1. Troy Eversen | 19 May 2015 Data Integrity Workshop

2. 2 DATA INTEGRITY dirty data accuracy completeness quality business decisions errors consistency reporting reliability mistakes punctuation incomplete inaccurate data cleanse out dated facts transpose automation analysis paper based results proof credibility checking time over payment manual entry

3. 3 What is Data Integrity? »Data Integrity refers to the overall accuracy, consistency and completeness of data. »Data Integrity is enforced in part by rules and procedures within Alesco and Web Self Service (e.g. Preventing duplicate entries), however, there are other ways the accuracy of your data can be compromised. »Data Integrity is critical as data is frequently used for business decisions – Bad data can lead to bad decisions

4. 4 What is Data Integrity? »When Data Integrity is compromised the data cannot be reliably used – Compromised data is dirty data. »Dirty data is data that contains errors such as spelling or punctuation errors, incorrect data associated with a field, incomplete or outdated data »Dirty data can be difficult to indentify and sometimes difficult to correct if left unmanaged.

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6. 6 Understanding the source of “dirty data” Dirty data can come from any number of sources and for any number of reasons. These include but are not limited to: »Manual processing errors »Inaccurate interpretation of requirements »Inconsistent entry of “non-mandatory” information »External data sources (Custom Interfaces / API Bulk Loads)

7. 7 Manual processing errors »Paper documents anywhere in the processing chain that require manual input into Alesco can add to the number of errors. »Paper forms add to delays in processing that can cause inconsistent or incomplete results. »Users transposing numbers or incorrectly entering data. »User interpretation of data and requirements. »Users skipping through non-mandatory fields or entering data to get to the next screen.

8. 8 External data sources »Different systems have different inputs and outputs and are set up differently. Miscommunication can happen between systems just as easily as it does between people. »Miscommunication of changes across different applications and systems within the organisation. For example, if a general ledger account number is closed in a finance system but not in Alesco. »Incorrectly formatted or prepared load files.

9. 9 Alesco OUT IN You only get out what you put in... OR

10. 10 Unfortunately there is no magic button......so where do you start?

11. 11 Maintenance of configuration Proper maintenance of configuration goes a long way to preventing “dirty data” »End date old codes to remove them from LOV’s. »If a code is no longer required don’t be tempted to re-label it and use it for another purpose. For Example: End-date old positions – don’t recycle position numbers for different roles (this can cause havoc with reporting). »Decide on a single source of truth where multiple systems contain similar information.

12. 12 Maintenance of security »Control who has access to update the system and put in place a framework for new configuration (e.g. Naming conventions and structure for codes etc) »User security is a must! If a user does not need access to certain data for their role, why give them access? »Its easy to give a user access to the “MAS” security group but fixing errors is not. Create role specific security groups »If people move out of a role, update their access to reflect the change – The same applies for terminated staff.

13. 13 Make fields fit your requirements with UI Configuration »If users are skipping key fields required for reporting etc., consider making them mandatory with UI Configuration. »Re-label fields to use naming that is more meaningful to your business so users know the purpose of the field. »Different UI Configuration labels can be set up for different groups of users (e.g. employees in countries).

14. 14 Make fields fit your requirements with UI Configuration Also available in WSS

15. 15 Prevention is better than cure... Education on the importance of data accuracy »Often one of the main reasons for data not being entered into Alesco is the lack of understanding of why the data is required. »Educating on what the data is used for, and issues caused by errors or missing data, will assist in user awareness and reduce error rates.

16. 16 Incorporate user help into Alesco

17. 17 Incorporate user help into Alesco User presses F1 or

18. 18 Is your data open for interpretation?

19. 19 User-defined Business Rules »Alesco offers powerful functionality to create custom business rules. »Business rules can be created for a variety of purposes and can be used to validate and prevent common data integrity issues around insert and update. »Business rules are core functionality maintained in Alesco via FG376 and FG377.

20. 20 WARNING With great power comes great responsibility Business rules are complex functionality and require a strong understanding of sql, pl/sql and Alesco table structures. Business rules create triggers in the database that can disrupt key processes if not configured correctly. Always thoroughly test functionality before implementing in Production.

21. 21 Example business rule »A new rule could be established to prevent the creation of codes with specific characters that are known to cause issues with processes or third party interfaces. The rule is set up with a list of restricted entries

22. 22 The rule is attached to the Alesco table and a record trigger is created On insert / update of a record, the rule is checked and entry of data is rejected where the rule fails validation.

23. 23 Some degree of errors are inevitable... »Timely feedback for correction of errors is important »One idea is to use email notifications via FD067 to check data that is frequently misinterpreted, incorrectly entered or skipped. For example: ›Set up an email notification to advise when a user has entered an old Company Level code. ›Email an employee when they have not provided specific EEO information.

24. 24 Timely feedback for correction of errors »If users and employees are made accountable for their own “dirty data” they might be less likely to repeat the same mistake again.