Posts Tagged ‘“University”’

Revisiting IT Policy #3: Harassment

OwlBThe so-called “star wars” campuses of the mid-1980s (Brown, Carnegie Mellon, Dartmouth, and MIT) invented (or at least believe they invented–IT folklore runs rampant) much of what we take for granted and appreciate today in daily electronic life: single signon, secure authentication, instant messaging, cloud storage, interactive online help, automatic updates, group policy, and on and on.

They also invented things we appreciate less. One of those is online harassment, which takes many forms.

Early in my time as MIT’s academic-computing head, harassment seemed to be getting worse. Partly this was because the then-new Athena computing environment interconnected students in unprecedentedly extensive ways, and partly because the Institute approached harassment purely as a disciplinary matter–that is, trying to identify and punish offenders.

Those cases rarely satisfied disciplinary requirements, so few complaints resulted in disciplinary proceedings. Fewer still led to disciplinary action, and of course all of that was confidential.

Stopit

imgresWorking with Mary Rowe, who was then the MIT “Ombuds“, we developed a different approach. Rather than focus on evidence and punishment, we focused on two more general goals: making it as simple as possible for victims of harassment to make themselves known, and persuading offenders to change their behavior.

The former required a reporting and handling mechanism that would work discreetly and quickly. The latter required something other than threats.

stopit poster (2)Satisfying the first requirement was relatively simple. We created an email alias (stopit@mit.edu) to receive and handle harassment (and, in due course, other) complaints.  Email sent to that address went to a small number of senior IT and Ombuds staff, collectively known as the Stopits. The duty Stopit–often me–responded promptly to each complaint, saying that we would do what we could to end the harassment.

We publicized Stopit widely online, in person, and with posters. In the poster and other materials, we gave three criteria for harassment:

  • Did the incident cause stress that affected your ability, or the ability of others, to work or study?
  • Was it unwelcome behavior?
  • Would a reasonable person of your gender/race/religion subjected to this find it unacceptable?”

Anyone who felt in danger, we noted, should immediately communicate with campus police or the dean on call, and we also gave contact information for other hotlines and resources. Otherwise, we asked that complainants share whatever specifics they could with us, and promised discretion under most circumstances.

To satisfy the second requirement, we had to persuade offenders to stop–a very different goal, and this is the key point, from bringing them to justice. MIT is a laissez-faire, almost libertarian place, where much that would be problematic elsewhere is tolerated, and where there is a high bar to formal action.

As I wrote in an MIT Faculty Newsletter article at the time, we knew that directly accusing offenders would trigger demands for proof and long, futile arguments about the subtle difference between criticism and negative comments–which are common and expected at the Institute–and harassment. Prosecution wouldn’t address the problem.

UYA

And so we came up with the so-called “UYA” note.

“Someone using your account…”, the note began, and then went on to describe the alleged behavior. “If you did not do this,” the note went on, “…then quite possibly someone has managed to access your account without permission, and you should take immediate steps to change your password and not share it with anyone.” The note then concluded by saying “If the incident described was indeed your doing, we ask that you avoid such incidents in the future, since they can have serious disciplinary or legal consequences”.

keep-calm-and-change-your-password-1Almost all recipients of UYA notes wrote back to say that their accounts had indeed been compromised, and that they had changed their passwords to make sure their accounts would not be used this way again. In virtually all such cases, the harassment then ceased.

Did we believe that most harassment involved compromised accounts, and that the alleged offenders were innocent? Of course not. In many cases we could see, in logs, that the offender was logged in and doing academic work at the very workstation and time whence the offending messages originated. But the UYA note gave offenders a way to back off without confession or concession. Most offenders took advantage of that. Our goal was to stop the harassment, and mostly the UYA note achieved that.

heatherThere was occasional pushback, usually the offender arguing that the incident was described accurately but did not constitute harassment. Here again, though, the offending behavior almost always ceased. And in a few cases there was pushback of the “yeah, it’s me, and you can’t make me stop” variety. In those, the Stopits referred the incident into MIT’s disciplinary process. And usually, regardless of whether the offender was punished, the harassment stopped.

So Stopit and UYA notes worked.

Looking back, though, they neglected some important issues, and those remain problematic. In fact, the two teaching cases I mentioned in the Faculty Newsletter article and have used in myriad class discussions since–Judy and Michael–reflect two such issues: the difference between harassment and a hostile work environment, and jurisdictional ambiguity.

