Tags: sampling

4

sparkline

Wednesday, August 5th, 2020

Design maturity on the Clearleft podcast

The latest episode of the Clearleft podcast is zipping through the RSS tubes towards your podcast-playing software of choice. This is episode five, the penultimate episode of this first season.

This time the topic is design maturity. Like the episode on design ops, this feels like a hefty topic where the word “scale” will inevitably come up.

I talked to my fellow Clearlefties Maite and Andy about their work on last year’s design effectiveness report. But to get the big-scale picture, I called up Aarron over at Invision.

What a great guest! I already had plans to get Aarron on the podcast to talk about his book, Designing For Emotion—possibly a topic for next season. But for the current episode, we didn’t even mention it. It was design maturity all the way.

I had a lot of fun editing the episode together. I decided to intersperse some samples. If you’re familiar with Bladerunner and Thunderbirds, you’ll recognise the audio.

The whole thing comes out at a nice 24 minutes in length.

Have a listen and see what you make of it.

Tuesday, May 7th, 2019

Unraveling The JPEG

A deep, deep, deep dive into the JPEG format. Best of all, it’s got interactive explanations you can tinker with, a la Nicky Case or Bret Victor.

Monday, May 14th, 2018

VocaliD

You know how donating blood is a really good thing to do? Well, now you also donate your voice.

Monday, February 26th, 2018

as days pass by — Collecting user data while protecting user privacy

Really smart thinking from Stuart on how the randomised response technique could be applied to analytics. My only question is who exactly does the implementation.

The key point here is that, if you’re collecting data about a load of users, you’re usually doing so in order to look at it in aggregate; to draw conclusions about the general trends and the general distribution of your user base. And it’s possible to do that data collection in ways that maintain the aggregate properties of it while making it hard or impossible for the company to use it to target individual users. That’s what we want here: some way that the company can still draw correct conclusions from all the data when collected together, while preventing them from targeting individuals or knowing what a specific person said.