One of my New Year’s resolutions is to run at least 20 km per week. Strava tells me I am currently going to fail unless I manage to hit 40 km in the next few days. On the bright side, one of my other resolutions was to blog every Tuesday and so far I have managed to keep that up. To celebrate I figured I would spend some time looking at the data behind my first ten posts:
#1 Have Data, Want Impact
#2 Mission Over Task
#3 Why You Should Care about Analytics
#4 Analytics Value Chain: Insights to Action
#5 Navigating Data & Analytics Technologies
#6 Starting with the Right Question
#7 Bias, Diversity, and Profitability
#8 Zero to Impact in a Week
#9 Choosing Metrics that Matter
#10 Data Visualisation: No Tools Required
The most popular post on this list was #10 and sadly the least popular was #7 by a factor of roughly four. The latter saw the least engagement in two ways. Firstly, it had by far the lowest click-through rate from LinkedIn (3.3%) and secondly, the lowest ratio of likes on LinkedIn to views on LinkedIn (1.0%).
For comparison, while #8 had exactly the same number of views on LinkedIn as #7, it ended up with twice the number of reads thanks to double the click-through rate. Given both of these ratios were at least 50% higher for every other post it does raise the question if the average reader is interested in posts about bias and diversity, or whether this post simply needed a better title.
What is interesting is that while the readership of these ten posts has varied quite a bit, the ratio of views on Medium to reads on Medium is almost always precisely 69%. Consistently one in three people loads the page and thinks “Nah, what was I thinking, reading about data!” and then leaves. It is interesting to see that there is no correlation with LinkedIn click-through rate.
That said, given this level of engagement and the average length of my posts I can calculate that people have spent precisely 58 hours reading them so far. Hopefully they enjoyed them as otherwise I would feel pretty guilty about cumulatively wasting a week and a half of the average employee’ time. If you have found these interesting, you might want to also check out the following:
#1 Made To Measure by Colm O’Grada
#2 Studying the Old Masters — using neural networks by Simon Wintels
#3 Go on, be offensive by Tim Lum
#4 Pay attention to that man behind the curtain by Cassie Kozyrkov
#5 Why Do Data Projects Fail? by Luis Vaquero
This was an interesting exercise as, while in this case the data set was small (with only ten posts and associated metrics), we managed to perform some simple analyses. Although there are complex techniques that can help teams extract value out of disparate, disordered, and dynamic data sets, these are not always necessary. Today I learned a number of things I had not expected.
Never underestimate the value of employees thoughtfully applying simple analyses to their everyday data.
Hopefully this has inspired you to think about what simple analyses you can perform on your data! In the next ten posts I will cover topics including data quality, recruitment, and machine learning in practice. If there are particular subjects you are interested in and would like me to address, please leave a comment and I will do my best. In the meantime, thank you for your support.
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