I hate the word “curation.” Or, rather, I hate that it’s one of the most overused, overhyped words in Silicon Valley these days. Or maybe it was yesterday, before “actionable insight” became the term du jour. But curation may be about to make a comeback.
Why? Because it turns out that it’s really hard for machines to pull “operational insights” out of big piles of data. This is why big enterprises are scrambling to hire data scientists, and are increasingly discovering there’s far more demand than supply.
In short, we can, as Forrester does, trumpet operational insight or actionable insight as top priorities, but achieving them is easier said than done:
Top priorities for Big Data? Marketing – understanding your customer – and operational insights. #feaf12
— Forrester Research (@forrester) May 4, 2012
Which is why I found a lunchtime conversation with my neighbor and friend, Chuck Sharp, interesting. Chuck is the founder and CEO of Right Intel, a marketing data analytics company. This is Chuck’s second analytics company. His first, Sharp Analytics, was acquired by iCrossing in 2007. As Chuck told me, one thing that he learned from his first attempt was that a dashboard-based approach to analytics doesn’t work. People don’t reliably log into a dashboard service and, even when they do, they often struggle to understand what they should do with the data being presented.
Right Intel provides a platform that makes it easy to amass and amalgamate different data sources (e.g., Twitter feeds, charts and graphs found in blog posts, etc.), but that’s only half the story. Right Intel then connects with partners who in turn service big brands (e.g., Marriott Hotels). Those partners (or a designee within the end user/customer) then siphon through the pool of data to determine which highlights to pass on, and which actions to recommend based on the data.
It’s a pretty light-handed way to intervene to make sense of Big Data, and it might become much more common than we’d like. As much as we’d like to assume all data can simply be crunched by machines to spit out insights, the reality is that human intelligence and intuition are going to remain relevant, and probably dispositive to getting great insight from data.
In the systems intelligence world, things are a bit easier, as a company like Nodeable, for example, can poll the APIs for AWS or GitHub and pull out somewhat structured data, and interpret that data without human intervention. But machine data is the exception to the rule, and even here, someone needs to be looking at the output and determining the best course of action based on the data (though we do make suggestions).
Which, I suppose, is a long way of saying: it’s not too late to go back to school and get a degree in data science. People are going to be important to Big Data for a long time. Probably forever.