I have been trying to catch up on some readings lately and the following article caught my eye “Complicated or Complex? Analytics Treats Them Differently” at the icrunchdatanews.com website. The article talks about something that that interest me, the difference between complicated and complex in terms of analytics. It’s something that we have written about previously and is very much an area that needs more work.
The article drops the usual buzzwords and phrases straight from a consultant’s handbook such as:
“the stakes have never been higher for managers to make better decisions with analyzable information”
“They need powerful, high-performance analytics that can process the Big Data”
Actually, with the burgeoning faddism around analytics and big data, it’s getting difficult to avoid these sorts of phrases and find stuff that meaningful. However, they do go on to make some points that aligns with our thinking around learning analytics:
“Business intelligence and drill-down queries are insufficient”
This is a valid point and links nicely to the contextual nature of learning analytics (pedagogical intent, task being undertaken etc) and the difficulties associated with learning analytics and the re usability paradox.
“Delegate more decisions to employees”
It’s about time! This is something we were alluding to in our complexity paper some time ago and is a key part of the IRAC framework. Information needs to be represented appropriately in context with the task it’s associated with, it needs to be coupled with affordances for action (what’s the point of data if it doesn’t lead to action?) and it needs to be able to change as the context changes.