Data Quality Project


Everyone is interested in the insights and value they can derive from data. Data can lead to better decision-making across an organization. But data is only useful if it is high quality. Bad data is at best inconsequential. In the worst-case scenario, it can lead to costly mistakes. 

CLCs Australia is focused on delivering the right information and support for centres to have the correct data for operations, planning, reporting and advocacy. To support this approach, CLCs Australia is planning to conduct a Data Quality Project on the national dataset.


The objectives of this project are to:

  • Give the sector feedback on the quality of data held in CLASS without compromising confidentiality and security of clients and centres
  • Improve the quality and consistency of data collected by CLCs nationwide in CLASS, particularly in relation to those fields used in reporting​
  • Add value to the National Dataset

What will we do?

This project seeks to analyse the quality of data within the CLASS national dataset by identifying 28 critical fields and checking how often those fields are missed. 

CLCs Australia will then share the aggregated results as feedback in training to centres, where appropriate make changes to CLASS and give options for ways to enter the values in fields correctly as often as possible. CLCs Australia would also like to provide centres with their own Data Quality Dashboards to monitor quality.


This project provides a quick look at the overall quality of following data items. The critical parameters for this check includes:

  • Percentage of unknown or blanks
  • Any obvious gaps
  • Anything that seems odd or unexpected in the dataset

Data Items/fields to be quality-profiled:

Client Demographics

1. Client Id

2. Location

3. Age

4. Gender

5. ATSI Status

6. Interpreter/Translator required

7. Main language Spoken at home

8. Financial disadvantage indicator

9. Disability Status

10. Homelessness status

11. Employment Status

12. Centrelink status indicator

Service Characteristics

13. Family violence indicator

14. Service Type

15. Law Type

16. Primary Law Type

17. Problem Type

18. Mode of Service Delivery

19. Client in custody Status

20. Income level


21. Reason for Referral

22. Referred To

23. Family Type

24. Relationship status

25. Country of Birth

26. Proficiency in English Spoken

27. Mode of Service Delivery

28. SA4 Tagging

The following dataset shows an example from a data quality improvement project we ran in South Australia.  As you can see Centre 1 has many blank values for disability status. This exercise allowed centres to identify the disability field as one with uneven reporting and generated a discussion around removing the option for blank values from CLASS for this field.

How can you be involved? 

Your feedback is very important to us. So if you have any feedback, please send us an email to