by JCM | 16 Sep, 2020 | Narratives & Meanings
Seeing this graphic doing the rounds. Pretty. Still, call me a cynic, but:
1) [citation needed] – the full graphic lists multiple top-level sources, but without details – what were the exact sources? What was the methodology for identifying this data used by each of those sources? How credible is this information?
2) So what? What useful insight do these lump sums tell us without context? Most of the numbers are random, unrelated big figures, so how does this help us understand the world? What are the trends? What’s the insight?
This is superficially a great bit of marketing, as it’s getting shared a lot and is designed to promote a company flogging a data analytics platform. But there’s no further detail on their site, which is a masterclass in promising a lot (e.g. “Solve back-end integration of any data, at cloud scale, without moving data”) without actually saying or revealing anything about how their tools actually work. To find out more, you need to give them your contact details.
For true data geeks, as for ex-journalists like me, alarm bells start going off at this point:
– Data without context is meaningless
– Single data points don’t equal insight
– Data needs to be well sourced to warrant trust
– Don’t give away your data if you don’t know what you’re getting
by JCM | 15 Sep, 2020 | Systems & Technology
This long piece neatly sums up the paradox of the age of algorithmic analytics:
“Algorithms that tell us which topics are trending don’t merely reflect trends; they can also help create them…
“The internet has shown us that the oddest of subcultures and smallest of niches can develop followings… I don’t think readers weren’t interested. It’s that they were told not to be interested. The algorithms had already decided my subjects were not breaking news. Those algorithms then ensured that they would never be.”
This approach of following your analytics is a *terrible* content strategy. By pursuing a mass audience and popularity above all, same as everyone else, you’re doomed to lose your distinctiveness – and relevance to your true target audiences. Even though the algorithms supposedly love relevance above all, they’re still (usually) not sophisticated enough to identify your priority audiences among all those visits.
This is why we’re seeing so many traditional publications fail, and ad revenues collapse: They’ve all become alike, because the algorithms have told them all the same things. That’s made them less valuable, in terms of both price and utility.
Don’t get me wrong: audience analytics are essential. But you need to know how to read them – and their limitations.
by JCM | 5 Sep, 2020 | Structures & Models
I’m not a stickler for “correct” punctuation, as a rule – except when it comes to apostrophes and the Oxford Comma. This is because punctuation, mostly, is about flow and rhythm, not meaning. Misplaced apostrophes and missing commas in lists can substantially change meaning rather than flow, so their correct placement becomes vital.
This fascinating essay on the evolution of punctuation makes clear that improving flow and clarifying meaning has long been the goal – while also exploring the long history of resistance to punctuation that over-clarifies meaning.
It’s a useful reminder that words are about interpretation as much as intention. Sometimes ambiguity lets greater meaning emerge, building stronger connections with your audience by encouraging them to think more deeply about your words. Sometimes it creates confusion.
The challenge, as ever, is getting the balance right – so focus on the needs of your audience. What will most help them understand your meaning (or meanings)? What will confuse? No one wants to have to try and parse a complex run-on sentence with multiple sub-clauses and dozens of punctuation marks. Even if they do make it through to the end without giving up, your meaning is likely to be lost.
In other words, as ever, when in doubt: Keep it simple.
