20100421 Dg2010 Case Study Abn Kester De Vylder V1 4 Final As Presented

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Paper presented in the London Data Governance conference on 21st of April 2010 By Theo Kester, DQ Manager ABN AMRO, theo.kester@nl.abnamro.com Thibaut De Vylder, CEO Deployments Factory

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20100421 Dg2010 Case Study Abn Kester De Vylder V1 4 Final As Presented

  1. 1. CASE STUDY: Data Quality Continuous Improvement Processes at ABN Amro By Theo Kester, DQ Manager ABN AMRO, theo.kester@nl.abnamro.com Thibaut De Vylder, CEO Deployments Factory, tdv@deploymentsfactory.com DG2010, London, 21st of April 2010 1
  2. 2. Agenda PART 1 – THE ISSUES 1.1 - Data Governance challenge in a simple theoretical model 1.2 - Data Governance challenge in the real world PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION 2.1 Data Quality Management Framework: 3.1 Major data quality dimensions Basic Principles 3.2 7 modules to make DQ come true 2.2. Data Quality Organizational Framework 3.3 1 additional module to make FORECASTING 2.3. Relationships among Organizational layers come true 2.4 Issue Management 2.5 Cost of non quality in Basel 2 2
  3. 3. PART 1 – THE ISSUES PART 1 – THE ISSUES 1.1 - Data Governance challenge in a simple theoretical model 1.2 - Data Governance challenge in the real world PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION 2.1 Data Quality Management Framework: 3.1 Major data quality dimensions Basic Principles 3.2 7 modules to make DQ come true 2.2. Data Quality Organizational Framework 3.3 1 additional module to make FORECASTING 2.3. Relationships among Organizational layers come true 2.4 Issue Management 2.5 Cost of non quality in Basel 2 3
  4. 4. 1.1 - Data Governance challenge in a simple theoretical model Data are transferred, stored, extracted, prepared, calculated and reconciled several times before being reported. A long and risky journey ! Operational Real World systems t1 tranfer Central Chains A t2 storing t3 extraction t4 preparation t5 calculation t6 reporting B C D E F G Information presented in report G depends on succession of embedded transformations Quality of G = Quality of [t6(t5(t4(t3(t2(t1(data in operational system A)))))))]  Substantial part of data may be lost or deteriorated during the process ! 4
  5. 5. 1.2 - Data Governance challenge in the real world Reality is even more complex A t1 transfer  Duplication of stores t2 storing t3 extraction t4 preparation t5 calculation t6 reporting  Many chains run in parallel B C D E F G  Reconciliations t3’ extraction t4’’ preparation t5’ calculation t6’ reporting between chains  Human factor D’ E’ F’ G’  Re runs t1 transfer T3’’ extraction T4’’ preparation T5’’ calculation T6’’ reporting  Errors and manual D’’ E’’ F’’ G’’ corrections t2 storing t3 extraction t4 preparation t5 calculation t6 reporting  ... H I J F L M t3’ extraction t4’’ preparation t5’ calculation t6’ reporting J’ F’ L’ M’ Complexity is exponential 2 types of risks  Internal risk : availabiliy of right information for management decisions  External risk : inconsistent reporting to third parties 5
  6. 6. PART 2 – ADAPT THE ORGANISATION PART 1 – THE ISSUES 1.1 - Data Governance challenge in a simple theoretical model 1.2 - Data Governance challenge in the real world PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION 2.1 Data Quality Management Framework: 3.1 Major data quality dimensions Basic Principles 3.2 7 modules to make DQ come true 2.2. Data Quality Organizational Framework 3.3 1 additional module to make 2.3. Relationships among Organizational layers FORECASTING come true 2.4 Issue Management 2.5 Cost of non quality in Basel 2 6
  7. 7. 2.2 - Data Quality Organizational Framework Central governance (a.o. DQCC) BAU, “domains” 8
  8. 8. 2.3 - Relationships among Organizational layers CFO (lead of MB) EVP of Finance organization DQ Management (chairman of Center Relevant EVP and DQMC) SVPs of BUs • DQCC reports to EVP Decisions of DQMC are • Provides support funcion for DQMC communicated to DQOC or (agenda, minutes) product chain meetings DQ Head DQCC DQ Business Competence (chairman Operations process DQOC) chain meetings Center Relevant VPs SVP, relevant of BUs BAU people DQ people within BUs / domains 9
