The art of using data is ensuring that it moves people to action; these stories are no less fragile.
Data architecture can be difficult to understand – when new to the subject, it can be very hard to distinguish between hype and concept, between real things (with obscure names) and conceptual names which are actually marketingy gizmos. Over time in consulting I come back to a fish based explanation, which I synthesised in a phone call last year with someone and have finally committed to paper.
My firm have recently signed up with Snowflake, so on a spare afternoon I decided to compare the different ways you can connect data to Snowflake from Alteryx. I’m doing this with some Kickstarter data I found on data.world; I’m breaking the data down into four tables, and writing each table with a different method.
I was on Github looking at something else (as always) when I saw someone I follow forking an R repository called Polite. Dmytry’s markdown to explain the pillars of politeness are well written and absolutely applicable to the wider corners of scraping that a lot of analysts do using tools like Alteryx.
Recently, I took on the mantle of teaching data ethics and security to colleagues who join my company. The training runs over a whole day, but I still feel there’s so much I can’t cover. Making some tea this morning, I realised one of the really important ones was an item that comes up at work under the radar a lot, which is the ethics of overtime.
One of the clearer roots of the problem is the wretched state of American policing, which has been in dire need of reform for longer than I’ve been alive; a rallying call of the 2020 movement has been “Defund the Police”. I’ve not looked into police spending to know how much is too much, so I decided to do a comparative analysis of some American and international cities to see what their police budgets looked like.
Webscraping can vary between wildly hard (purposefully or otherwise) and being the notch below an open API to extract data. However, there are often some hints you can use to check whether or not a particular site will be one of the easy or hard ones.
I actually don’t spend that much time working with Tableau, so in the last year I’m a little out of practice. I went back to my training to restart my practice; do something you’re passionate about, and draw it out first.
On Wednesday my esteemed colleague @liluns and I went to the AWS Summit in London, which is several thousand people crammed into one end of the Excel Centre in the Docklands.
There’s a lot of data where one observation to a human (e.g. one survey) isn’t ideally one observation to the query language of a database system.
Sneaking in just before April does, I’ve decided to come out swinging with an frankly uncontroversial idea which may still split my colleagues, but only one this month.
Fragile Data 