1. XBRL and Complex Data mapping Paul Snijders CEO Semansys Technologies BV Board member XBRL Nederland Founding member XBRL in Europe Former vice chair XBRL Solutions group Project Manager Architecture NTP

2. XBRL: from a software point of view  XBRL process  Data models in XBRL  Data mapping  Conclusions Important for implementation

3. Presentation is based on cases 90’ties19992000200120022004200320052006 Scripting & Automation XBRL 1.0 Composer XBRL 2.0 Composer XBRL 2.1 Composer Professional XBRL Development Kit Conformance Suite Support XBRL validator XBRL receiving and processing application XBRL Deployment Manager Semansys is the first to offer a complete application suite for digital reporting based on XBRL, enabling organizations to optimize their internal and external financial reporting and business monitoring processes. Semantic Business Intelligence XBRL 1.0, XBRL 2.0, XBRL 2.1, XBRL GL,Conformance Suite, Dimensions, LRR, FRTA, FRIS, Formulas,

4. Projects First European projects XBRL Dutch Treasury NTP, Netherlands Taxonomy Project Water Boards Local Government (60.000 data elements, 150+ contexts) Large bank/Daily Close, (50!!! Dimensions, 500 contexts dynamically generated every day) Banks in Europe (dimensional mapping, conversions)

5. When is data mapping important  XBRL and basic financial data  XBRL and reporting data  XBRL and dimensional data  Mapping with multiple taxonomies  XBRL and data warehousing  XBRL-GL and financial applications

6. Current software products Accounting ERP/GL Logic Business Intelligence Data Warehouse Logic Consolidation Reporting Financial Reporting Logic Tax reporting Tax Software Logic  Each application has proprietary data definitions  Applications have different data models

7. The XBRL reporting process Validation Compliance checking Analysis Taxonomy creation Accounting rules Validation Regulator Taxonomy Data entry Manual Tagging XBRL Report ERP/GL Logic Data Warehouse Logic Financial Reporting Logic Tax Software Logic Mapping Validation Companies XBRL Report Reporting Area of interest

8. XBRL is externalization of data XBRL delivers standardization and externalization of data definition and data validation  Data exchange format  Data definitions also labels, references  Multiple data models Hierarchical, dimensional and relational  Validation rules Externalization of:  Exports, Queries  Data Dictionary  Application data model RDMB, Cubes Impact on

9. Data in XBRL?  XBRL has a complex data structure  XBRL holds many data models  XBRL data is different per output  XBRL has data validation and quality inside  XBRL is NOT a standard chart of accounts  XBRL data mapping is needed

10. Example XBRL Based on Taxonomy Goodwill 3.400.500 Turnover 168920000 Common stock 12.500.000 CEO Bill Gates Profit 45.870.000 Data element

11. XBRL: more than a data definition Label Cash & Cash Equivalents Label Kas en Geldmiddelen Formulas Cash Beginning Balance ≥ 0 References IAS 16, 2, a Presentation Annual report Presentation Balance sheet Indirect Presentation Income statement Presentation Cash flow statement Calculation Cash = Currency + Deposits Currency Euro/US$ M2, Segment2 Prod TV Hifi Segment2 Video Audio Scenario Budget Actual Period FY 2004 Q1, Segment1 Benelux EU Label Comptant et Comptant Equivalents Label Geld & Geld nahe Mittel Label Гроші та їх еквіваленти XBRL Taxonomy XBRL Instance Elements Profit : 45.870.000 Entity: XYZ Ltd

12. Instance document <ifrs:profit contextRef=“Contex1” unitRef=“Euros” Decimal=“0”>45870000 Data Taxonomy iso4217:EUR Unit Virtual Company North 2004-01-01 2004-06-30 Actual Context Definition Simplified

13. Instance data: is a simple star schema C2 C1 Context C3 Unit2 Unit1 Unit Unit3 ifrs-gp_bank ifrs-gp_Cash Taxonomy element ifrs-gp_loan 23.750 112.340 Value 454 Element Data C2 C1 Value Unit2 Unit1 Unit C3 Unit3 Units Reference Entity A Entity Entity B C2 C1 Context C3 31-12-2006 01-01-2006 Period 1-12-2006 31-12- 2006 Budget Actual Scenario Pro forma TV Hifi Products USA Regions Far East Dealers Internet Channel Direct sale Contexts Reference

14. Technical fundament of XBRL XBRL specification 2.1 Schema driven XML language Heavy use of X-link Data structuring Relational data models Multidimensional structures Hierarchical data Flat structures Multiple data representations Fully extensible Taxonomies Data structuring XML and custom data types Presentation & Calculation Important

