I'm going to suggest that the suggested example website of amateur data analysis (http://devnambi.com/2015/uc-analysis/) is a good example of well-intentioned BI off the rails.

1. This Smacks of a Rhetorical Axe-grinding

The author comes across as analytical b/c of the use of public data sets and graphs, but the rhetoric of his write-up makes it clear he is anything but objective about his subject. Phrases like these suggest bias and call into question the author's intent: "it's clear that all non-teaching activities are the tail that wags the dog", "the teachers and researchers are there as window dressing", "Administrators have no incentive to cut their own budgets". This last one is a conclusion that absolutely cannot be drawn from the data set: maybe there are incentives, but they aren't working? maybe there are external forces like new regulatory requirements or state policies, beyond administrators' control?

But the average reader will nod and agree that spending is out of control and there aren't any measures in place to stop it.

An honest researcher/analyst will not mix agenda with analysis and will, in fact, bend over backwards to try to look for ways to invalidate his/her own conclusions.

2. Missing Essential Data

Hands up: how many of you read this post and discovered that he presents no data to indicate how fast or even if tuition at UC is increasing?

Yup, that's right. He shows a shocking graph at the beginning showing massive increases in college tuition over time in the United States. Nothing specific to UC.

Then the rest of the article presents cost breakdowns by group, as a percentage of total costs at UC. What happened to the initial premise that tuition was too high? Go ahead, search the page: the word tuition doesn't even appear anywhere in the body of the presentation. "Tuition" is in the lead-in hook paragraph and in his wrap-up broadside "Implications" section.

It's implied but never stated that the average, overall increase in tuition for the country applies equally well to UC. Maybe so, maybe not, but that's a sloppy (or possibly deliberate) omission.

I could continue, but I think I've made my point. Mind you, I don't actually think this is a particularly bad presentation, because at least he's transparent in his biases and an astute reader will see this quickly.

This to me highlights the delight and danger of the web: data sets are ubiquitous, but so are soapboxes.

Rich