Work Environment

fishbowl.57Judy Hamilton complains that images displayed on monitors in a public computing facility make it impossible for her to work comfortably. This really isn’t harassment, since the offending behavior isn’t directed at her. Rather, the offender’s behavior made it uncomfortable for Judy to work even though the offender was unaware of Judy or her reaction.

The UYA note worked: the offender claimed that he’d done nothing wrong, and that he had every right to display whatever images he chose so long as they weren’t illegal, but nevertheless he chose to stop.

But it was not correct to suggest that he was harassing Judy, as we did at the time. Most groups that have discussed this case over the years come to that conclusion, and instead say this should have been handled as a hostile-work-environment case. It’s an important distinction to keep in mind.

Jurisdiction

001Michael Zareny, on the other hand, is interacting directly with Jack Oiler, and there’s really no work environment involved. Jack feels harassed, but it’s not clear Michael’s behavior satisfies the harassment criteria. Jack appears to be annoyed, rather than impaired, by Michael’s comments. In any case the interaction between the two would be deemed unfortunate, rather than unacceptable, by many of Jack’s peers.

Or, and this is a key point, the interaction would be seen that way by Jack’s peers at MIT. There’s an old Cambridge joke: At Harvard people are nice to you and don’t mean it, and MIT people aren’t nice to you and don’t mean it. The cultural norms are different. What is unacceptable to someone at Harvard might not be to someone at MIT. So arises the first jurisdictional ambiguity.

In the event, the Michael situation turned out to be even more complicated. When Kim tried to send a UYA note to Michael, it turned out that there was no Michael Zareny at MIT. Rather, it turned out that Michael Zareny was a student elsewhere, and his sole MIT connection was interacting with Jack Oiler in an the newsgroup.

There thus wasn’t much Kim could do, especially since Michael’s own college declined to take any action because the problematic behavior hadn’t involved its campus or IT.

Looking Ahead

The point to all this is straightforward, and it’s relevant beyond the issue of harassment. In today’s interconnected world, it’s rare for problematic online behavior to occur within the confines of a single institution. As a result, taking effective action generally requires various entities to act consistently and collaboratively to gather data from complainants and dissuade offenders.

Yet the relevant policies are rarely consistent from campus to campus, let alone between campuses and ISPs, corporations, or other outside entities. And although campuses are generally willing to collaborate, this often proves difficult for FERPA, privacy, and other reasons.

It’s clear, especially with all the recent attention to online bullying and intimidation, that harassment and similarly antisocial behavior remain a problem for online communities. It’s hard to see how this will improve unless campuses and other institutions work together. If they don’t do that, then external rules–which most of us would prefer to avoid–may well make it a legal requirement.

You Report. We Decide?

botstein “It’s one of the real black marks on the history of higher education, ” Leon Botstein, the long-time President of Bard College, recently told The New Yorker’s Alice Gregory, “that an entire industry that’s supposedly populated by the best minds in the country … is bamboozled by a third-rate news magazine.” He was objecting, of course, to the often criticized but widely influential rankings of colleges and universities by US News & World Reports.

Two stories, and a cautionary note.

Wired

leydonSeeing Wired magazine‘s annual “wired campus” rankings in the same way Botstein viewed those from US News, some years ago several of us college and university CIOs conspired to disrupt Wired‘s efforts. As I later wrote, the issue wasn’t that some campuses had different (and perhaps better or worse) IT than others. Rather, for the most part these differences bore little relevance to the quality of those campuses’ education or the value they provided to students.

wiredWe persuaded almost 100 key campuses to withhold IT data from Wired. After meeting with us to see whether compromise was possible (it wasn’t) and an abortive attempt to bypass campus officials and gather data directly from students, the magazine discontinued its ratings. Success.

But, as any good pessimist knows, every silver lining has a cloud. Wired had published not only summary ratings, but also, to its credit, the data (if not the calculations) upon which the ratings were based. Although the ratings were questionable, and some of the data seemed suspect, the latter nevertheless had some value. Rather than look at ratings, someone at Campus A could look and see how A’s reported specific activity compared to its peer Campus B’s.

Partly to replace the data Wired had gathered and made available, and so extend A’s ability to see what B was doing, EDUCAUSE started the Core Data Survey (now the Core Data Service, CDS). This gathered much of the same information Wired had, and more. (Disclosure: I served on the committee that helped EDUCAUSE design the initial CDS, and revised it a couple of years later, and have long been a supporter of the effort.)

