Weeknotes #6 — Mozfest is on its way!
Last week I have done SO MUCH WRITING, I barely had time to write my own weeknotes, so I am catching up now! I am grateful for every minute I have to write, and like exercise, it is just something I need to make as a habit. So here we go…
Things I did
Mozfest blog post — Technically I did it in week #5, but it was published this week, so I including it here. Mozfest is three weeks away, and I wrangling the Shifting Power in Tech space. If you don’t know what it is, MozFest is a unique hybrid: part art, tech and society convening, part maker festival, and the premiere gathering for activists in diverse global movements fighting for a more humane digital world.
I wrote a blog about our Shifting Power in Tech space so you will get to know better what we are planning. In the spirit of the pandemic, MOZFEST IS ONLINE THIS YEAR, and tickets are “PAY AS YOU CAN”. So if you were never able to attend Mozfest because of costs, this year is a great opportunity to be part of it. Totally recommended. You can get you ticket here.
Data Collective Community Call — We had our first Data Collective community call and it was going well. We ran the method of Rant for a Better Future, where participants rants about their frustrations with data and then try to find solutions. I ran a similar session about open government 5 years ago (can’t believe it has been 5 years) and wrote about the methodology here.
My main takeways from the call is that we think we are alone in our struggles with data in our organisations, but actually, others have similiar if not the same challenges in other orgs too. Speaking to others always helps to get new perspectives and ideas, and personally, recharge me with working in different ways on these challenges. Want to learn more about it? Read the Data Collective Weeknotes I wrote.
Data analysis review process — When someone submit code in Github, it will always go through review process. When we ask for editing. Why? Not because the coder or writer have done something wrong or can’t be trusted but because we are all humans and make mistakes. So why not checking our work to reduce these mistakes? Same thing we can say for data analysis — why not share our work to make sure we have done everything right? This week me and one of my team mates worked on what a review for insight and analysis work looks like and how we can implement it successfuly. Spoiler — look at the basics of methodology and the data itself.
Things I listened to this week
Freaknomics — How much do we really care about our children? Some intresting data questions and the truth about car seats.
