There are many traps for the unwary in the practice of analytics, which I take to be the process of developing actionable insights through problem definition and the application of statistical models. The technical traps are most obvious but the epistemological traps are better disguised.
That these traps exist and are seemingly not recognised in the commercial and corporate rhetoric around analytics worries the more philosphically-minded; Virginia Tech’s Garner Campbell has shared some clear and well-received thoughts on the potential for damaging reductionism in Learning Analytics. I particularly like Anne Zelenka’s blogged reaction to Gardner’s LAK12 MOOC (I believe there is a recording but elluminate recordings don’t seem to play on linux) and my colleague Sheila has also blogged on the topic.
I don’t see reduction as being the issue per se but careless reductionism and failing to remember that our models are surrogates for what might be does worry me. Analytics does give us power for “myth busting” and a means to reduce the degree to which anecdote, prejudice and the opinion of the powerful determines action but let us be very wary indeed.
This all reminded me of the following poem by my favourite poet and mythographer, Robert Graves. Let us be slow.
In Broken Images
He is quick, thinking in clear images;
I am slow, thinking in broken images.
He becomes dull, trusting to his clear images;
I become sharp, mistrusting my broken images,
Trusting his images, he assumes their relevance;
Mistrusting my images, I question their relevance.
Assuming their relevance, he assumes the fact,
Questioning their relevance, I question the fact.
When the fact fails him, he questions his senses;
When the fact fails me, I approve my senses.
He continues quick and dull in his clear images;
I continue slow and sharp in my broken images.
He in a new confusion of his understanding;
I in a new understanding of my confusion.