer.billing_address.zipcode 14 222 217 214 213 214 1 post niet naar juiste adres (SWL, Factuur) Customer.billing_address.country 15 10 10 11 11 13 2 post niet naar juiste adres (SWL, Factuur) Customer.billing_address.bill_line_2 16 108 68 68 68 70 2 Frico 10.2 Details file example attribute primary_key found_in found_in value_in_system a value_in_system b first_detected last_detected_ customer _system a_system b _date date _status Customer.account_number <<number>> Y Y 494291400 603238416 14-Aug-10 29-Aug-10 active Customer.account_number <<number>> Y Y 3243325 14-Aug-10 29-Aug-10 active Customer.account_number <<number>> Y Y 420657096 21-Aug-10 29-Aug-10 active Customer.account_number <<number>> Y Y 6287953 28-Aug-10 29-Aug-10 active Customer.account_number <<number>> Y Y 3674570 2838689 28-Aug-10 29-Aug-10 active Customer.account_number <<number>> Y Y 2195502 463859987 28-Aug-10 29-Aug-10 active Customer.account_number <<number>> Y Y 546149928 559308787 28-Aug-10 29-Aug-10 active 21/10/2010 Page 16
  17. 17. Data Quality check examples attribute primary_key parent_key ext_ref Source value first_detected_datelast_detected_date customer_status Cla_contact.birthname 123456677 1.11591288/0 System a 01/07/1955 03-Oct-10 04-Oct-10 active Cla_contact.birthname 269859164 1.11689312/0 System a BUSCH 03-Oct-10 04-Oct-10 active Cla_contact.birthname 269859164 1.11846553/0 System a CHEN 03-Oct-10 04-Oct-10 active Cla_contact.birthname 269859164 1.11870069/0 System a ROUS 03-Oct-10 04-Oct-10 active Cla_contact.birthname 270042435 1.11872380/0 System a VERHAGEN 03-Oct-10 04-Oct-10 active Cla_contact.birthname 270060987 1.11890909/0 System a SEWPERSAD 03-Oct-10 04-Oct-10 active Cla_contact.birthname 270070652 1.11900568/0 System a PIETERSE 03-Oct-10 04-Oct-10 active attribute primary_key parent_key ext_ref source value first_detected_datelast_detected_date customer_status Cla_contact.initials 272300706 1.11580066/1905523 System b JJ 03-Oct-10 04-Oct-10 active Cla_contact.initials 272300752 1.11580066/1905569 System b DJ 03-Oct-10 04-Oct-10 active Cla_contact.initials 272300754 1.11580066/1905571 System b MCA 03-Oct-10 04-Oct-10 active Cla_contact.initials 272300810 1.11580066/1905627 System b HJM 03-Oct-10 04-Oct-10 active Cla_contact.initials 272300982 1.11580066/1905799 System b HWA 03-Oct-10 04-Oct-10 active Cla_contact.initials 272300989 1.11580066/1905806 System b PFM 03-Oct-10 04-Oct-10 active Cla_contact.initials 272301111 1.11580067/1905928 System b r 03-Oct-10 04-Oct-10 active 21/10/2010 Page 17
  18. 18. Data Quality & Inconsistency Monitor Data Quality & Inconsistency Monitor Total System Overview - Direct Impact X% <<amount>> System Overview X % X % X% <<amount>> X% X% <<amount>> X% X% # customers <<amount>> X% X% X% <<amount>> X% X% X% <<amount>> X% X% X% <<amount>> X% <<amount>> X% ? X% X% X% New compare New compare 0 X% Jan Feb Mrt Apr Mei Jun Jul Aug Sep Oct Nov Dec System A versus B System X Quality System L versus H System C versus D System h / K Barrings System K retrospectively dbA versus dbB System Z Quality System L versus P System E / F retrospectively Overig (a,b,c,d,e,f,) System K retrospectively Percentage of total customers Overall target % 21/10/2010 Page 18
  19. 19. DQM “Driehoeks overleg” and Tooling System Originator Business IT/NT Ops Development DQM Fix Tools partner System a name name name System b name name Products Services service Under Development Functional System manageme management + Users nt Project Out of Scope SLA ’s + Cooperation KPI ’s Communication Structure Monitoring Problem management IT Service IT Management Enablers OLA 21/10/2010 Page 19
  20. 20. Data Quality improvement and 6 Sigma DMAIC methodology - How to systematically improve data quality What is the problem in data quality ? How can we How big is the data make the data quality problem? improvement Direct customer impact ? sustainable? Indirect customer impact? Document new No customer impact? process Set in place monitoring New controls required? What is the fix What is the root cause of to the data problem? the data problem? Design and develop data fix Work around required ? Implement data fix Communicate solution/work around to customers Create known error (KER) 21/10/2010 Page 20