  9. 9. 2.4 - Issue Management  Importance: – Data Quality issues must be fixed as early in the data logistical chain as possible as the graph below will show – Studies prove that the costs grow exponentially while data progress through the data logistical chain  Goal: solutions, not issues  Process: As Data Quality is being analysed and checks are performed issues will be identified. The issues are addressed by the Issue Management Team (part of DQCC) in cooperation with the domains. All issues are given a priority, a deadline and addressed to an action owner.  Tools: – Formalized Issue Management process – Quality Centre: A tool in which DQ issues are logged and managed – Prioritisation Tool: A tool which is used to prioritise the DQ issues – Issue Management Process Guideline: A guideline for the domains how they could set up their own Issue Management Framework 10
  10. 10. 2.5 - Cost of non quality in Basel 2 DQ versus the calculated, reported and real RWA/EC I. Calculated RWA / EC without corrections Is already realised, RWA but not structural or EC and opaque. Goal DQ infrastructure I. Can only be II. realised by II. Reported RWA / EC with current means of a workarounds infrastructural improvement. III. III. Real RWA / EC I. The monthly calculated RWA/EC is volatile; This is not the result of a changed risk profile, but due to Data Quality defects as a result of changes in systems, reference tables and changes in the business and so on. II. Many of the irregularities are manually corrected, which results in a more stable monthly reported RWA/EC. Due to the “defaulting” rules is line II lower than line I. However these corrections are often not robust, opaque and could lead to incompliance. III. The real RWA only changes as a result of changes in the risk profile of ABN AMRO. The real RWA is lower than the reported RWA, because many Data Quality issues can only be solved by means of changes in the system- and IT- infrastructure and not by manual corrections. 0 Time Goal: Aligning of calculated and reported RWA/EC with the real RWA/EC 11
  11. 11. PART 3 – ENABLE THE ORGANISATION PART 1 – THE ISSUES 1.1 - Data Governance challenge in a simple theoretical model 1.2 - Data Governance challenge in the real world PART 2 – ADAPT THE ORGANISATION PART 3 – ENABLE THE ORGANISATION 2.1 Data Quality Management Framework: 3.1 Major data quality dimensions Basic Principles 3.2 7 modules to make DQ come true 2.2. Data Quality Organizational Framework 2.3. Relationships among Organizational layers 3.3 1 additional module to make FORECASTING come true 2.4 Issue Management 2.5 Cost of non quality in Basel 2 12
  12. 12. 3.1 - Major data quality dimensions Accuracy Completeness Integrity & Bus. Rules Operational Real world systems A Central Chains B C D E F G // Chains D’ E’ F’ This Month Month - 1 Month - 2 Quarter - 1 Consistency Consistency Consistency Intra-chain Inter-chains Cross-Months 13
  13. 13. 3.2 – 7 modules to make DQ come true LOCAL  Module 1: Launch data CENTRAL CHAINS quality actions in the local COLLECTOR CHAIN 1 systems  Module 2: Measure the data CHAIN 2 quality sourced in the collector & feedback to the CHAIN 3 sources DQ DQ  Module 3 : Define common OPERATIONAL SOURCED measures (thermometers & SYSTEMS DATA 1 2 3 THERMOMETERS & KPI’s PRODUCTION KPI’s) across the chain(s) CUBE PREDICTION TOOL (AS IS)  Module 4: Create an 4 5 aggregated multi-sources /multi-periods reporting environment 6  Module 5: Challenge the DQ INDUSTRIAL BACK OFFICE results produced in the Reporting layer chains  Module 6: Industrialize the DQ PREVENTION, ANALYSIS & CONTROL production of the deliverables (reports, referential, distribution)  Module 7: Industrialize the DQ IMPROVEMENT & COMMUNICATION DQ analysis & follow-up of 7 issues DQ INDUSTRIAL FRONT OFFICE 14
  14. 14. 3.3 – 1 additional module to make FORECASTING come true LOCAL CENTRAL CHAINS  Module 8 : Evaluate the impact of scenarios COLLECTOR CHAIN 1 based on the evolution of the parameters CHAIN 2 (stress, simulations, CHAIN 3 senticity analysis..) & Store results DQ OPERATIONAL DQ SOURCED  Module 7’ upgrade: SYSTEMS 2 DATA Industrialize the DQ 1 THERMOMETERS & KPI’s 3 PRODUCTION information AND CUBE PREDICTION TOOL (AS IS) forecasting analysis 4 5 SIMULATIONS SIMULATION STRESS TESTS CUBEs (FUTURE) 6 SENSITIVITY 8 DQ INDUSTRIAL BACK OFFICE 8 Reporting layer DQ PREVENTION, ANALYSIS & CONTROL Effective DQ can help organisation FORECASTING to forecast their DQ IMPROVEMENT & COMMUNICATION future potential 7 states. DQ INDUSTRIAL FRONT OFFICE 15
  15. 15. Thanks to… 16

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