Data & Technology

Thinking about emails

Last week in line with collecting data using technology, I was also tracking work emails for my visualisation. I wanted to understand how many emails were coming in and going out as well as how many of those are related to calendar invites.

In a previous post when looking at my music habits, I realized 50% of my time is spent in meetings. So when looking at emails, rather than trying to categorize them into several buckets, I wanted to simply track which ones were invitations to meetings. Below is my visualisation for attempting to track my work emails.

Work emails for the week – incoming vs outgoing from a calendar invite perspective

My work doesn’t require me to spend a lot of my time in email, rather the organization prefers using Slack to communicate. In this shift to Slack at the start of the pandemic, a lot of emails have transitioned to Slack. This left me with a total of 86 incoming emails last week with 32 of those being related to a calendar invite. That means 37% of my incoming email is a calendar invite.

For the visualisation, I decided on squares to represent an incoming email. If the email was related to a calendar invite, I added a ‘C’ to the square. For sent emails, I chose circles. An ‘S’ inside the circle represents a calendar invite that I responded to (rather than a sent email). The vertical columns read Monday to Sunday, left to right.

One of the first things that I realized when embarking upon this week’s visualisation is that I am tied to the idea of when something happens in the data collected. Looking back, each one of my visualisations is related to a time interval. Only adding squares and circles as they came in without a dimension of time seemed almost frightening; hence the sun rising, midday sun, and moon on the left hand side.

In reflecting on this throughout the week, I am coming to the realisation that this is most likely because I try my best to have a work life balance – a time for work and a time for ‘me’. For example, I have a separate work phone and computer, so once they’re switched off, they remain switched off until the next work day.

As you can see in the visualisation, last week’s incoming emails were consistent throughout the day, even with some extending late into the evening on Friday. As I work for a US-based company, my inbox often collects emails after UK work hours. The one thing I can visually see in this data collection is that my direct colleagues are great at not sending emails on the weekends unless it’s necessary.

While I set out to only look at calendar emails, I realized this week that the calendar emails often come along side a ‘case’ email, meaning that the calendar invite is only 1% of the process. The rest of the 99% is the prep that leads up to the meeting with the customer and that information is in the case email. In hindsight, I should have also tracked how many case emails I received and even perhaps color coded the emails to relate to a specific customer meeting.

This reflection left me thinking back to the idea that “…’what counts’ as education when it comes to digital data is what can be counted” (Williamson, 2017, p. 46).

What if what counted for me was emails because this is one of the few things that can be counted in my work?

Or, alternatively, the number of meetings I attended? If this was the case, I would most likely have a poor performance.

Williamson, B. (2017). Big Data in Education: The Digital Future of Learning, Policy and Practice.