…and when is data mining and analysis just a sophisticated, math-laden opinion?

I like to draw insight from juxtapositions. Yesterday, I listened to half a dozen academic presentations on modeling and data mining aimed at understanding the impact of extreme weather on global communities. As you might imagine, these exercises require large data sets, bold assumptions, and extrapolations, some out to as far as year 2100.

Later in the day, I sat at the piano with a blog post about Debussy’s Arabesque No. 1 for solo piano, a popular piece known for its “impressionistic” qualities. The author of the blog did some analysis on melody, harmony, and rhythm that essentially was trying to get into Debussy’s head as he composed this piece.

The blog author teased out a melody buried in some arpeggios and then attempted to show how it becomes a motif throughout the piece. She admitted she couldn’t really know whether this melody was Debussy’s intent, but made an assumption that this certainly could have been what was going through Debussy’s head.

The academic data mining and modeling would probably be scary to those who aren’t comfortable with numerical modeling and methods; the analysis of Arabesque No. 1 would probably be scary to those not familiar with musical notation and compositional methods. The assumptions and extrapolations made in both cases could make nervous anyone familiar with both.

In both cases, a “specialist” is trying to gain insight into something that, for all practical purposes, is unknowable – Debussy’s thought process (even if subconscious) as he composed Arabesque No. 1 and economic and community impacts as the planet warms over the coming decades – and then convince an audience that they’ve indeed shed some light into a dark cave. And if we are to take either analysis as useful, others would have to validate the findings, or otherwise agree on the methodology, results, and conclusions.

Moral of this tale: Analysis isn’t “new knowledge,” regardless of what kind of notation accompanies it, until many other experts weigh in and many analyses converge on similar conclusions. And just because someone has credentials that brand him or her a specialist, doesn’t mean their analysis is more than a sophisticated opinion.

What really astounds me about listening to academic presentations these days (which I have been doing my entire career) is how few people, usually experts with as much background and experience on the topic as the presenter, actually question the results or methodology. This to me is dangerous at its core. Academia is where data and findings should be vigorously interrogated and debated. These days, technical presentations in general seem to be more of an advertising opportunity than a spark for debate towards achieving some consensus and contribution to the knowledge base.