[HN Gopher] U.S. universities, rich in data, struggle to capture...
___________________________________________________________________
U.S. universities, rich in data, struggle to capture its value,
study finds
Author : giuliomagnifico
Score : 74 points
Date : 2022-12-31 17:54 UTC (5 hours ago)
(HTM) web link (newsroom.ucla.edu)
(TXT) w3m dump (newsroom.ucla.edu)
| eruci wrote:
| Then give the data away for free to those who might do something
| useful with it.
| Eumenes wrote:
| We need more ad-tech startups for the university/higher education
| industry NOW
| a9h74j wrote:
| One model: YT for persons without an adblocker.
|
| Or: This lecture brought to you by Pepsi.
| jrm4 wrote:
| 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.
| wolverine876 wrote:
| > 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 understanding is that universities have been
| greatly expanding such non-core functions.
| fullsend wrote:
| 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.
| wolverine876 wrote:
| It's a popular meme but IMHO does not reflect university-
| level analysis. That exists, but reductionist arguments
| merely take the worst of someone or something and blow that
| up to 100%.
|
| For example, everyone feels self-interest, but the
| reductionist claim it that it's all we are. A simple look at
| the evidence shows that it's manifestly untrue; people are
| much more than self-interested.
|
| Similarly universities do and are much more. I know
| university students and they are learning _a lot_ - I 'm very
| impressed with the thought and creative effort put into the
| cirriculum.
|
| The reductionists really hurt themselves and people who
| listen to them. They wall themselves off from all the good in
| the world, including education.
| hammock wrote:
| The abstract suggests higher ed administrators collect data from
| "research, administrative operations and other sources" but there
| is one source not mentioned: donors.
|
| I am absolutely sure that donor data is organized, systematized,
| useful and generates positive ROI.
|
| It's surprising that this study would not mention donor data
| (unless they had a predetermined "struggle with data" point of
| view in mind)
| jimlongton wrote:
| I can't access the original study however taking data, explicitly
| naming security cameras for example, to "use it or merge it with
| data from external parties such as publishers or public or
| private sector organizations" will surely not seriously degrade
| student and staff privacy, right?
|
| The rush to "exploit" data reminds me of the dot com hype. It's
| one thing to use available data to make more informed decisions
| about things from course content to building occupancy. It's
| quite another to rush towards total surveillance because of a
| Fear of Missing Out of "exploitable data".
| McSinyx wrote:
| > degrade student and staff privacy
|
| That's the primary value to be captured right there! Their
| privacy is highly valued, monetarily:
|
| > authors contend that universities have been slower than
| organizations in other _economic_ sectors
|
| Education in the US is just pure business.
| a9h74j wrote:
| Make of it what you will, I just saw on the project censored
| website that protecting student data is seen by some as a
| major priority. The article seems more PII and cybersecurity
| focussed, rather than recognizing the threat of intentional
| monetization.
|
| https://www.projectcensored.org/ferpa-and-higher-ed-
| should-p...
| etempleton wrote:
| There are a couple of challenges at play:
|
| 1. Systems in higher Ed often do not talk and departments tend to
| be siloed and have drastically different goals and objectives.
|
| 2. There are a lot of privacy concerns as most of the data is
| students data.
|
| In addition to this, I question what the end goal of the data
| analysis is? What is the value for a not-for-profit? The goal is
| going to be based on the department as a college doesn't have the
| same profit motive. The individual departments typically don't
| have the resources to do deep data analysis.
| tomrod wrote:
| This is the type of article that makes me wonder: "therefore,
| what?"
|
| Yes, absolutely, universities are awash in data they do little to
| anything with outside of hyperfocused laboratories. I wonder if
| having central data management groups would be an enabler or a
| bottleneck, however.
| datadad wrote:
| [dead]
| Dwolb wrote:
| Therefore they can't optimize anything.
|
| They have 0 visibility into any of the operations and therefore
| can't derive any insights or install controls.
|
| They have no idea if:
|
| Some professors more effective teachers than others.
|
| Which days students buy more food.
|
| Whether taking a given prerequisite leads to better mastery of
| advanced material.