Unlike Wired, EDUCAUSE does not make individual campus data publicly available. Rather, participating campuses can compare their own data to those of all or subsets of other campuses, using whatever data and comparison algorithm they think appropriate. I can report from personal experience that this is immensely useful, if only because it stimulates and focuses discussions among campuses that appear to have made different choices.

cds postitBut back to Botstein. EDUCAUSE doesn’t just make CDS data available to participating campuses. It also uses CDS data to develop and publish “Free IT Performance Metrics,” which it describes as “Staffing, financials, and services data [campuses] can use for modifications, enhancements, and strategic planning.” The heart of Botstein’s complaint about US News & World Reports  isn’t that the magazine is third rate–that’s simply Botstein being Botstein–but rather that US News believes the same rating algorithm can be validly used to compare campuses.

Which raises the obvious question: Might EDUCAUSE-developed “performance metrics” fall into that same trap? Are there valid performance metrics for IT that are uniformly applicable across higher education?

mckMany campuses have been bedeviled and burned by McKinseys, BCGs, Accentures, Bains, PWCs, and other management consultants. These firms often give CFOs, Provosts, and Presidents detailed “norms” and “standards” for things like number of users per help-desk staffer, the fraction of operating budgets devoted to IT, or laptop-computer life expectancy. These can then become targets for IT organizations, CIOs, or staff in budget negotiations or performance appraisal.

Some of those “norms” are valid. But many of them involve inappropriate extrapolation from corporate or other different environments, or implicitly equate all campus types. Language is important: “norms,” “metrics,” “benchmarks,” “averages,” “common”, “typical,” and “standards” don’t mean the same thing. So far EDUCAUSE has skirted the problem, but it needs to be careful to avoid asserting uniform validity when there’s no evidence for it.

US News

lake desertA second story illustrates a different, more serious risk. A few years ago a major research university–I’ll call it Lake Desert University or LDU–was distressed about its US News ranking. To LDU’s leaders, faculty, and students the ranking seemed much too low: Lake Desert generally ranked higher elsewhere.

patA member of the provost’s staff–Pat, let’s say–was directed to figure out what was wrong. Pat spent considerable time looking at US News data and talking to its analysts. An important component of the US News ranking algorithm, Pat learned, was class size. The key metric was the fraction of campus-based classes with enrollments smaller than 20.

tutorialPat, a graduate of LDU, knew that there were lots of small classes at Lake Desert–the university’s undergraduate experience was organized around tutorials with 4-5 students–and so it seemed puzzling that LDU wasn’t being credited for that. Delving more deeply, Pat found the problem. Whoever had completed LDU’s US News questionnaire had read the instructions very literally, decided that “tutorials” weren’t “classes”, and so excluded them from the reporting counts. Result: few small classes, and a poor US News ranking.

usnewsUS News analysts told Pat that tutorials should have been counted as classes. The following year, Lake Desert included them. Its fraction-of-small-classes metric went up substantially. Its ranking jumped way up. The Provost sent Pat a case of excellent French wine.

In LDU’s case, understanding the algorithm and looking at the survey responses unearthed a misunderstanding. Correcting this involved no dishonesty (although some of LDU’s public claims about the “improvement” in its ranking neglected to say that the improvement had resulted from data reclassification rather than substantive progress).

Caution

But not all cases are as benign as LDU’s . As I wrote above, there were questions not only about Wired‘s ranking algorithm, but about some of the data campuses provided. Lake Desert correcting its survey responses in consultation with analysts is one thing; a campus misrepresenting its IT services to get a higher ranking is another. But it can be hard to distinguish the two.

whistleAuditing is one way to address this problem, but audits are expensive and difficult. Publishing individual responses is another–both Wired and US News have done this, and EDUCAUSE shares them with survey respondents–but that only corrects the problem if respondents spend time looking at other responses, and are willing to become whistleblowers when they find misrepresentation. Most campuses don’t have the time to look at other campuses’ responses, or the willingness to call out their peers.

If survey responses are used to create ratings, and those ratings become measures of performance, then those whose performance is being measured have incentive to tailor their survey responses accordingly. If the tailoring involves just care within the rules, that’s fine. But if it involves stretching or misrepresenting the truth, it’s not.

More generally, it’s important to closely connect the collection of data to their evaluative use. Who reports, should decide.

 

 

 

Mythology, Belief, Analytics, & Behavior

MIT_Building_10_and_the_Great_Dome,_Cambridge_MAI’m at loose ends after graduating. The Dean for Student Affairs, whom I’ve gotten to know through a year of complicated political and educational advocacy, wants to know more about MIT‘s nascent pass/fail experiment, under which first-year students receive written rather than graded evaluations of their work.