  21. 21. Cost of poor Data Quality (summary) In the period from july 13th till august 13th 2009, ‘x amount’ calls to Customer Service were related to dataquality issues. ‘y amount’ of these calls needed a case to 2nd line. The majority of these calls were related to: Incorrect bills Loyalty (e.g. not receiving gifts or points) Customers unable to use certain services (e.g. outgoing calls, service ‘b’) The total costs for TMNL in 31 days are € z amount of This means that the direct initial costs on a yearly base for TMNL/Customer Service caused by dataquality issues are € y 21/10/2010 amount of Page 21
  22. 22. TMNL Data Quality mission statement 2007 TMNL Mission statement The goal of Data Quality Management is to initiate, stimulate, coordinate and support activities that improve and maintain the quality of data of T-Mobile Netherlands so that data can be trusted and used to support company business processes internally and externally in the most efficient and effective way. Key area’s of the function are: Representing the business interest in data quality for customer, contract and product data in all parts of T-Mobile Netherlands where data quality is involved to ensure that the elements of data management are part of operational processes. =€? Ensuring that the appropriate tools are in place to measure data quality, regularly reporting on the status of data quality and making sure that where there are problems with data quality or inconsistencies and that the appropriate measures are taken to solve them. Evangelise the information culture, represent the right behaviours for a mature information based organization and raising the profile of Data Quality as a business issue by making the business value of data quality clear. Continuously looking for new opportunity areas where data quality can be improved, and gaining the support from the relevant departments to undertake new data quality initiatives. 21/10/2010 Page 22
  23. 23. 21/10/2010 Page 23
  24. 24. Data Management Maturity Models Reward Risk 21/10/2010 Page 24
  25. 25. Maslow Maturity Model: Hierarch of Needs (1943) 21/10/2010 Page 25
  26. 26. Maturity Models Overview 2010 Maslow’s Hierarchy of Needs 1943, psychologist proposed such a model for 5 levels of human needs Richard Nolan’s SGM (Stages of Growth Model) 1970 – 1979; maturity of automation CMM - Capability Maturity Model for Software (also known as CMM and SW- CMM) published by Software Engineering Institute (SEI) and Carnegie Mellon University and defines software development maturity of organizations based on procedures and processes CMMI-SE/SW CMM Integration (CMMI) ; successor of CMM http://iea.wikidot.com/cmmi CMM - ITSM; IT Service Management Data Warehouse Maturity models VDC Maturity Model - Virtual Data Center (VDC) of tomorrow--the data center where virtualization technologies work together to deliver applications. Internet Marketing Maturity Models Gartner's web analytics maturity model presented by Bill Grassman at eMetrics San Francisco is to analyze the vendors themselves in comparison to what they data they can provide. The Architecture Maturity Model is organised into 5 levels, based on the 21/10/2010 Page 26 Carnegie-Mellon Software Engineering Institute’s Capability Maturity Model for
  27. 27. Maturity Models 2010 (continued) New Services Maturity Model technology professional services maturity model The Professional Services Maturity Model The study has been developed to measure the correlation between process maturity and service performance excellence. Project management maturity model Corporate Sustainability: Capability Maturity Model: The first step in developing a sustainability program is to assess where your firm is and where you want it to be on the following five-level corporate sustainability capability maturity model. BPM Maturity Model Alignment to a BPM Maturity model helps to ensure that the overall Organisational BPM intiative is in alignment with a solid internal BPM Architecture Framework. SOA Maturity Model has become a great foundation for companies worldwide who have approached application integration using a service-oriented architecture (SOA). It provides IT decision makers with a simple framework for benchmarking the strategic value of their SOA planning and implementation—and a model for visualizing future success. E-Business Maturity Model 21/10/2010 Page 27
  28. 28. Why do you need a Data Maturity Model? 21/10/2010 Page 28