|
| If additional spend is correlated with better student outcomes.
|
| For people in tech businesses, it's such a shocking thing to
| read it's hard to fathom how any of the leadership at
| universities find this situation acceptable.
| jimlongton wrote:
| I didn't go to a prestigious US university but even mine had
| all this data and used it. In tech we like to think we are
| the only ones to "get" data. We are not.
|
| Teaching outcomes are evident from exam results, grants won,
| papers published etc and are routinely used to determine
| promotions, staffing and more. Food/footfall etc are
| monitored and predicted by the private catering companies on
| campus. Budget reviews are comprehensive and require yearly
| and greater analysis of university performance on a per
| department basis. All that doesn't even include competitive
| processes like ranking tables for universities, applications
| for grants, participation in international research groups
| etc etc.
| miguelazo wrote:
| > They have no idea if: Some professors more effective
| teachers than others.
|
| What are you even talking about? Literally every course at
| most universities end with an evaluation. Obviously, there
| are other problematic ways to measure effectiveness
| (publications). How much that data gets used is another
| question and varies widely by institution. "People in tech
| businesses" love to exaggerate the quality of the data they
| have and its usefulness, hence the Subprime Attention Crisis
| that is rapidly eroding their cash on hand as the Fed's cheap
| money and QE dries up.
| lazyasciiart wrote:
| > For people in tech businesses, it's such a shocking thing
| to read it's hard to fathom how any of the leadership at
| universities find this situation acceptable.
|
| I'm in a tech business and it doesn't sound shocking at all.
| The only things people really have data for is how many
| people are giving them how much money.
| bee_rider wrote:
| My school did course evaluations every year, and I'm under
| the impression that the Professor' performance is tracked in
| the sense that if somebody was failing too many students, it
| would be noticed. Beyond that, I'm not sure how one could
| evaluate teaching without also being a subject matter expert.
| So this seems more like the sort of thing that ought to be
| handled by the professor's expert peers, than data people or
| administrators.
| ghiculescu wrote:
| This article feels like it took hundreds of words to say nothing
| at all.
|
| The funniest part was the example of security cameras. What big
| breakthroughs are universities hoping to achieve with this data?
| [deleted]
| mistrial9 wrote:
| some industry players want to normalize facial recognition and
| location tracking
| jimlongton wrote:
| I had to laugh at one of the authors being Christine L.
| _Borg_ man. I'm sure all our data will be treated
| respectfully when fully integrated into their unaccountable
| profit-driven total surveillance system.
| foreverCarlos wrote:
| Security cameras in universities seem to be accomplishing...
| nothing. I am getting very frequent emails (2-3 per week) from
| one of the top US universities with blurry unusable screenshots
| of perpetrators stealing items, breaking in, assaulting
| students, etc. Makes me think - should they not invest into
| better security instead?
| nonrandomstring wrote:
| They're being pushed very hard from the supply side while
| lacking much push-back internally due to dwindling expertise
| and leadership.
|
| This 4 parter I wrote for Techrights [1] and the Times article
| that seeded it [2] go into detail.
|
| [1] http://techrights.org/2022/12/28/andy-farnell-on-british-
| uni...
|
| [2] https://www.timeshighereducation.com/campus/eliminating-
| harm...
| pcrh wrote:
| Universities already waste vast sums of money of vice-provosts,
| middle management, administration, etc.
|
| There's no need to encourage them to start treating their mission
| as one of data mining in order to "capture" more value from the
| students.
| foreverCarlos wrote:
| While I agree with your general skepticism, I also believe
| there are ways these data could benefit the students.
| Extracting value from operational data could mean modernizing
| curriculum, offering more office hours, etc.
| civilized wrote:
| I think most of the value of "data" is captured in empowering
| individual contributors to observe their working conditions
| and the impact of their actions and adapt their strategy in
| response. The power of the central office spreadsheet
| wielders is very secondary. (Which is of course not to say
| that spreadsheets are not valuable, as any teacher knows.)