MIT being MIT, “know more” means data: the Dean wants quantitative analysis of patterns in the evaluations. I’m hired to read a semester’s worth, assign each a “Usefulness” score and a “Positiveness” score, and then summarize the results statistically.

Two surprises. First, Usefulness turns out to be much higher than anyone had expected–mostly because evaluations contain lots of “here’s what you can do to improve” advice, rather than lots of terse “you would have gotten a B+” comments, as had been predicted. Second, Positiveness distributes remarkably as grades had for the pre-pass/fail cohort, rather than skewing higher, as had been predicted. Even so, many faculty continue to believe both predictions (that is, they think written evaluations are both generally useless and inappropriately positive).

20120502161716-1_0A byproduct of the assignment is my first exposure to MIT’s glass-house computer facility, an IBM 360 located in the then-new Building 39. In due course I learn that Jay Forrester, an MIT faculty member, had patented the use of 3-D arrays of magnetic cores for computer memory (the read-before-write use of cores, which enabled Forrester’s breakthrough, had been patented by An Wang, another faculty member, of the eponymous calculators and word processors). IBM bought Wang’s patent, but not Forrester’s, and after protracted legal action eventually settled with Forrester in 1964 for $13-million.

According to MIT mythology, under the Institute’s intellectual-property policy half of the settlement came to the Institute, and that money built Building 39. Only later do I wonder whether the Forrester/IBM/39 mythology is true. But not for long: never let truth stand in the way of a good story.

Not just because mythology often involves memorable, simple stories, belief in mythology is durable. This is important because belief so heavily drives behavior. That belief resists even data-driven contradiction–data analysis rarely yields memorable, simple stories–is one reason analytics so often prove curiously ineffective in modifying institutional behavior.

Two examples, both involving the messy question of copyright infringement by students and what, if anything, campuses should do about it.

44%

laurelI’m having lunch with a very smart, experienced, and impressive senior officer from an entertainment-industry association, whom I’ll call Stan. The only reason universities invest heavily in campus networks, Stan tells me, is to enable students to download and share ever more copyright-infringing movies, TV shows, and music. That’s why campuses remain major distributors of “pirated” entertainment, he says, and therefore why it’s appropriate to subject higher education generally to regulations and sanctions such as the “peer to peer” regulations from the 2008 Higher Education Opportunity Act.

That Stan believes this results partly from a rhetorical problem with high-performance networks, such as the research networks within and interconnecting colleges and universities. High-performance networks–even those used by broadcasters–usually are engineered to cope with peak loads. Since peaks are occasional, most of the time most network capacity goes unused. If one doesn’t understand this–as Stan doesn’t–then one assumes that the “unused” capacity is in fact being used, but for purposes not being disclosed.

And, as it happens, there’s mythology to fill in the gap: According to a 2005 MPAA study, Stan tells me, higher education accounts for almost half of all copyright infringement. So MPAA, and therefore Stan, knows what campuses aren’t telling us: they’re upgrading campus networks to enable infringement.

But Stan is wrong. There are two big problems with his belief.

MPAAFirst, shortly after MPAA asserted, both publicly and in letters to campus presidents, that 44% of all copyright infringement emanates from college campuses, which is where Stan’s “almost half” comes from, MPAA learned that its data contractor had made a huge arithmetic error. The correct estimate should have been more like 10-15%. But the corrected estimate was never publicized as extensively as the erroneous one: the errors that statisticians make live after them; the corrections are oft interred with their bones.

Second, if Stan’s belief is correct, then there should be little difference among campuses in the incidence of copyright infringement, at least among campuses with research-capable networking. Yet this isn’t the case. As I’ve found researching three years of data on the question, the distribution of detected infringement is highly skewed. Most campuses are responsible for little or no distribution of infringing material, presumably because they’re using Packetlogic, Palo Alto firewalls, or similar technologies to manage traffic. Conversely, a few campuses account for the lion’s share of detected infringement.

So there are ample data and analytics contradicting Stan’s belief, and none supporting it. But his belief persists, and colors how he engages the issues.

Targeting

imagesOKVW44NDI’m having dinner with the CIO from an eminent research university; I’ll call her Samantha, and her campus Helium (the same name it has in the infringement-data post I cited above). We’re having dinner just as I’m completing my 2013 study, in which Helium has surpassed Hydrogen as the largest campus distributor of copyright-infringing movies, TV shows, and music.