  29. 29. Data Quality Management From Reactive to Adaptive Data Management 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 Reacti Proactive Adaptive ve Incident &problem Preventive testing & data Make sure new projects and management; Clean/ repair inconsistency monitoring in order to changes are in line with data when problems become proactively business and data model visible identify and correct errors /problems •ITT and UAT testing •Develop business model Clean data manually or via •Logical data model •End to end testing script •Technical data model •Data Acceptance testing •Data Distribution matrix •Glossary of terms •Data Monitoring •Data standard •Create incidents/problems •GUI design standards Find and fix the root •Work around scripts •Interface architecture cause •Business & validation rules 7% Clarif y Qualit y •Contact/channel matrix 6% My T-Mobile BSCS-HLR 5% ADB-BSCS Clarif y - BSCS inconsist ency •Monitor data quality 4% •KPI’s for data quality in SLA & Current focus is on reactive data- 3% PM’s management. Trouble shooting 2% when problems get identified 1% 0% Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 21/10/2010 Page 29
  30. 30. T-Mobile Enterprise Data Management Maturity model 2006 Initial Repeatable Defined Managed Optimizing Reward Risk People – Who is involved and what contributions must they make? Process – What activities must be performed? Technology – What investments in technology must be made? Risk and Reward – What risks does the organization face at the current stage and what could it gain from progressing forward? 21/10/2010 Page 30
  31. 31. The IBM model for Data Management Maturity (2008) Stage1: Uncertainty Stage2: Awakening Stage 3: Enlightenment Stage 4: Wisdom Stage 5: Certainty (ad hoc) (repeatable) (defined) (managed) (optimizing) 1. Strategy and Understanding Execs are not aware of data IT execs support data IT and business teaming on data- Execs support data governance Execs manage data assets as * Executive Interest governance. management. Limited, informal, related projects. Cross-dept financially, incl personal emphasis. driver of efficiency, performance * Alignment of Business and No coordinated information talk on data initiatives. Elements of information strategy in place, Benefits tracked; strategy adjusted and comp. differentiation. Information Strategy strategy. information strategy exist. Initiatives aligned with business strategy. to maximize benefits and support Partners support info strategy. * Communication on Data Projects executed in ad hoc way. coordinate on stand alone basis. Regular communications on data- business priorities. Data is 'talk of the town'. No communication on data-projects projects & results. or results. 2. Organization Business/IT roles in data Data management roles and Roles & responsibilities assigned, Strategic business planning leads Business/IT roles implemented and * Business & IT roles in management and projects not responsibilities in business/IT are not always executed. Business efforts to bring info innovation into adaptive. Information Lifecycle clearly defined. Inconsistent defined. Data management skills directing data mgt priorities. business plans. Deep role- based Changing in- and external * Data Skills, Learning and business participation. Data skills and training available across the IT Consistent development of data training on data mgt in business & environments supported by Training not always available.. organization. skills. IT. ongoing development of in- and external data skills 3. Processes Data collection takes up most of Some data integration. Controls Services-based data apps. Business process integration via In- and external data shared and * Processes for obtaining the time. Sources of data often silo- developed around changes of data Integration of data silos. Key data information services. Data is readily available. information Customer, Service, ed. Information is non¬integrated. definitions. Some common data available. seamless, shared and available Additional sources easily added. Product Data Definition Processes Changes to data are uncontrolled. definitions. Different guidelines and Data management processes throughout processes, enabling High level of standards-centric * Alignment of Business No common data definitions. processes around definitions and rationalized. Common data process innovation. Definitions information definition, creation and Processes and Data Mgt requirements gathering. definitions, shared between shared and centrally managed use across business and IT. business and IT. Controlled changes. 