| foreverCarlos wrote:
| In an ideal world - yes. But seeing how overworked
| professors and other staff members already are in top
| universities, I doubt this is top of mind for them. There
| are courses with hundreds of enrolled students, weekly
| homework, practical projects, etc. IMO somewhat centralized
| DS tools are more suited to handle this load than an
| individual contributor (a professor in this context).
| blthree wrote:
| You have a point about the amount of money spent on admin at
| Universities, but I think you (and most commenters here) don't
| really realize how bad the data situation is at a lot of major
| universities. If we want to streamline admin overhead at public
| universities, there's an incredible amount of time and money
| wasted due to extremely inefficient use/access to data.
|
| I work as a BI dev at a large Big 10 school (50k students/35k
| faculty and staff). For all those students and staff, there are
| a grand total of 3 data engineers, who are responsible for all
| our central data warehouses, including DBAing our oracle and
| redshift DBs. Just to get a new (untransformed) table added to
| the data warehouse from a source takes 6+ months, and that's
| only if it's from a source with an existing integration.
|
| On top of that, there are at least 50 people I know of whose
| job is basically to produce one or two reports manually in
| excel every week, and this isn't even considering people in
| finance or accounting. I'm talking about simple things like how
| many active research grants do we have, or how many students
| have enrolled in certain courses. These are reports (and entire
| fte positions) that could easily be automated with a single SQL
| query. Speaking of SQL, outside of the data engineering team,
| there are only 4 or 5 people who know any SQL out of the 100 I
| know of in reporting/BI positions. The "advanced" data teams
| are using MS access as an ETL tool to pull together data for
| tableau reports.
|
| However, there are a lot of institutional issues that make
| fixing those problems difficult. For one, while we have a
| central IT dept, we also have about 10 individual college-level
| IT teams, which means that data isn't just in different
| databases, but on a whole separate network. For example, if I
| want to create a report on student faculty ratio, I need to
| connect to VPN 1 to export faculty data from redshift, then
| switch to VPN 2 to export student data from Oracle, then switch
| to VPN 3 so that I can upload both datasets to our depts SQL
| server. After all that I can finally write a SQL query to get a
| student faculty ratio. Oh and when we need to update that ratio
| in a month, I'll have to go thru the whole manual extract/load
| process again. Forget automating that, since the network teams
| have no incentive to allow any tunnelling or bridging from one
| network to another.
|
| I could rant about this all day, but I think it's fair to say
| that there are still a ton of low-hanging fruit inefficiency-
| wise at universities. If we could get universities to value
| their data more highly, maybe that wod have the additional
| effect of solving some of these problems and even be a net
| money saver.
| gchallen wrote:
| 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
| standard and important indicator of program health. Next, break
| this down by various cohorts: What is our retention rate among
| women? And so on. Heck, frequently we can't even answer questions
| about the current gender ratio within our program--and this is
| something that has been a focus of our diversity efforts
| recently.
|
| I've had people say with a straight face that we _cannot_
| calculate retention because we don't know when students leave our
| program. But of course someone knows this! And I've been able to
| produce rough estimates even given the limited data that I have
| access to. But a lot of educational data is fairly siloed, and
| frequently the people assigned to perform these tasks don't have
| much training and tend to give up quickly.
|
| I suspect that many departments just don't have anyone assigned
| to do even basic educational data analysis on a regular basis,
| and with access to enough data to run interesting reports. My
| department is in the process of creating a faculty leadership
| role around academic data analytics, but my sense is that this
| will be a very unusual position. (And don't worry--it'll be
| filled by a faculty member, and not a new administrator.)
|
| And don't even get me started about student evaluations of
| teaching. Yes, we give a survey at the end of every semester and
| ask students whether they liked a particular course and
| professor. No, those answers have very little to do with how much
| they actually learned. Yes, we could measure learning in other
| better ways--success in downstream courses, for example. No,
| people don't tend to do that.
|
| There's a lot of room for improvement here, just working with the
| data we already have. No need for additional "telemetric
| signals".
| analog31 wrote:
| 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?
| gchallen wrote:
| > Of course there are lots of rationalizations for why every
| program needs to be unique and special, but does it really
| benefit the students?
