Workwalk

Project description

The team behind Workwalk wanted to improve the usage of Workwalk after running a pilot on the Fontys campus. During this Pilot the booking data was gathered and put in a excel file.
With this data they came to our team Datalution to see if we could utilize this data and come up with improvements or conclusions through analyzing the data.

The main objective of the workwalk initiative was improving activity during work hours. The main focus of our project was looking for variables that would improve the amount of people going on a workwalk. This could vary from pushnotifications or stimulus to promoting the project or giving it more exposure.

Context

The workwalk project is focused on students and teachers of the Fontys campus, the next pilot will be held at the high tech campus.

During the pilot a selective group is making use of the option to go on a walking meeting, this is done for research purposes. The people in the pilot have been interviewed after their meeting to find drivers and barriers for going on a walking meeting.

In the Netherlands we are leading in the amount of time spent sitting down, on average 9 hours a day! This is a general health problem since a study has shown that this leads to a higher risk of heart and vascular diseases. Although studys show that we as a country are not lazy and do a lot of sports in our freetime this still doesn’t make up for the time that we spent sitting. They say that sitting is kind of like the new smoking.

The solution for this lies in light movements during the day, like cleaning the house or making a small walk to one of our colleagues, but also with an initiative like a walking meeting.

The barriers that were found to this were the weather and taking notes, people tend to not go on a walk when its raining and also find it difficult to remember what was spoken about without taking notes.

Results

Our team has made a finished product in tableau, this contains visualizations that were made with the booking data that was handed to us. It also contains external information about the weather that we included.

Our challenge was to come up with advices on how to increase the usage of workwalks, we did so by analyzing the data and to look for outliners in the data. These were found by plotting the data in different graphs.
With these graphs we created a tableau storyline with the most important data for the research team of workwalk. some examples of insights we gave were most used meeting points, meetings during working hours and the total meetings that have been done during the pilot. The dashboard has been made interactive with different kinds of filters like the different months and years so users can zoom in on specific information. After the analysis of the booking data the weather data was merged and analyzed to look for any correlations between the weather and the amount of meetings.

We analyzed 3 variables of the weather:

• total precipitation
• temperature
• wind speed

These have been used to create a weather score for each meeting.

Our cleaning process has been registered so the steps are clear for usage in the next pilot, these steps are made for the dataset we received so the reproducibility depends on the structure of the new dataset.