In fact, Helium accounts for 7% of all detected infringement from the 5,000 degree-granting colleges and universities in the United States. I’m thinking that Samantha will want to know this, that she will try to figure out what Helium is doing–or not doing–to stand out as such a sore thumb among peer campuses, and perhaps make some policy or practice changes to bring Helium into closer alignment.

But no: Samantha explains to me that the data are entirely inaccurate. Most of the infringement notices Helium receives are duplicates, she tells me, and in any case the only reason Helium receives so many is that the entertainment industry intentionally targets Helium in its detection and notification processes. Since the data are wrong, she says, there’s no need to change anything at Helium.

I offer to share detailed data with Helium’s network-security staff so that they can look more closely at the issue, but Samantha declines the offer. Nothing changes, and in 2014 Helium is again one of the top recipients of infringement notices (although Hydrogen regains the lead it had held in 2012).

The data Samantha declines to see tell an interesting story, though. The vast majority of Helium’s notices, it turns out, are associated with eight IP addresses. That is, each of those eight IP addresses is cited in hundreds of notices, which may account for Samantha’s comment about “duplicates”. Here’s what’s interesting: the eight addresses are consecutive, and they each account for about the same number of notices. That suggests technology at work, not individuals.

image0021083244899217As in Stan’s case, it helps to know something about how campus networks work. Lots of traffic distributed evenly across a small number of IP addresses sounds an awful lot like load balancing, so perhaps those addresses are the front end to some large group of users. “Front end to some large group of users” sounds like an internal network using Network Address Translation (NAT) for its external connections.

NAT issues numerous internal IP addresses to users, and then technologically translates those internal addresses traceably into a much smaller set of external addresses. Most campuses use NAT to conserve their limited allocation of external IP addresses, especially for their campus wireless networks. NAT logs, if kept properly, enable campuses to trace connections from insiders to outside and vice versa, and so to resolve those apparent “duplicates”.

So although it’s true that there are lots of duplicate IP addresses among the notices Helium receives, this probably stems from Helium’s use of NAT on its campus wireless. Helium’s data are not incorrect. If Helium were to manage NAT properly, it could figure out where the infringement is coming from, and address it.

Samantha’s belief that copyright holders target specific campuses, like Stan’s that campuses expand networks to encourage infringement, has a source–in this case, a presentation some years back from an industry association to a group of IT staff from a score of research universities. (I attended this session.) Back then, we learned, the association did target campuses, not out of animus, but simply as a data-collection mechanism. The association would choose a campus, look for infringing material being published from the campus’s network, send notices, and then move on to another campus.

utorrent-facebook-mark-850-transparentSince then, however, the industry had changed its methodology, in large part because the BitTorrent protocol replaced earlier ones as the principal medium for download-based infringement. Because of how BitTorrent works, the industry’s methodology shifted from searching particular networks to searching BitTorrent indexes for particularly popular titles and then seeing which networks were making those titles available.

I spent lots of time recently with the industry’s contractors looking closely at that methodology. It appears to treat campus networks equivalently to each other and to commercial networks, and so it’s unlikely that Helium was being targeted as Samantha asserted.

If Samantha had taken the infringement data to her security staff, they probably would have discovered the same thing I did, and either used NAT data to identify offenders, or perhaps to justify policy changes for the wireless network. Same goes for exploring the methodology. But instead Samantha relied on her belief that the data were incorrect and/or targeted

Promoting Analytic Effectiveness

Because of Stan’s and Samantha’s belief in mythology, their organizations’ behavior remains largely uninformed by analytics and data.

decision-treeA key tenet in decision analysis holds that information has no value (other than the intrinsic value of knowledge) unless the decisions an individual or an institution have before them will turn out differently depending on the information. That is, unless decisions depend on the results of data analysis, it’s not worth collecting or analyzing data.

Colleges, universities, and other academic institutions have difficulty accepting this, since the intrinsic value of information is central to their existence. But what’s valuable intrinsically isn’t necessarily valuable operationally.

Generic praise for “data-based decision making” or “analytics” won’t change this. Neither will post-hoc documentation that decisions are consistent with data. Rather, what we need are good, simple stories that will help mythology evolve: case studies of how colleges and universities have successfully and prospectively used data analysis to change their behavior for the better. Simply using data analysis doesn’t suffice, and neither does better behavior: we need stories that vividly connect the two.

Ironically, the best way to combat mythology is with–wait for it–mythology…

The evil that men do lives after them. The good is oft interred with their bones.

- Exterior  GeneralLunch with an old friend, beautiful day in Washington, seated outdoors enjoying surprisingly excellent hamburgers. We’re going to talk about our kids, and what we’re doing this summer, and maybe even about working together on a project some day (as we did decades ago).