|
| Of course not. What would benefit the students would be
| having a lot more standardization so that we compare ideas
| and approaches and determine what works. But the problem with
| standardized evaluation is that half of the programs suddenly
| discover that they aren't in the top half--as most of them
| had previously thought. This seems like more or less what
| happened to standardized testing in K-12 education.
|
| But, in the context of a specific curriculum, having no idea
| what is happening and therefore no way to improve your
| bespoke curriculum is even worse than just deciding to do
| things your own way.
|
| And it's worse than medicine, because at least they have some
| common metrics for what it means to be healthy. Whereas,
| faculty get to assign grades however they want! Imagine you
| ran a diet study where you both controlled the meals and got
| to reposition the numbers on the scale at will.
| nobrains wrote:
| OK, then what?
|
| Then you find out why retention is low.
|
| Then you brainstorm ideas to increase retention.
|
| Then you attempt to apply those ideas. It is at this point that
| the person responsible for applying those ideas says "been
| there done that".
|
| Point is, most data dashboards are non actionable. The
| challenge is to create a good actionable dashboard (i.e. if
| values cross a certain threshold, then the user should take
| some action on it).
|
| Once you create an excellent actionable dashboard, you realize
| it doesn't need a dashboard. It can be a notification.
|
| So, while the data is important, the questions around it might
| just lead to the same work that was being done anyway.
| gchallen wrote:
| > Point is, most data dashboards are non actionable.
|
| Maybe you find out that one specific class or set of classes
| is responsible for a lot of people leaving the program. So
| you zero in on that part of the curriculum and improve it.
| Sounds pretty actionable to me. We've actually done similar
| things on a smaller scale (week by week) to improve student
| success in my course.
|
| But of course you don't know anything until you do the
| analysis.
| josephg wrote:
| > The challenge is to create a good actionable dashboard
|
| Huh? You don't need a dashboard to make use of data. The best
| use of data in my mind is asking and answering questions.
|
| Eg, maybe your program has a low number of graduating female
| engineers. Why? Maybe they're dropping out along the way.
| Maybe female intake is low. With the data you can answer
| these questions.
|
| The graduating rate of female CS students is low, but is it
| abnormally low compared to other schools? You investigate and
| - everyone has an equally low rate except one place where
| it's 50/50. The data has led to a question - Why? What are
| they doing differently? And so on.
| 1auralynn wrote:
| Many times you need the data/visuals to get funding to
| actually implement ideas.
| roenxi wrote:
| In my experience it is generally worse than that. If a
| program has a retention rate of, say, 30% then it isn't like
| that is going to come as a surprise. Data generally reveals
| things that are very obvious. It is easier to read a
| situation by talking to a few people and asking simple
| questions. Which raises further questions about why a data-
| driven approach is needed.
|
| The value in data driven approaches is high, but it takes a
| rare person to figure out why. Traditionally data has
| actually been a communication tool for things that are
| already known. That isn't at all how people expect it to be
| used, everyone seems to anticipate it is used to make
| decisions.
|
| An organisation resisting data is bad news because it will
| struggle to talk about things that everyone knows to be true.
| trop wrote:
| 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.
| MaxBarraclough wrote:
| Shouldn't the finance folks know that kind of thing pretty
| definitively?
| wolverine876 wrote:
| Hazarding an uninformed idea: If organizationally the
| department isn't geared toward this kind of analysis, what if
| you opened up the data, and let whoever wanted to work with it
| do their thing. Probably a perfect environment for crowd-
| sourcing, given the expertise and resources.
| josephg wrote:
| The most interesting data is usually about people. This data
| usually shouldn't be shared for obvious privacy reasons.
|
| And anonymising data sources is a notoriously fraught task.
| zitterbewegung wrote:
| I was in a social network research laboratory. Some of the best
| people at the lab went into industry while the others did do post
| docs. The professor that worked at the lab also went to Microsoft
| Research. Honestly if you were to go the route of being a
| professor or from MANGA what would you choose? Also, I think
| there are laws and also rules within Universities that would
| preclude the study to capture value and even then you would have
| to hire data analysts that would be external to being a professor
| so I think it would be hard to compete with MANGA in that regard
| also.
