There was a lack of a simple and automatic way to gather gender disaggregated data. Data gathering was taking place manually and organisation of data was complicated. Little analysis of data was no monitoring of the gender and diversity state of art in the organization was undertaken.
Gathering gender disaggregated data regularly, quantitative and qualitative. Analysing these data in a dedicated Report so as to monitor gender and diversity in the organization
Problem (evidence)
Aims/objectives
To simplify and automate the process of collecting gender data from different databases and make access to data available for everyone. Implement regular analysis of data.
Resources
Workload allocation/time
Specialised knowledge (IT technician)
Software development/specialised knowledge to use the software
Brief outcomes
With the aid of the software the team managed to collect the data needed for the 2018/19 annual gender equality report and presented it to the social board in October 2019.
This report will form the baseline for future gender equality plans.
Key area
The governance bodies, key actors and decision-makers
Type of action
Data gathering and analysis
Organization
Mondragon Unibertsitatea
Higher education institution
Action level of implementation
Researchers/professors and technical and administrative staff, students
Implementation
With the aid of the software the team managed to collect the data needed for the 2018/19 annual gender equality report and presented to the social board in October 2019.
This report will form the baseline for future gender equality plans.
Challenges
The biggest challenge was that university data were divided in different databases and they were collected for different purposes. Some of them have been developed by the university itself and are common to all faculties, but others are external software that each faculty has purchased on its own. It is a challenge to adapt all of them and to design the data gathering process in a periodic and efficient way.
Coping strategies
Discuss with companies which provided external software to adapt the systems and enable them to provide disaggregate data by gender.
Tips/strategies – Lessons learnt
Include a technician/expert in software engineering to undertake such an action and ensure workload recognition for this person.
Ensure liaison between the IT expert and the gender equality team.
Be aware that data are in many different places.
Ensure that introduction of new data will be in a disaggregated way.
The creation of graphs/figures is really important.
Use survey software that disaggregates data by sex.
More detailed Outcomes/Impact
To have a simple and automated process to access and extract disaggregated by gender.
Ensure that an annual gender equality report is produced to monitor progress.
Reflection/What we would do different
Ensure that any new data that we store, is stored in a disaggregrated way. Nobody knows which data will be useful in the future.
Unintended consequences
This will impact the features of the new prp system.