But as is so often the case for those of us who work in IT, first there’s a technical question about calendars on his iPhone. He’s not clear on the distinction between the iCloud calendar and the one installed by his campus IT group.

I clarify that one is personal and the other enterprise. That segues into a discussion of calendar/email/contacts services (somewhat inexplicably, his campus still uses Notes), and then into IT services and help desks.

My friend observes that his campus provides an excellent array of IT equipment, software (Notes excepted),  and services. But it also has one of those “your call will be handled by the next available representative” queuing systems on its IT help desk.

Cobbe_portrait_of_Shakespeare“I really hate that,” my friend says, as I swipe some of his sweet-potato fries. Because he so dislikes the queuing system, he says, he can’t think positively about his campus’s IT, no matter how good the rest of it is. The evil that men do lives after them; the good is oft interred with their bones. (Why is the Bard on my mind? Because at home we’ve been watching the excellent BBC/PBS Shakespeare Uncovered series on Netflix.)

It’s a familiar refrain. I’ve just been rereading a 1999 article with advice for new CIOs, where I had this to say:

Information technology most often succeeds when it is invisible–when people do not realize they are using it and focus on larger goals. When you and your staff do things right, even spectacularly, no one will notice. This is immensely frustrating. The only comments you are ever going to hear–from the big bosses, from faculty, from staff, from the student newspaper–will be negative, sometimes vitriolically so. This will drive you crazy. No one outside IT at the institution will sympathize.

We like to think this is peculiar to IT. It isn’t.

sct logoCase in point: Registrars. During my tenure at the University of Chicago, we replaced an old terminal-based student system for staff only with a highly flexible, modern web-based system directly accessible by students, faculty, and staff. Students used to wait in line to give their class choices to Registrar clerks, who would then set class lists and enter data in the system manually. Grading, transcripts, and other processes were similar. No one was happy except the Registrar, whose staff and budget necessarily remained large.

The new system (now-defunct SCT‘s now-defunct Matrix product) changed everything: no more waiting in line, simpler scheduling, later deadlines for grades, online transcript requests, you name it. Asked about specifics, almost everyone described almost everything as better.

But no one seemed to feel any better about the University than they had before.

Irving_Frederick_Herzberg_y_sus_teorias_de_motivacion_en_el_trabajoIrving_Frederick_Herzberg_y_sus_teorias_de_motivacion_en_el_trabajoIrving_Frederick_Herzberg_y_sus_teorias_de_motivacion_en_el_trabajo herzAt lunch, my friend pointed to this apparent conundrum as an interesting parallel to “two-factor theory,” the suggestion by Frederick Herzberg that job satisfaction and job dissatisfaction are independent of each other. The Registrar’s customers were less dissatisfied, but that did not mean they were more satisfied.

Messier case in point: Business travel. Time was, one made business-travel arrangements by calling (or having one’s assistant call) a travel agency or travel office to make reservations and get a travel advance, and one accounted for the advance and/or got reimbursed for out-of-pocket expense by filling out (or having one’s assistant fill out) a form, attaching paper receipts to it, mailing it somewhere, and eventually receiving a check.

Concur_Logo_VT_Color_500px--1-Today it’s much more typical to make one’s own reservations through an employer-provided website, to pay expenses with a credit card that charges the employer directly, to account for expenses through the same dedicated website, and to have any reimbursement deposited directly. This all goes much faster, and is much more cost-effective for the employer.

For those of us who like rolling our own, it’s also much more appealing. But for those who don’t, and who don’t have assistants, it’s more awkward and burdensome.

We implemented a modern travel system (Concur) while I was at UChicago. I know anecdotally that most users liked its speed and convenience, but the public reaction consisted largely of complaints (most of which really weren’t about the travel system, but rather about the loss of departmental secretaries as the University did away with them in favor of centralized clerical support).

Coincidentally, my current employer switched to Concur from a paper-based system shortly before I arrived, and I observe the same pattern: widespread private appreciation completely overwhelmed by isolated objection (much of which is actually about changes in policy, such as having to justify non-preferred hotels, rather than the system itself).

marlon-brando-antonyWhat to do? For the most part we can’t use Mark Antony’s technique: through sarcasm (“Brutus is an honourable man“–imagine the air quotes), he discredits assertions of Caesar’s evil. However, it’s unwise for us to treat our customers’ complaints sarcastically.

