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U.S. universities, rich in data, struggle to capture its value, study finds

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Re: U.S. universities, rich in data, struggle to capture its value, study finds

#71
There are so so so so many reasons for this. Research data: hard to centralize because subject to impossible conflicting demands from funders and human subjects IRBs, and in the hands of ferociously independent faculty. Students: privacy laws and worse than that over-cautious university counsel and administrators who are afraid of running afoul of those laws. Administrative data: stuck in a bunch of individual bureaucratic buckets that don't talk to one another because universities are badly managed in general, also in the clutches of horrific enterprise software platforms made by the worst companies in the world (like oracle) and administered by IT people who aren't paid market rates.

University data projects tend to succeed if and only if they're turned over to librarians, who are typically the only people on campus who have any clue how to do such a thing.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#72

Earlier quoted context omitted.

> It's kind of like a very boring video game for adults that makes them feel like they're working, Absolutely love it. Let's distinguish a few things though. "Data science" seems like a pretty weird name. I mean, it's just "Science" right. Of course there's statistics, mathematics, signal processing, systems analysis, machine learning... all the good things that you and I are into. But how does this get huddled uncom…

Agree that the term data science is strange. Data engineer seems like a better name. Science implies rigorous hypothesis testing guided by a theories of how particular systems work. Not sure that applies to most “data science” work.

Already taken. Data scientists are data engineers, and data engineers are data warehouse workers and janitors.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#73
post #59
post #26

Earlier quoted context omitted.

I lived through an acquisition in the teens that I feel was an illustrative story. An old financial services provider embarked on a massive, whole organization modernization effort. The new CEO went all in and actually made the necessary investment in staff and headcount, which was remarkable in itself. A big part of the effort involved providing customers with big data solutions and all the implied benefits that wou…

>Turned out the entire modernization and big data effort had been an elaborate bait and switch committed by the board and CEO of the acquired company Are you saying that rhetorically, or do you actually believe (perhaps with evidence) that the modernization and big data effort was not undertaken in earnest?

> masterfully executed.

I feel like this part you omitted clarifies my opinion on the matter.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#74

> The study’s authors contend that universities have been slower than organizations in other economic sectors to create senior-level positions focused on data quality, strategy, governance and privacy matters. Universities are not an economic sector; they aren't profit-making enterprises. They are knowledge- and education-making enterprises. Do they need more employees who aren't creating knowledge and educating? My…

That’s exactly what universities are these days. They sell a signal that says “this worker can do the bare minimum you need” called a Degree. We make the worker get certified themselves, but this is the purpose. To act like modern Universities do anything else today is disingenuous in my opinion. It’s like saying people who work in finance are there to allocate capital efficiently. No, they’re there to make money. That’s it.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#75

As a data scientist, I think most data is useless, but there is an addictive, video game-like quality to throwing lifeless spreadsheets into a machine and having colorful visualizations come out. It's kind of like a very boring video game for adults that makes them feel like they're working, when they're actually just enjoying colorful abstract shapes and colors. To be honest, this is probably a sizable piece of why…

This definitely applies to programming as well. It's not uncommon to see people happily churning out mountains of highly redundant and repetitive code, or zealously pursuing far-reaching nit-picky refactors of dubious benefit and I think it's simply because the act of writing code and running tests and watching them go green is fun.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#76
Data is not information. Lots of data is worthless if you don't know what you're looking for.

Perhaps we could actually do something about this if we focused strongly on falsification, but right now there's just going to be a whole lot of Wittgenstein's ruler going on; what people want to look at or see will come first with such an abundance, and there's going to need to be some kind of real filter to make it useful.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#77

Working in a top-tier computer science department, I find our ability to answer basic questions about the health of our degree program fairly troubling. I think non-academics may be surprised by how much we don't know, and how little useful and continuous data analysis is taking place. For example: What is our retention rate? Meaning, what percentage of students who start our degree programs complete it. A fairly sta…

Perhaps one problem is that every college has its own bespoke curriculum and processes, so every data problem is a "little data" problem. Of course there are lots of rationalizations for why every program needs to be unique and special, but does it really benefit the students?

A similar problem in medicine: Every clinic system has a unique set of business processes, and a custom build of Epic. Granted the clinics are competing on which one can develop the most efficient processes, but does the patient benefit?

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#78

Working in a top-tier computer science department, I find our ability to answer basic questions about the health of our degree program fairly troubling. I think non-academics may be surprised by how much we don't know, and how little useful and continuous data analysis is taking place. For example: What is our retention rate? Meaning, what percentage of students who start our degree programs complete it. A fairly sta…

An anecdote from someone in IT at a major university: The registrar has two employees whose sole job is to write SQL queries. These are to answer basic questions such as, "How many undergraduates are currently enrolled." And it turns out that this is a nearly impossible question to which to give a definitive answer.

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#79
post #78

Working in a top-tier computer science department, I find our ability to answer basic questions about the health of our degree program fairly troubling. I think non-academics may be surprised by how much we don't know, and how little useful and continuous data analysis is taking place. For example: What is our retention rate? Meaning, what percentage of students who start our degree programs complete it. A fairly sta…

An anecdote from someone in IT at a major university: The registrar has two employees whose sole job is to write SQL queries. These are to answer basic questions such as, "How many undergraduates are currently enrolled." And it turns out that this is a nearly impossible question to which to give a definitive answer.

Shouldn't the finance folks know that kind of thing pretty definitively?

Re: U.S. universities, rich in data, struggle to capture its value, study finds

#80

Earlier quoted context omitted.

Agree that the term data science is strange. Data engineer seems like a better name. Science implies rigorous hypothesis testing guided by a theories of how particular systems work. Not sure that applies to most “data science” work.

Already taken. Data scientists are data engineers, and data engineers are data warehouse workers and janitors.

Not at all. Data engineers are data engineers. Data scientists are a mix of statisticians, machine learning researchers, and data analysts.
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