| nonrandomstring wrote:
| There's a troubling tone behind this article.
|
| It's a prophecy that demands to be fulfilled.
|
| Starting with the premise "Data is useful", it proceeds to pick
| at all the ways we've failed to make it useful... and we're
| damned well going to make it useful if it kills us!
|
| Maybe, just maybe (for those that dabble in the sceptical,
| explanatory game we call science) it might be that "bare data"
| has little use within certain contexts... say those that by
| definition are on the cutting edge of knowledge and reality, best
| steered by imaginative vision of leading experts.
|
| Data driven market cybernetics may be very useful in some
| industries, such as ones with physical logistics, complex supply
| chains, rapidly shifting supply sources and demand sinks. But the
| primacy of "data uber alles" should not be one-size-fits-all.
| lukev wrote:
| There's a cost/benefit curve here.
|
| Having data can undeniably be useful. The question is, how much
| ROI does the insights in the data provide, relative to the cost
| to build the organizational infrastructure capable of
| extracting these insights?
|
| And there's a bootstrapping problem, because often it's hard to
| calculate the potential ROI without actually going through the
| work of building the infrastructure to process the data.
| nonrandomstring wrote:
| Indeed, absolutely true. And furthermore, sometimes the value
| of data, and of possible other sources, doesn't emerge until
| you start analysis and then deductively see new collection
| opportunities. It's a to-and-fro process.
|
| > how much ROI does the insights in the data provide,
| relative to the cost to build the organizational
| infrastructure
|
| Having seen the workings of academia for a long time now I'd
| argue very little.
|
| But I think the problem is more subtle. A Heisenbergian
| problem. When data collection involves people you have a
| triple problem of intrusion, distraction, and ossification.
| the act of trying to create a "data driven culture" in some
| contexts simply kills what's there. The cost of this, in
| addition to the bootstrapping costs of sensors and analytics
| are paralysing.
|
| Academia, by its very nature, if properly functioning,
| demands to be dynamic. If it's good, it will not stand still
| long enough for you to look at it. And if you measure it "too
| hard" it will evaporate as all the good people who resent
| ossification leave.
| stevenally wrote:
| The old "solution looking for a problem" problem.
| xwolfi wrote:
| I think it's simpler: imagine you're the NSA and decide to
| transcribe every phone call ever made to text: you'll spend
| gazillion in storage, connectivity and processing for the
| capture, and spend a gazillion squared on post-capture text
| analysis to find "something", like "who is communist in
| Atlanta", and not even be able to make it find the communists
| pre-emptively, before it's too late and they already donated $3
| to a local chapter.
|
| There can be too much data. There are questions you cannot
| answer even by collecting all data, unless you have no time or
| food cost constraints but the value of your answer will
| decrease with the time distance from the moment the question
| was asked. In a million year you might know for sure who was
| communist in Atlanta during the 2000s, year by year, block by
| block, but you may care less by then.
| wolverine876 wrote:
| I expect Facebook can easily identify most communists are in
| Atlanta.
| Jibbedeyeah wrote:
| One man's troubling tone is another man's dystopic reality.
|
| I read a comment on here months ago about someone who enabled a
| hotel chain to process the data coming from the motion
| activated lights in every hotel. The chain was able to track
| workers with this data and cracked down on people taking too
| long of breaks.
|
| And now a personal anecdote. The business I used to work at had
| an ID badge door you had to use to enter the smoking area. I
| was told by someone I trusted that when layoffs came, a member
| of management asked for a list of the people that used that
| door. The people on that list were prioritized for termination.
|
| "Universities are literally awash in data. From administrative
| data offering information about students, faculty and staff, to
| research data on professors' scholarly activities and even
| telemetric signals"
|
| Why are professors' telemetric signals even being discussed?
| Are publicly listed office hours not enough? My dogs got
| chipped without their consent. Do professors deserve more
| dignity than my dogs?
| taeric wrote:
| This is generalizable. "____ is useful" is rarely (ever?) true
| by itself. Gasoline? Solar Panel? Turbine? Wings? Extra Food?
