Making data work for you

28 March 2022

UQ collects a variety of data that can be used to improve teaching and learning, research, delivery of services, and more. To accurately and effectively utilise this data, it’s important to understand how to read it. Find out what to remember when presented with information.

Understand what is excluded

Data completeness is an important principle, as omissions can impact on data quality. Regardless of whether they’re unintentional or not, ‘gaps’ in information impact decision making, as they leave out crucial points for understanding. For example, course coordinators may be evaluating how they can assist students. However, their data set includes the program of study for each student, but not whether they have a Student Access Plan (SAP), which discloses circumstances that impact on their ability to study. Without this information, the objective of studying the data cannot be fully achieved.

Highlight and understand assumptions made

Within data, there may be several assumptions made. If these assumptions aren’t understood, they can lead to misinterpretation of the information, which can negatively affect decision making made using the data. For example, you may be surveying the number of full-time, part-time, and casual staff currently within your department, to evaluate where further hires could be made. However, the quantity of full-time-equivalent staff stated may not be reflective of those currently working. As a result, you would need to search for further information, to include those presently on leave (e.g. maternity, sick, stress, personal, long-service). As a result of accounting for this potential assumption in the data and receiving a more accurate perception of the present state, more informed decisions about staffing requirements can be made.

Understand what is included

As with omissions, data quality is impacted by what information is included. Unnecessary inclusions of information in certain situations can create (conscious or unconscious) biases, leading to unfair judgements being made. For example, when providing information about potential PhD candidates to an assessment panel, think about what data is necessary to include. Information regarding gender, ethnicity, and disability is not required for the assessment process, and could lead to biased judgements.

Learn how to use data ethically

Data Strategy and Governance has developed a variety of resources for using data ethically, so you're able to identify the situations where it shouldn't be used. Access these resources and sign up for training on our website. You can also find out more about ethical data use by reading our previous blog.

Where can I learn more?

To find out how to get the most out of your data, check out our website, and sign up for our various training offerings