How did you spend your 2016? Use Calendar Data Analytics Tool to find out

TL;DR

While reviewing last year’s activities, I built this tool to look at how much time I spent in meetings vs. working. It surfaced interesting insights on who I met and how often.

Link to Tool: http://xpertly.co/Calendar

After building it, I also wanted to make it available for others to use as well. Would love feedback in three forms (a) what was insightful about your data? (b) bugs and © feature requests. Use the comments section below to share your thoughts. Alternately, you can also email me at emad [at] xpertly.co

Disclaimer: This only connects to Google Calendars. This tool does on-demand analysis and does not store any data on the server or the device. Since this a fun side project, I cannot provide ongoing support to maintain this tool.

Motivation

I was interested in computing how much work I did last last year. More importantly I was curious about my ‘meetings’ to ‘work time’ ratio. In the past, this analysis would have been difficult because working at large companies you don’t get access to Outlook Exchange data (at least not easily). For the last year or so I have been using Google calendar diligently for my meetings.

In addition, in my recent chats with HR specialists the topic of employee time-spent, engagement and communications analysis kept coming up. It was clear that companies have little idea on how much time is wasted in meetings and task-switching. I wanted to build a tool that could surface some of this data.

A key goal with Calendar Insights Tool was to build a proof-of-concept that shows how knowledge about your time can be super helpful and actionable. It not only uncovers information about how much time is spent in meetings but also surfaces opportunities about which meetings and follow-ups that are falling through the cracks. The tool can eventually recommend which interactions to skip, which ones to follow-up on and suggest new people to meet.

How I built this tool over one weekend

This was a relatively simple project since I only had to access one data source. However, I did think of ways the tool can be made more complex if you further connect calendar data with LinkedIn or company directory to surface even richer insights.

I used a combination of the following tools: R, RShiny, Google API, Highcharts and a nifty little package called GoogleAuthR that helps glue some of this together. Thank you to Mark Edmondson for writing some amazing open source packages that made this easy. Only thing I wasn’t able to find was an API call for Rapportive which could have been very cool.

If you’d like to build sometime similar, drop me a note and I can share the code.

Key Metrics

Here are the key metrics that this dashboard can answer about your time.

  • Number of actual meetings where there were more than two attendees present [Vanity]
  • Time spent in hours [Vanity]
  • Average number of meetings per day [Actionable if too high or low]
  • Percentage of total time spent in meetings [Actionable if too high or low]
  • Number of people met during meetings [Vanity]
  • Number of unique people met [If you want to grow your network, pay attention to this number]
  • Distribution of average number of attendees for meeting [Tells you if you prefer small meetings or larger groups]
  • Time trend of number of meetings and people met [Vanity]
  • Distribution of how often you meet someone [Are you following up enough?]
  • Meeting distribution by time of day and month [Are you minimizing task switching and paying attention to seasonality]

Insights

Over the course of last eight months I have had 672 meetings and spent 571 hours in meetings. One cool feature to add would be to split out internal vs. external and one-time vs. reoccurring meetings. The data suggests that I have met with 216 unique people. Here are my summary statistics:

Over 41% of my time is spent in meetings.

Below is the graph of my meeting distribution of number of attendees in my meetings. Most of my meetings included two additional people. After digging into this more it turns out that these were internal reoccurring meetings with my team members.

Another interesting graph shows the kind of meetings I tend to do. I expect this to be fairly similar to most other people with a high number of 30 minute and 60 minute meetings.

One of the things that was revealing through this analysis that I am not the best at following up with meetings. For example the chart below shows that I have met 178 people just once. And, there are no more than 17 people that I have met more than three times.

The bulk of my meetings tend to happen between 9 AM and 5 PM with a slightly higher emphasis on morning meetings. That is by design because I try to group my meetings together to minimize task switching.

Conclusion

It was very interesting to see where I spend most of my time. Also useful to see recommendations on who to follow up with and which contact to set up reoccurring meetings with. Would love to hear if anyone else who uses Google Calendar diligently finds some of these metrics insightful.

Find out how you have been spending your time: http://xpertly.co/Calendar

P.S. Please suggest other cools tools, articles, book or tips for time management. I’ll add those to the resources section of the tool

One clap, two clap, three clap, forty?

By clapping more or less, you can signal to us which stories really stand out.