| Fertilizer?
| [deleted]
| carbocation wrote:
| Oh good - an opportunity to use "begging the question" for its
| original meaning.
|
| I don't see a single example of "success" in the article. (They
| said "We unexpectedly found a pervasive void of infrastructure
| thinking and a relatively limited set of data-informed planning
| successes" but I must have missed the successful example.)
|
| So the entire article describes what they think should be done,
| without any successful examples to show that anything useful can
| be done in the first place.
| colechristensen wrote:
| Honestly, down with data science. Down with university
| administration.
|
| Stop running dragnet data collection on unwilling participants
| and stop using it to try to "optimize" your interactions with
| them.
|
| This data shouldn't exist, it's value shouldn't be exploited.
| lifeisstillgood wrote:
| Because it does not "belong" to them. It belongs to all science
| and all society, whom will through creative destruction find ways
| to extract the value for everyone.
|
| The same applies to corporations, landholders, monarchs and
| parliaments and governments.
|
| New year is bringing out my fundamentals :-)
| wjnc wrote:
| Obviously I'm 20 years past university and nearer being the
| paying parent, but why oh why should university "capture the
| value of data". They should offer affordable education (if you
| believe in the value of tertiary education) and affordable
| signaling of abilities (if you don't). A recent post by John
| Cochrane [1] somewhat pointed out the absurdity in US education,
| Stanford specifically. Stanford itself mentions 15750 non
| teaching staff members on 2288 professors. My kids K12 (EU) runs
| on about 6 administrators (excluding cleaning, but including all
| other staff) for 22 teaching staff, including upstream shared
| services in the city. In a high school the ratio is probably even
| better.
|
| 15750 administrators wanting to capture the value of data is just
| plain silly. (I'm going overboard here, but it's about an order
| of magnitude of difference! I know of 50% differences in
| FTE/value in the market I work in, but x10...)
|
| [1] https://johnhcochrane.blogspot.com/?m=1
| Retric wrote:
| Public schools depend on a lot of administrative support
| outside the physical school its self. Buss drivers for example
| aren't managed at the individual school level because they
| transport students to multiple different schools.
|
| This extends through a huge range of administrative functions
| for everything from calling snow days to collecting taxes to
| pay for the school etc.
| ordersofmag wrote:
| A big chunk of that 15,000 are professionals engaged in
| research activities (mostly funded by grants and other external
| funds) e.g. the 1600 folks involved in running the Stanford
| Linear Accelerator, and activities like fundraising and
| finances. They mostly are NOT folks 'administering' the faculty
| in any way. So the comparison with your kid's school is not
| meaningful. Not to say there are perhaps more administrators
| than might be ideal. But these numbers aren't helpful in
| understanding whether that's true.
|
| Also, it's worth noting that Stanford is in fact free for folks
| under the median household income in the U.S. (roughly
| speaking) which seems pretty 'affordable'. Of course the
| economics of one of these large-endowment, research intensive
| institutions is pretty much unrelated to their teaching
| function. But that just highlights the weakness of using gross,
| whole-institution numbers of people/dollars for any sort of
| comparison. Big universities serve lots of ends (not just
| teaching undergrads) and so the teasing apart the economic
| picture (and whether it's efficient at meeting it's many goals)
| is complicated.
| MengerSponge wrote:
| Oh, so we need a Vice-provost of Data. That's what is keeping
| universities, otherwise homogenous and well structured, from
| aligning on data infrastructure and strategy.
| civilized wrote:
| 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 I'm in this line of work.
| nonrandomstring wrote:
| > 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 uncomfortably beneath the
| umbrella "Data science"?
|
| I think the answer is found by asking about the _ends_ of data
| science, the old Cui Bono?
|
| There's the raw entertainment value you mention. It's cool to
| have knowledge and visualise it. Sensors, transducers,
| processing is fun.
|
| Then there's legibility. That is political and is about
| control.
|
| What most _scientists_ are doing with data is either hypothesis
| testing or combing for causal relations to then abductively
| feed back into hypothesis generation.
