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:
1. Client Id
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
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 [email protected]