By Anna Trichkine, Data Quality Lead
During a period of change, surveys are a go to tool. If my own experience is anything to go by, I would say you’ve probably filled out dozens of surveys over the last year and for most of these surveys you are yet to see the results.
The good news is that the survey results often have important stories contained within them, even if they’re anonymous.
So how can we retell the stories whilst still maintaining anonymity?
The data team at London Borough of Hounslow have recently been tasked with analysing the data for anonymous workplace surveys. These surveys aim to capture how participants feel about working in the borough, and how they feel about working from home. As the data that are collected are anonymous, it is important to try to find patterns or stories in the responses without revealing any personally identifiable experiences. The user profile must not reveal an individual but must provide an insight into a group of people with similar experiences.
How do we do this?
One way to do this is by using decision trees, a type of mathematical model that identifies patterns in your data set by asking true/false style questions.
Using this mathematical model we are able to identify patterns in the data set. The mathematical model takes into consideration all of the questions asked by the survey, and suggests which of these questions are more important. Even if a survey had dozens of questions, it may be that only two or three of the questions have significant differences for the way people respond.
Once these key questions are identified, the data team specify minimum sample sizes for each group to make sure anonymity is maintained.
This combined approach can build a compelling story whilst also making sure that the narrative is anonymous.
Once the model identified the key questions, and the groupings, the team were able to build up a user profile from the grouped responses.
We were able to create 4 main personas, each with unique reflections and experiences of working in the Borough of Hounslow. Underneath these personas were the collective responses for a group of individuals who shared similar thoughts.
Relaying these stories as personas, rather than as graphs and line charts, allows the stories to come to life. We can empathise a lot more with a person, rather than with a line. In this way, the anonymous surveys become more exciting both for our team, and hopefully for those who would like to see the results of the survey.
We will be sharing the personas internally with staff, and would love to hear which persona resonates with you.