|
| What most business people are trying to do is _optimise_ , and
| adjust constraints and parameters. It's modelling for the most-
| part. It's ancient and goes back to linear analysis and
| regression from before the last century.
|
| Security people are looking for stress signifiers, suspicious
| patterns with various triggers, selectors and tripwires.
|
| Financial people want fortune telling. They want the models to
| extrapolate into beautiful hockey sticks.
|
| Within any organisation we may need to do one, a few, many or
| none at all of the above. The problem then is that "Valuable
| data" is such a broad, open prospect it seduces gushing,
| credulous administrators into valuing the process, and the
| tools, but not the ends.
| judge2020 wrote:
| The job description for a data scientist isn't necessarily
| consistent across companies, but I can imagine a good
| description would be to act like a real scientist in that you
| collaborate with product teams to figure out what experiments
| to run, what data to collect, and how to visualize that for
| both the product and business team to decide on their
| approach to make the company more money, ie. specializing in
| a/b testing and experimentation. A problem I see is creating
| a data science team just to follow the trends and
| specifically requesting they try to "study user behavior" or
| something in hopes the data shows some underlying trend that
| triggers an "eureka!" moment.
| selimnairb wrote:
| 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.
| civilized wrote:
| Already taken. Data scientists are data engineers, and data
| engineers are data warehouse workers and janitors.
| nerdponx wrote:
| Not at all. Data engineers are data engineers. Data
| scientists are a mix of statisticians, machine learning
| researchers, and data analysts.
| foreverCarlos wrote:
| Agreed! It all probably started with big tech promoting the
| "data-driven decision" paradigm. Of course, in many cases this
| approach is effective, but it's not a panacea and has its
| limits. It's tempting to interpret availability of any data as
| an amazing untapped resource, but in many cases analyzing it is
| just a massive waste of resources and could be more effectively
| replaced with traditional tools (surveys and such).
| shagie wrote:
| > but there is an addictive, video game-like quality
|
| Have you looked at the Financial Modeling World Cup?
| https://www.youtube.com/channel/UCOlnCUAKLENyFC8wftR-oNw
|
| Its tagline is: "Excel Esports. Yes, It's a thing"
| porknubbins wrote:
| You perfectly described why I became disillusioned with any
| kind of data science job. The feeling that most of the time
| data is used to support preexisting bias and can be arranged to
| say anything you want. Interesting that you know this and enjoy
| it despite that.
| comfypotato wrote:
| Only slightly related to your very funny comment here, you
| might enjoy "The Visual Display of Quantitative Information" by
| Edward Tufte. Not only does he help you to best make the
| abstract shapes and colors, but there's an added aspect of the
| visuals that tickles the mind. That added aspect is how easily
| some information is communicated along with the image. It's
| along the lines of how sometimes someone can tell a joke just
| by saying something as concisely as possible (even if the
| content isn't humorous).
|
| Like you said, whether or not the visuals provide any value is
| a completely different line of discussion.
| koz_ wrote:
| 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.
| seydor wrote:
| Sounds like finance and advertising
| lukev wrote:
| I have no doubt the problem is _worse_ in academia, but to be
| quite honest nobody is really doing a good job at curating,
| governing and unifying data across large organizations (at least
| without extreme expense.)
|
| Just look at the treadmill of architectures designed to "solve"
| this problem: integrated databases to "data warehouses" to
| "linked data" to "data lakes" to "data fabric" to "data mesh."
|
| Organizations that succeed do so because they have the resources
| to prioritize data management, and do so at significant team size
| and expense. But any organization that doesn't (or can't) view
| data curation as first class, top-line budget isn't in a position
| to capture even a small part of the theoretical value of their
| data.
|
| Consider the "data mesh" architecture, the latest fad in this
| space. It basically assumes that not only do you have a dedicated
| data curation team, each data source also has it's own dedicated
| team of curators capable of productizing data sources.
|
| That kind of thing is simply out of reach for most organizations.
| jerglingu wrote:
| Observation/mini rant: unfortunately the data industry is both
| very susceptible to fads and hype, and does not have widespread
| standardization or a generally-accepted set of best practices.