Rather, a principal strategy for those of us in domains where dissatisfaction automatically overwhelms satisfaction must be to minimize the former. For example, I wrote,

One way to gain unproductive visibility is by unnecessarily constraining choice. To avoid this, wherever possible use carrots rather than sticks to encourage standardization, so that homogeneity is the product of aggregated free choice rather than central mandate… Try to keep institutional options open. Avoid strategies, vendors, architectures, and technologies that constrain choice. Seek interoperability. Wherever possible, have spillover vendors… Think carefully ahead about likely small disasters, many of which are caused by backhoes doing minor excavation, contractors oblivious to wiring closets, incompetent hacking, vandalism, or broken pipes.

But although minimizing unproductive visibility is important, it’s not enough. Mark Antony didn’t rely entirely on discrediting Brutus; he also cited Caesar’s good:

He was my friend, faithful and just to me… He hath brought many captives home to Rome, whose ransoms did the general coffers fill… When that the poor have cried, Caesar hath wept…

Mark Antony understood that discrediting Brutus and extolling Caesar aren’t the same thing. But it was necessary for him to do the former in order to succeed at the latter.

So let it be with IT. We need to recognize more explicitly that maximizing the good things we in IT do to satisfy our customers and campuses (or other organizations) is important, but those good things are different from and do not counterbalance the unproductively visible ways we dissatisfy them.

Notes From (or is it To?) the Dark Side

“Why are you at NBC?,” people ask. “What are you doing over there?,” too, and “Is it different on the dark side?” A year into the gig seems a good time to think about those. Especially that “dark side” metaphor.  For example, which side is “dark”?

This is a longer-than-usual post. I’ll take up the questions in order: first Why, then What, then Different; use the links to skip ahead if you prefer.

Why are you at NBC?

5675955This is the first time I’ve worked at a for-profit company since, let’s see, the summer of 1967: an MIT alumnus arranged an undergraduate summer job at Honeywell‘s Mexico City facility. Part of that summer I learned a great deal about the configuration and construction of custom control panels, especially for big production lines. I think of this every time I see photos of big control panels, such as those at older nuclear plants—I recognize the switch types, those square toggle buttons that light up. (Another part of the summer, after the guy who hired me left and no one could figure out what I should do, I made a 43½-foot paper-clip chain.)

One nice Honeywell perk was an employee discount on a Pentax 35mm SLR with a 40mm and 135mm lenses, which I still have in a box somewhere, and which still works when I replace the camera’s light-meter battery. (The Pentax brand belonged to Honeywell back then, not Ricoh.) Excellent camera, served me well for years, through two darkrooms and a lot of Tri-X film. I haven’t used it since I began taking digital photos, though.

5499942818_d3d9e9929b_nI digress. Except, it strikes me, not really. One interesting thing about digital photos, especially if you store them online and make most of them publicly visible (like this one, taken on the rim of spectacular Bryce Canyon, from my Backdrops collection), is that sometimes the people who find your pictures download them and use them for their own purposes. My photos carry a Creative Commons license specifying that although they are my intellectual property, they can be used for nonprofit purposes so long as they are attributed to me (an option not available, apparently, if I post them on Facebook instead).

So long as those who use my photos comply with the CC license requirement, I don’t require that they tell me, although now and then they do. But if people want to use one of my photos commercially, they’re supposed to ask my permission, and I can ask for a use fee. No one has done that for me—I’m keeping the day job—but it’s happened for our son.

dmcaI hadn’t thought much about copyright, permissions, and licensing for personal photos (as opposed to archival, commercial, or institutional ones) back when I first began dealing with “takedown notices” sent to the University of Chicago under the Digital Millennium Copyright Act (DMCA). There didn’t seem to be much of a parallel between commercialized intellectual property, like the music tracks that accounted for most early DMCA notices, and my photos, which I was putting online mostly because it was fun to share them.

Neither did I think about either photos or music while serving on a faculty committee rewriting the University’s Statute 18, the provision governing patents in the University’s founding documents.

sealThe issues for the committee were fundamentally two, both driven somewhat by the evolution of “textbooks”.

First, where is the line between faculty inventions, which belong to the University (or did at the time), and creations, which belong to creators—between patentable inventions and copyrightable creations, in other words? This was an issue because textbooks had always been treated as creations, but many textbooks had come to include software (back then, CDs tucked into the back cover), and software had always been treated as an invention.