15. Different data models in XBRL Element 1: 4000 Element 2: 203 Element 3: Amsterdam Element 4: 8000 Element 5: KPMG Simple list + Element 1: 12000 + Element 2: 8000 + Element 3: 4000 Element 4: Address Element 5: Street Element 6: zip Hierarchy Relational 6000 Street 4Address 4 Employee 4 Street 3 Street 2 Street 1 Street 4000 Address 3 Employee 3 1700 Address 2 Employee 2 1300 Address 1 Employee 1 Salary AddressName 600 7507000 Employee 4 560 420 230 400 5000 Employee 3 170 2700 Employee 2 130 2300 Employee 1 Insur. GrossNameSoc.Sec Multi dimensional 200300 Costs 1100012000 Sales 18002700 Profit BudgetActualSales, 2006 April 200024002500Product D 1200 400 UK 10002000Product C 700Product B 300500Product A FranceGermanySales, 2006 Actual, April X X

16. Data mapping  Account  Account - XBRL  Data preparation  Data transformation  Data cleansing  Dimensions  Dimensional mapping  Dimension data transformation

17. Accept Transform Import layer Accept Transform Import

18. Discussion points  Account – taxonomy mapping  Check on processing  Account transformations  Account value mapping  Checks on processing  Dimensional mapping  Dimensional data transformation  Checks on processing

19. Account – XBRL mapping 1 Simple account mapping Account Result 100.100.10 Cash 45.000 Data value mapping IFRS-GP_CashEquivalents 45000 Mapping

20. Account – XBRL mapping Checks  GAP analysis  Is there a match between COA and mapping  No new accounts  Account have same meaning  Checks on processing  Is the correct data set collected  Monthly process closed  Complete dataset, correct query  Is the correct account map used

21. Account transformations Cost centre = NOT used Main account code = used Xxx.yyy.zzz Account transformation 2 yyy needs to be converted to ….. Xxx.yyy.zzz Account transformation 3

22. Data value transformations Account Debit Credit Result 100.100.10 Loan 23.750 12.250 11.500 IFRS-GP_Loan 11.500 IFRS-GP_LoanDecrease 12.250 IFRS-GP_LoanIncrease 23.750 Mapping account values to different Elements Account Result 100.100.10 Cash 11.450,45 Value transformation IFRS-GP_CashEquivalents 11.500 (+ - / * ) Calculations 4 5

23. Data value transformations 6 Mapping based on sign Account value Positive Element A Account value Negative Element B 7 Sign conversion Account value Signs need to be converted to Debit or Credit elements

24. Checks on processing  Correct account mapping  Correct mapping file  Correctness of account transformation  Correct value to element mapping  Correctness of value transformation (calculation)  What to do with calculation roundings  What is the unit of the values (currency)  What is the scenario of the values (budget/actual)

25. Dimensional mapping 8 Simple: Internal dimension = reporting dimension Dimension members = dimensions members Internal dimension External dimension Region Country 9 Different dimension names Internal dimension External dimension Domestic Spain Verenigde Staten USA 10 Different member names

26. Dimensional mapping 11 Internal dimension External dimension Region Country USA Canada Different members North America Internal dimension External dimension Region Country USA Canada North America

27. Dimensional mapping Checks  Correct content  Correct dimension definitions  Correct translations  Completeness  Cross totals over all dimensions  Match with Account mapping  Audit trail, change log

28. Mapping summary  XBRL is a great global standard for financial reporting and business intelligence/Data warehousing  XBRL can contain multiple data models  Data mapping requires attention and specific functionalities  Account mapping, data transformation  Dimensional mapping, dimensional transformation  Audit and control on mapping is advisable

29. Advanced Import Mapping Functionality Taxonomy Mapping DB Mapping Gap Analysis Audit & Control Instance Generation Account Mapping - Simple - data transformation - conditional mapping - sign conversion - Add/replace/subtract values Dimensional mapping - Simple - dimension name change - dimension member change - conversion mapping DB integration - Database integration - automatic load - Multiple database API’s A ccept T ransform I mport Semansys’ software support Specific customization Scripting Data control Audit trail

30. Todo’s  Understand the mapping issue  Investigate current databases  Investigate XBRL reporting data models  Customize and automate the Mapping  Account mapping  Data transformations  Dimensional mappings  Audit and control  Check the mapping and transformations  Implement process control

31. Thank you paul.snijders@semansys.com Free Taxonomy Viewer Visit the Semansys booth