4. Governance No data governance organization, Stronger, informal governance role Governance organization in place. Data governance in place, linked to Governance extended to bus. * Data Governance Org. policies or standards. Data aspects and policies exists. Departmental Standard processes to address data key internal processes. Preventive partners. Prevention has main * Stewardship & Ownership of business & IT projects seldom processes address data aspects aspects of projects. Data action. Deliveries of projects that focus. All demand and supply * Policies & Procedures linked or addressed. of/between IT/business projects. Stewardship implemented. address data aspects are reviewed. processes address data aspects. * Data aspects in No Data Stewardship. Data owned Data stewardship and ownership on Accountability and authority over Data linked to exec. performance. Stewardship extended to bus. projects/processes at departmental level departmental level. data definitions and changes Policies stored and accessible. Partners. Adherence to policies is coordinated. enforced and trained. 5. Data and Data Quality Decisions cannot be made due to Data Quality monitored. Enterprise data architecture Flexible data architecture - Partners managed to use data * Data Architecture & Standards unreliable data : no quality checks. Preventative data quality developed and managed. Quality information as a service. DQ architecture. Master data controlled * Master Data Management DQ Ad hoc efforts to meet quality processes. Ad hoc correction requirements governed by metrics embedded in processes across bus. partners. DQ meets Management needs. Manual effort to coordinate efforts. Loose, not uniform, master business/IT. Processes to validate and systems. DQ approach industry quality standards. Self- * DQ Metrics & Standards master data. Capture of metadata data mgt. Silos of metadata. High data quality compliance. Master adjusted when bus. strategy healing DQ Metadata Management when it adds value. level architectural standards. data owned and controlled across changes. Metadata integrated capabilities. Metadata capturing No version of the truth. Multiple versions of the truth processes and depts. Metadata across processes/technologies. and exchange with business captured and used consistently. Single version of truth. partners. 21/10/2010 Page 31
  32. 32. 21/10/2010 Page 32
  33. 33. Key Elements of Data Maturity Level 1: Ad Hoc (1998 – 2004) Executives are not aware of data management No data management organisation, policies or standards Ad hoc efforts to meet quality needs (project oriented) Level2: Repeatable (2005 – 2009) Full Time Data Manager (role) Some common data definitions Data stewardship Data Quality monitoring “To measure is to know” Level 3: Defined (2009 – 201x) Data Quality Management/Governance processes in place Meta Data Level 4: Managed Data Quality Budget Preventive Level 5: Optimized 21/10/2010 Page 33
  34. 34. DQM maturity timelines Stage 1 “Ad hoc” Stage 2 “Repeatable” Stage 3 “Defined” Data Quality Background Data inconsistency meetings New Data Quality compares Financial effect DQ issues were held were created for CS made visible The first draft for a Customer Data Quality standard was Data inconsistency reports Data Quality Targets First draft on Product- and created were created officialized Contract Data Standard Data Manager role was defined 2002 2003 2004 2005 2006 2007 2008 2009 2010 Data cleaning was started to Data Manager role officialized prepare for customer data Standardization in reporting migration Customer Data Standard realized and officialized Data Quality Dashboard Second Data Manager introduced appointed A start was made with measuring data inconsistencies 21/10/2010 Page 34
  35. 35. Summary: Why do we need a data maturity model? You need to know at what stage you are currently and why you are there (as-is) You can understand the risks associated with undervalued data management practices Help understand the benefits and costs associated with a move to the next stage To improve you have to change the entire culture of your organization – from personnel to technology to management strategies You can accurately set goals for data maturity (and it takes time) This will help you to move to the next stage (to-be) Current Stage + Best practice Roadmap for mature data management 21/10/2010 Page 35
  36. 36. Thank you for your attention. 21/10/2010 Page 36
  37. 37. Any Questions? 21/10/2010 Page 37