Monday Musings

By July 9, 2018 Mondays No Comments

No Musings last week due to mostly being tied up with our semi-annual joint tri-birthday and wedding anniversary bash. With that now out of the way (much fun and cake enjoyed by all concerned), normal service can resume.

Much of my time this week has been spent preparing a user survey, due for publication early next week. That’s sent my brain spinning is various different directions as I’ve pondered how best to elicit the kind of data we need back from the survey, and how to analyse and act upon it once we’ve gotten it. Some of the things I’ve found interesting are naturally skewed as a result.

  • Data visualisation isn’t a topic I’ve spent much time on in the past, though I’ve naturally been vaguely aware of the benefits of translating a dataset into something with a bit more visual snappiness to it. While pondering how best to communicate some results, I discovered the Data Analysis and Presentation Skills specialism on the Coursera site. If you can ignore the somewhat in-yer-face PWC branding, there’s certainly some good content in the first section – Data Driven Decision Making – and I plan on working through the rest in due course.
  • Of course, there’s a possibility that I may not. Get around to working through the rest of the course, that is. But that’s fine too, since I could argue that I already found what I was looking for in the first part; namely, a model or framework to help guide me towards data-driven-ness in my current capacity. I used to worry about my laissez faire approach to learning and self development, but I’ve realised over time that if nothing else, what I usually end up with is a better sense of what I don’t know yet, and as in this case, something to help guide my thinking until I have the time or inclination to explore the area further at a later date. Just in time learning, as I like to think of it.
  • The model I found particularly useful in this case was the DIDO framework (not referred to as such, at least officially, by PWC). You’ll probably note that it has fairly strong parallels with the Scientific Method. One thing I noticed was that, in retrospect, I’ve been applying the model already; but it’s nice to now have a bit more definition to the approach I’ve generally been taking:
    1. Discovery – identify the problem, form a hypotheses, collect available data
    2. Insights – perform data analysis
    3. Decisions/actions – link insights to actionable recommendations, make a plan
    4. Outcomes – review the desired outcomes of long term objectives & solutions, revert back to defining the problem
  • It was helpful to be able to correlate the Data Analysis specialisation with The Lean Product Playbook, which as you might imagine, contains entire chapters on how to use product data to its best advantage.
  • In other news, I hosted the Winning at TestRail Reporting webinar the week before last but didn’t get to share it here due to laziness the party I mentioned. If you’re interested in watching it, you can find the video here:
Thanks for reading. Feel free to reach out via a comment or on the socials if anything resonates.


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