Author: Anna Trichkine

How to get the most out of your anonymous surveys

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.

Results

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.

First step towards data science collaboration

By Ejaz Hussain – Data Scientist and Anna Trichkine – Data Quality Lead

An exciting learning Journey with LOTI and ONS.

The Data Science and Data Quality Team at London Borough of Hounslow is a newly formed team within Digital and IT. The team is made up of Lead Data Scientist Ejaz Hussain, and Data Quality Lead Anna Trichkine.

As a new team, we will be working to improve data practices and data ethics within London Borough of Hounslow. We will be exploring new opportunities for how to use our data and how to make sure we are making more data-enabled decisions across the borough.

To begin with, we have been selected to join the pilot data science and machine learning programme run collaboratively by ONS and LOTI.

Who are ONS?

The Office for National Statistics, the government department specialising in everything data and statistics!

Who are LOTI?

The London Office for Technology and Innovation, working to support collaboration between 33 London local authorities.

What is the programme?

The programme focuses on developing the team’s expertise in data science, specifically to improve the quality of local government data by using programming languages; R and Python.

This is an 8-12-week programme and we will be meeting with the ONS mentors on a weekly basis.

During these weekly sessions, we review data projects together with the mentors along with other local authority participants who have also been selected to join the programme.

A picture of our first session together is shared below.

Zoom call screenshot of first data science programme session

How will this benefit London Borough of Hounslow?

We will be taking the learning from these sessions and sharing the tools and techniques with other analysts across the council.

We have been given access to the ONS learning pool, a hub of well-prepared learning materials, that we can share with staff across the council.

If you are interested in accessing this content or to find out more about our sessions, please email us and we will be happy to guide and support.

Thank you to ONS and LOTI for this exciting opportunity.