In the information age we are constantly bombarded with content.
Previously, having access to more information provided decisive competitive advantages.
However, as the cost of producing new information has plummeted, information access is becoming less of a differentiating factor for businesses. Access to information is becoming more of a commodity and as a result the playing field has been leveled.
In this new age of information overload, it is becoming critical to know how to curate information. Knowing what not to read is just as important knowing what to read.
Editorial curation is relying on authority to present you with information. Whether it be a brand you trust to have high quality content or an author you have followed for years, you are relying on past performance as an indicator for future returns.
Publications such as the Financial Times and columns such as Lex are examples of an editorially-curated product.
However, as past performance is a lagging indicator, editorial curation as a method can often be slow to adapt to changes. Add the natural human propensity to avoid change into the mix and it makes spotting dips in quality difficult to notice or even acknowledge.
Similarly, the choice to rely on editorial curation often requires ignoring what else may be available. Breaking your comfort zone of the editorial nest and trying something new, strange or different can be daunting. Once accustomed to one style it can be very difficult to change.
Successfully utilizing editorial curation requires immense discipline to objectively and regularly scrutinize new content and compare it against what is being provided by elsewhere. Often a lack of objective data on what is actually being used is major roadblock.
With digital channels becoming more accessible this has given rise to a new form of information curation - social.
Our family, friends and colleagues all have varying degrees of knowledge about others and as a result can sometimes accurately predict what kind of content others would be interested in. As personal recommendations put social capital at risk, there is an inherent increase in trust toward such recommendations. Consistently sharing irrelevant content shows a clear lack of personal understanding and will encourage social blocking.
Companies like Twitter and Facebook are prime examples of this form of curation.
Traditional media has tried to fight social curation since its inception. As it separates brands from their content, it forces content to stand on its own two feet to justify its existence.
Millennials apathy toward brand-content producers is no doubt shaped by this separation. In a world with no brand loyalty, content reigns supreme.
Automated curation has become much more accessible in recent years thanks to distributed computing and the dramatic drop in costs.
Google has pioneered this space, first with pagerankings and now with indvidualised recommendations. Facebook, armed with swathes of personal interaction data are competing strongly too.
The bottle-neck in automated curation has typically come from two places:
- Data availability: Generation of quality recommendations automatically requires a large amount of data. This can occur in social context as individuals have typically spent many years together to subconsciously collect such data. Without this data recommendation quality is bound to be low.
- Human design: Generation of insights from the available data requires strong assumptions about determining correlation and causation, which can often be woefully incorrect. Recently neural networks and machine learning has meant that this factor is becoming less of an issue, at the expense of requiring significantly more data than humans.
One of the benefits of automated curation is that it never misses any information. With strong automated curation you can be assured that any recommendations being made are from 100% of the content made available to the automated curator. The comprehensiveness of this approach makes it more attractive and therefore, the larger the potential corpus of information.
Like social curation, automated curation is source agnsotic. As a result it also drives content quality to the forefront.
Tying it all together
So we've seen that there are three methods for handling information overload: Editorial, Social, and Automated. While historically editorial has been the dominant method, modern technology has allowed for the rapid growth of social networks and deep machine learning enabling social and automated curation respectively. It's likely that harnessing the latter two is going to be the future of handling information overload in the modern era of the web.