Background

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.

Objective:

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.

How?

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

Others 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]