1. Creating a Data Governance Framework for Solvency II Copyright © 2011 DataFlux Corporation All rights reserved

2. A combination of Technology, Methodology & Services Developed by DataFlux through working with 10 insurers on early-stage Solvency II Data Governance initiatives The purpose is to provide both the technology required for Solvency II Data Governance and also a starter kit of examples built on a best practice approach What is the DataFlux Solvency II Data Governance Framework?

4. Completeness

5. Appropriatenesscorrection

7. Completeness

8. Appropriatenesscorrection

9. DataFlux Solvency II Data Governance Framework - Methodology 6-Step Approach to measuring & reporting Accuracy, Appropriateness & Completeness Data Governance Project Document templates Project Plan Mapping documents Design Brief DataFlux Solvency II Data Governance examples Dashboard with 3 reporting levels DQ Business Rule examples Data Job templates Best practice Scoring Methodology for measuring data quality by dimensions (Solvency II Data Governance does not include providing a Solvency II model, model scoring, SCR/MCR calculations, model reporting)

10. DataFlux Solvency II Data Governance Framework - Methodology Assess data landscape – Portfolios, Risk domains Identify list of fields to calculate SCR/MCR (Data Dictionary) Identify data governance requirements (Business Rules) Verify Data Dictionary against data sources Describe the data governance checks (Business Rules) Design project landscape (including population of staging area) Populate staging area Apply data governance checks Populate metrics repository Report on results of data governance checks Correct information (Manually and/or automatically) Perform a root cause analysis and initiate improvement/preventative actions The 6-step approach

11. 1. Define DataFlux Solvency II Data Governance Framework - Methodology Assess data landscape – Portfolios, Risk domains Identify list of fields to calculate SCR/MCR (Data Dictionary) Identify data governance requirements (Business Rules) Key documents: Landscape Diagram Description and Process diagrams of source systems Project Plan Identify the components, resources and delivery timelines Data Dictionary Detail the table & field names/types within the systems in scope Process document Functional description of data governance approach for each Portfolio or risk category in scope Mapping documents Using the data dictionary, map the required fields through the different processes from source to target Reporting Requirements Contains the design of output reports

12. Collaboration tool for business & I.T. users Document the data dictionary Promotes auditability Link the dictionary to rules, processes and external documents Track lineage Business Data Network DataFlux Solvency II Data Governance Framework - Methodology

13. 2. Design DataFlux Solvency II Data Governance Framework - Methodology Verify Data Dictionary against data sources Describe the data governance checks (Business Rules) Design project landscape (including population of staging area) Data Assessment Verify assumptions made when creating the data dictionary identify data issues that will be monitored using data governance checks Design data governance checks (business rules) based on: The detailed data requirements to support SCR/MCR calculation The data requirements to assess confidence in terms of accuracy, appropriateness and completeness Design the staging area support for: Data Quality Assessments Required metrics for data governance reports Design processes to populate staging area Migrate data to staging area Transform, cleanse and standardise data

14. DataFlux Solvency II Data Governance Framework - Methodology

16. Retrieve data from sources and populate into staging area

17. Apply data governance checks

18. “Data Governance Gateway”

19. Apply business rules against source data

20. Populate metrics repository

21. Store the business rules results in repository

22. Score the results of applying the business rules according to Appropriateness, Accuracy and Completeness

23. Aggregate data for different reporting levels

24. Productionise processes

25. Deploy to server

26. Set up processing schedules

28. 3. Apply DataFlux Solvency II Data Governance Framework - Methodology Populate staging area Apply data governance checks Populate metrics repository

30. 4. Publish DataFlux Solvency II Data Governance Framework - Methodology Report on results of data governance checks Company name and logo Data Dimensions in scope will appear. If out of scope, background will be grey Business Unit list Summary Level Data Feed list Detail Level – Appears when data is selected on Summary level Data dimension list

31. 5. Correct DataFlux Solvency II Data Governance Framework - Methodology Correct information (manually and/or automatically) Solvency II allows for the correction or enhancement of data in order to improve its quality with regard to calculating SCR These must be auditable and fully documented DataFlux Data Jobs: apply corrections to data built using graphical interface, not coding self-documenting with ability for notes full logging/audit trail

32. 5. Correct DataFlux Solvency II Data Governance Framework - Methodology Correct information (manually and/or automatically)

33. 6. Improve DataFlux Solvency II Data Governance Framework - Methodology Perform a root cause analysis and initiate improvement/preventative actions Analyse potential causes of data quality issues Internal business processes System processes Lack of data entry controls External data Integration touch-points Initiate improvement/preventative measures Business processes Training Real-time DQ controls on data entry Data governance rollout across the enterprise

35. Completeness

36. Appropriatenesscorrection

37. DataFlux Data Management Studio DataFlux Solvency II Data Governance Framework - Technology Create business rules in central repository Apply data monitoring jobs to conduct ongoing Data Governance Build DQ/DI jobs & services Conduct in-depth data assessment Modules: Essential: Profile, Monitor Potential: Integration, Quality, Entity Resolution

38. “Productionise” for business-as-usual processing Centralised repository Execute on Windows or Unix/Linux All processes are executable in batch or real-time Integration with other applications and environments is via SOA (web services) or via API (C, C#, Java, etc.) Security layer provided by the DataFlux Authentication Server Modules: Essential: Profile, Monitor Potential: Integration, Quality, Entity Resolution DataFlux Data Management Server DataFlux Solvency II Data Governance Framework - Technology

40. Define user groups

41. Email notification and email report delivery (HTML, PDF, Excel)

42. Access and privilege controls

43. Deploy reports on corporate network

45. Next Steps Solvency II Data Governance planning workshop Technology demonstration on your data Review of current project status Planning for next steps – how to adopt the DataFlux framework Launch phase Initial project plan Define the Solvency II Data Governance team Install technology Commence work on 6-step methodology Joint validation of approach with FSA