Someone once told me to ‘do one thing, insanely well’. Our working memory is infamously limited to juggling at most seven balls. Multi-tasking and context-switching are the enemies of productivity. My favourite HBR article is about timeboxing into a calendar, a corollary of which is: do one thing at a time. HBR have even written an article of articles about saying no.
The vast majority of PowerPoint slides and decks are too busy. There are a trivial many, a vital few and a law, from Pareto, relating them. Yuval Harari starts his latest best-seller 21 Lessons for the 21st Century: ‘In a world deluged by irrelevant information, clarity is power’. Cal Newport says in Deep Work ‘what we choose to focus on and what we choose to ignore—plays in defining the quality of our life.’ Michael Bhaskar says in his book Curation: ‘...more is not always more...there is a tipping point after which simply adding in extra no longer works. This matters. First because for the last two hundred or so years we have engineered society and businesses to keep growing; to keep adding more. Second because we are now reaching overload, when incremental additions are causing more harm than good…’
All this is true in corporate learning too. The modern workforce is overwhelmed with communications software, content systems, hardware, content. There is a constant FOMO with regard to content. An L&D Director told me last week that there’s a feeling that her staff feel there’s too much content and that they can’t find anything. We hear that a lot. Large libraries of content go mostly unused. More importantly, many workers go through their professional lives without ever being touched by the nuggets of knowledge and inspiration that might make a major difference to their levels of productivity and feelings of fulfillment.
There are plenty of signs that the journey from more to less is worthwhile. What paths are there?
The same less for every person
Also known as curation, this is about reducing the quantity and thereby increasing the quality of the pool of learning materials at your company. Combine quantitative data (e.g. usage and survey feedback) with qualitative data (feedback, focus groups, conversations) and what you think as a learning strategist to clear the clutter. If in doubt, cut it out is a useful mantra / mindset. Even if some content gets pulled which some of your staff find valuable, you’ll hear about it and be able to reinstate or, better, replace with a better version of that content, armed with specific feedback. One of our clients reduced a high-quality library (from one of the content market leaders) of over 10,000 assets to a couple of hundred; they know that if they get high usage from 200 assets, it’s a big win, relative to current engagement levels. It’s working.
Anders Pink is an automated curation tool. It crawls the entire web, constantly and returns results according to briefings users can set up. It’s excellent. I’ve heard some people say that it returns too many results. Of course it can do that - it’s looking at 2 million newly published articles every single day! But if you add extra conditions (only these providers, only if shared by this twitter account, only if shared by BOTH this twitter account AND that twitter account) to the system you can rapidly get from 2 million down to much smaller numbers (including zero, on some days, which is fine). Note that Anders Pink can be made to work at a company level or individual level. Also please note that we know the AP guys, but are not affiliated in any way.
Culling content in these way is actually a less extreme version of the experiment that quite a few corporates have tried and talked about in the last couple of years: turn off the LMS. You’ll either hear nothing - revealing in itself. Or you’ll hear some things - also revealing.
Here’s another thought experiment. Suppose you replaced your entire learning system and content with just seven assets. A podcast, a book, a class, a YouTube video, a speech, an infographic and a course. Suppose these seven assets were agreed in consultation with the entire company, from intern up to CEO. Of course, they would need to be about high-level, common workforce skills and behaviours rather than anything specific or technical. Suppose each of them were discussed, debated, known intimately by everyone. Not just the content but the reasons for their inclusion. Suppose there were healthy and open disagreement about the inclusion or meaning of some of them. These seven assets would be like company values are supposed to be. But richer, full of meaning, not forgotten, not fatuous. Suppose the CEO and other senior (and not-so-senior) figures publish what they think about some of these assets, how they’ve been affected by them. How would this compare to your current arrangement?
A different less for each person
Of course, the very best seven assets for me is probably not exactly the same for you. This is where a personalization comes in. How to get to the right less, the right vital few for each individual. The means of personalizing - and more specifically, prioritising - an array of content are well understood now. You can search, filter, browse and get recommendations as I argue here.
The most appropriate activity will depend on the characteristics of the content (e.g. is browsing a value-adding part of the experience, like, say, choosing a film, watching trailers etc) and how much knowledge the user has about what they want (a lot in the case of search, say).
Two key features of Filtered
Our learning experience platform - Filtered - provides a curation-first experience. That’s the one thing we do insanely well. And it’s appropriate for what we’re trying to do because learners often do not know what’s out there (the unknown unknowns) and we have enough clues to make meaningful, useful recommendations. But all the other getting-to-the-right-thing activities are relevant to our users. While recommendations, browsing, and search have been part of our UX from Day 1, we more recently launched a filtering feature. There are three metadata fields (format, length, provider) for which many of our users have strong preferences. They can filter across any and all of these. Note that this feature lives outside of what’s going on algorithmically in Filtered, as our Chief Scientific Officer, Chris, explains here.
The other key feature is much more algorithmically involved. It goes by the internal codename nexrex. Essentially, it’s a feature which brings the very best of what’s been curated in a given client’s instance of Filtered, but from outside of the skills signature framework.
This dash of freshness and serendipity, in tiny doses, whittles away the danger of "too much of the same recommendations" and lets the system distil down to only what is necessary. It is beautifully portable into other systems (via an API) and fast tracks users from more to less.
