Day 2 – Herding the Data Cats
I felt distinctly like I was herding cats today trying to not only extract data, but also meaning from World Health Organisation (WHO) data sets. Just as I thought I was heading in a direction, something took off at a tangent. But with some chipping away, and timely help, I think I made some progress. The overall plan is to pick at the global WHO data to discuss themes around Income, Mortality, maybe just this once, Why it’s Great to be a Girl!
So, today I was pretty comfortable that I could find some readily sourced data from WHO. However, trying to understand the meaning of some of the data was a whole other story. One data set talked about lost years of life. Quite an interesting statistic from a lost development and productively perspective for a region. However, it means you need to know how long someone would have lived, had they not died. Which I got to thinking is surely hard to be anything other than a big guesstimate, as no one has lived past their death to be able to test the data. I’ll stop there. And that was where I went back to back to clean cut mortality data. Because death is something you can depend on. Mostly.
Well, sort of. Reporting of deaths, and classifying it seems lot less clear. And that is where the cats got back out of control. I found that total deaths was a much smaller number than the sum of deaths from the various causes? So, although I accept there can be multiple causes for a death, it certainly made my life this afternoon much more complicated.
Despite the brain aches, I am certainly enjoying all the information and new software and lovely people. And so I am heading into day three= with some data, and graphs, and notes to try to convince Shane and Mel that I’ve met all the accountability criteria we set out on Day one and am ready to get going on some story telling on Day three.
Ambitious?? Never. Wish me luck!
About Esther Richardson
Esther is doing a short 8 day internship (over 4 weeks) to decide if she wants to change careers from studying exotic diseases (Epidemiology) to becoming a Data Story teller.
These are her guests blogs on her experience.