| The subset of data "influencers" whose discourse occurs mostly
| through Twitter and Substack have especially heavy sway in what
| is the current Data Big Thing. These people introduce new
| buzzwords and ride them to the mainstream, or redefine what
| were previously more-or-less anchored down concepts into new
| interpretations. It feels tepid and arbitrary, almost
| postmodern. On top of this, much of the "thought leadership" is
| being driven by individuals who lean heavily towards the soft-
| skill side. So we have a heavy overindexing on strong opinions
| around organizational methodologies, team structure and
| roles/responsibilities, and other Big Ideas without much
| engineering representation or consideration. What spawns from
| this are things like the "data mesh" and the bastardization of
| both data concepts and clear communication/thinking. The data
| mesh white paper perfectly encapsulates this[1]. I challenge
| anybody to try to read it and understand what the hell the
| author is even vaguely trying to say after a few passes.
|
| [1]: https://martinfowler.com/articles/data-mesh-
| principles.html
| lukev wrote:
| I do see what you are saying, though I think a lot of what
| they say makes somewhat more sense if you are familiar with
| their consulting "bubbles" and the language used within them.
|
| But the problem exists even on a concrete technical level.
| Look at how many different "big data" products are out there.
| The Apache project _alone_ has probably 15 different tools
| that largely overlap.
| le-mark wrote:
| 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 would entail.
| They jumped aboard the hype train with gusto. Each quarter
| progress was reported and margins improved with a clear upward
| trend. From the trenches it was clear that even with the
| investment made, it was still not enough to reach any of the
| goals.
|
| After a year of "modernization" the company was acquired, the
| buyer literally salivating over the coming profit margin
| increase and "synergies" in selling into related markets. After
| another year post acquisition it became clear that the benefits
| were simply out of reach, and the profits did not arrive. The
| acquiring company was left with the low margin business it had
| always been. Layoffs ensued as the buyer sought to salvage
| something from the transaction.
|
| 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, masterfully executed.
| hammock wrote:
| >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?
| le-mark wrote:
| > masterfully executed.
|
| I feel like this part you omitted clarifies my opinion on
| the matter.
| WrtCdEvrydy wrote:
| Same as it ever was.
| bee_rider wrote:
| It seems like a somewhat weird mismatch -- the described benefits
| are things like curriculum improvements and hiring better
| instructors, which I think most people (including most
| professors, I think, despite the fact that they'd be on the
| receiving end of this) would be in favor of.
|
| But including security cameras in the list of tools seems... odd?
| What would they tell you? Classroom attendance I guess. But all
| that tells you is that the instructor is either so bad that
| nobody bothers showing up, or so good that their slides,
| recordings, etc are enough for the students to skip class
| sometimes. (And if you measure the number of students in the
| classroom, attendance will just become mandatory, this is dumb).
|
| In general universities should be pretty inefficient I think.
| They are places where young people go to learn and try things
| out. Including student employees. If a university has no waste,
| that indicates that it hasn't given enough possibly-unqualified
| undergrads enough resources to accidentally misuse.
| yucky wrote:
| > In general universities should be pretty inefficient I think.
| They are places where young people go to learn and try things
| out. Including student employees. If a university has no waste,
| that indicates that it hasn't given enough possibly-unqualified
| undergrads enough resources to accidentally misuse.
|
| The problem is all the extra money (yes, pretty much all of it)
| has gone to building up the administrations, not into resources
| that actually help students. And the students certainly haven't
| turned out smarter as a result.
|
| Universities have turned into a jobs program for people looking
| to work in education administration.
| amelius wrote:
| University data is getting outdated very quickly. Large companies
| have taken over the role of data curators.
|
| I personally think this is wrong. Big tech companies should build
| hardware, while government agencies should use that hardware to
| curate our data. Like how it was near the beginning of the
| internet. Companies should be as far away from our data as
| possible.
| robertlagrant wrote:
| I think the exact opposite: government should know as little as
| is required to provide its services, as should any company. If
| you imagine a company can do damage with your data, wait til a
| bad government has it.
| [deleted]
| paultopia wrote:
| 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.
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