Hello,
It’s good to be back. As you’ll have noticed, these newsletters are no longer weekly. Returning to actual in-person interactions in Berlin and at two conferences (Big Data London and the Labour Party, diverted my attention somewhat. As the world opens up, and as I knuckle down further into building a portfolio and courses for the Data Skills Consultancy, I expect that will become more common - and also means more unexpected occasions for new things to write about. So now Sideways Looks will be coming to you at unspecified intervals, when I have both time and topic matter, which I hope will provide an extra frisson of excitement for your inboxes. As these newsletters will be less common, I’m even more keen to focus them on the things that you want to hear about. So please do keep letting me know what those things are. Evidence-based newslettering is the best form of newslettering. Happy Sunday all, Oliver
Thought for the Week: Data Divides
Much of my career has involved liaising between ‘techie people’ and ‘non-techie people’. The fact that this divide is so familiar, and often so stark, continually fascinates me. I’ve met many extremely smart people who clam up as soon as conversation turns vaguely quantitative; and many personable technical people who struggle to cut through to non-specialists. It’s one of those divides that easy to exaggerate through stereotypes, of course. And often institutional structures don’t help; for instance, I’m convinced that many academics would quickly improve as communicators if they got the daily practice of talking to non-specialists that most other workplaces provide. But the fact that people can so quickly place themselves on one side of the divide has, I think, pretty big implications. As you’ll all know, I think a lot about data. Data isn’t just huge amounts of maths flowing through super complicated machines. Data is the florist noticing that roses outsell orchids, or the parent spotting that their kid is eating less, or a manager realising a project is falling behind deadline. Analysing that involves working out if it’s a temporary blip or something long-term, or comparing to other florists/children/projects. It doesn’t require hardcore maths or coding skills. It’s about asking the right questions, checking your assumptions, and distinguishing good and bad evidence. Doing it well can save you from bad decisions (Here’s one great historic example of data analysis which involved basically no maths at all and may have saved quite a few lives). You won’t be surprised to hear that I think widespread training in low-key data analysis is important. I don’t think people should be scared of data analysis; but they should be well-informed. We’ve seen too many influential figures doing data badly during Covid – sorry to single you out Robert Peston, but you’re a good example. (For balance, specialist organisations like the WHO and CDC have not always done well at communicating or using data). I spent the Big Data London conference looking at loads of funky dashboard technology, designed to help more marketing people and managers use data well without being specialists. That’s to be welcomed – as long as those people are also asking the right questions, being humble and careful, and not jumping on the first graph which confirms their biases. (I have seen this happen. It’s hard to stop). Given all that, I worry there’s an increasing trend towards associating ‘data’ with ‘super scientific thing only specialists do’. Doing research into data-related training, I’ve found it heavily focused on supporting budding data scientists (read: hardcore coders). One approach I’m planning to use is calling courses things like ‘critical thinking’ or ‘making good decisions’, with content heavy on how to use data well. There’s the usual concern of whether that will get the right audience – just like ‘emotional intelligence’ training is unlikely to appeal those managers who really need it, would someone who habitually misreads evidence think they need a critical thinking course? But I have to acknowledge that the word ‘data’ can also put off precisely the people who need to be in the room. There’s a deeper problem, which probably isn’t going to be solved by a few training courses (though I can try). If the word ‘data’ connotes ‘something that techie people work with’, that encourages whole separate teams, training programmes, career pathways, and cultures developing under the heading of ‘data’. The idea that someone might move from sales to data, or from data to senior company management, becomes ever harder to sustain. And then you can end up with entrenched cultural mismatch between parts of organisations, and big bad decisions. (There’s more about issues of finding and/or training people who can combine data skills and soft skills in this report by DCMS). To end on a positive note – while I’ve met many people who are quick to place themselves in ‘techie’ or ‘non-techie’ camps, I’ve also met many people who do bridge that divide very effectively. I know data scientists who are great at engaging with other teams’ work, and humanities-educated managers who are great at asking the right questions of their data analysts. I’ve been lucky in having a career path, and the training that goes with it, which allows me to straddle the divide. I hope I’m wrong that such a career path is rare. But I need more data on that. I’d love more data from you, the various readers of this newsletter. If the above rings true for your organisation; if you’ve found smart ways of bridging the data divide; or if you’re a florist who sells more orchids than roses, actually – please do get in touch.
Fun Fact About: Children's Railway
There is a railway line in Budapest which is staffed by children. It also connects up to other railway lines in Budapest; I imagine the experience comes as shock for unsuspecting passengers who just needed a quick interchange between the 21 and the 22A.
Recommendations
There’s a new regular tech policy meetup happening in London. I went to the inaugural one and it was extremely interesting, but more importantly also great fun. You can sign up here. This article was a fun journey into ‘hackathon’ contests. Lots of political nerds are getting excited about the current BBC Blair and Brown documentary. I’ve found it merely OK, not adding much new to a well-trodden story. But the recommendation algorithms led me to a similar documentary on David Cameron, from which I learned a lot more. Finally – my parents’ new Havanese puppy is doing very well, thanks for asking. He is now big enough to steal shoes by picking them up rather than dragging them by the laces. Here’s a great story about how Andrew Lloyd Webber also ended up with a Havanese, for emotional support following the release of the films Cats.
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