Second, who owns intellectual property that grows out of the instructional process? Traditionally, the rights and revenues associated with textbooks, even textbooks based on University classes, belonged entirely to faculty members. But some faculty members were extrapolating this tradition to cover other class-based material, such as videos of lectures. They were personally selling those materials and the associated rights to outside entities, some of which were in effect competitors (in some cases, they were other universities!).

fathomAs you can see by reading the current Statute 18, the faculty committee really didn’t resolve any of this. Gradually, though, it came to be understood  that textbooks, even textbooks including software, were still faculty intellectual property, whereas instructional material other than that explicitly included in traditional textbooks was the University’s to exploit, sell, or license.

With the latter well established, the University joined Fathom, one of the early efforts to commercialize online instructional material, and put together some excellent online materials. Unfortunately, Fathom, like its first-generation peers, failed to generate revenues exceeding its costs. Once it blew through its venture capital, which had mostly come from Columbia University, Fathom folded. (Poetic justice: so did one of the profit-making institutions whose use of University teaching materials prompted the Statute 18 review.)

Gradually this all got me interested in the thicket of issues surrounding campus online distribution and use of copyrighted materials and other intellectual property, and especially the messy question how campuses should think about copyright infringement occurring within and distributed from their networks. The DMCA had established the dual principles that (a) network operators, including campuses, could be held liable for infringement by their network users, but (b) they could escape this liability (find “safe harbor”) by responding appropriately to complaints from copyright holders. Several of us research-university CIOs worked together to develop efficient mechanisms for handling and responding to DMCA notices, and to help the industry understand those and the limits on what they might expect campuses to do.

heoaAs one byproduct of that, I found myself testifying before a Congressional committee. As another, I found myself negotiating with the entertainment industry, under US Education Department auspices, to develop regulations implementing the so-called “peer to peer” provisions of the Higher Education Opportunity Act of 2008.

That was one of several threads that led to my joining EDUCAUSE in 2009. One of several initiatives in the Policy group was to build better, more open communications between higher education and the entertainment industry with regard to copyright infringement, DMCA, and the HEOA requirements.

hero-logo-edxI didn’t think at the time about how this might interact with EDUCAUSE’s then-parallel efforts to illuminate policy issues around online and nontraditional education, but there are important relevancies. Through massively open online courses (MOOCs) and other mechanisms, colleges and universities are using the Internet to reach distant students, first to build awareness (in which case it’s okay for what they provide to be freely available) but eventually to find new revenues, that is, to monetize their intellectual property (in which case it isn’t).

music-industryIf online campus content is to be sold rather than given away, then campuses face the same issues as the entertainment industry: They must protect their content from those who would use it without permission, and take appropriate action to deter or address infringement.

Campuses are generally happy to make their research freely available (except perhaps for inventions), as UChicago’s Statute 18 makes clear, provided that researchers are properly credited. (I also served on UChicago’s faculty Intellectual Property Committee, which among other things adjudicated who-gets-credit conflicts among faculty and other researchers.) But instruction is another matter altogether. If campuses don’t take this seriously, I’m afraid, then as goes music, so goes online higher education.

Much as campus tumult and changes in the late Sixties led me to abandon engineering for policy analysis, and quantitative policy analysis led me into large-scale data analysis, and large-scale data analysis led me into IT, and IT led me back into policy analysis, intellectual-property issues led me to NBCUniversal.

Peacock_CleanupI’d liked the people I met during the HEOA negotiations, and the company seemed seriously committed to rethinking its relationships with higher education. I thought it would be interesting, at this stage in my career, to do something very different in a different kind of place. Plus, less travel (see screwup #3 in my 2007 EDUCAUSE award address).

So here I am, with an office amidst lobbyists and others who focus on legislation and regulation, with a Peacock ID card that gets me into the Universal lot, WRC-TV, and 30 Rock (but not SNL), and with a 401k instead of a 403b.

What are you doing over there?

NBCUniversal’s goals for higher education are relatively simple. First, it would like students to use legitimate sources to get online content more, and illegitimate “pirate” sources less. Second, it would like campuses to reduce the volume of infringing material made available from their networks to illegal downloaders worldwide.

477px-CopyrightpiratesMy roles are also two. First, there’s eagerness among my colleagues (and their counterparts in other studios) to better understand higher education, and how campuses might think about issues and initiatives. Second, the company clearly wants to change its approach to higher education, but doesn’t know what approaches might make sense. Apparently I can help with both.

To lay foundation for specific projects—five so far, which I’ll describe briefly below—I looked at data from DMCA takedown notices.

Curiously, it turned out, no one had done much to analyze detected infringement from campus networks (as measured by DMCA notices sent to them), or to delve into the ethical puzzle: Why do students behave one way with regard to misappropriating music, movies, and TV shows, and very different ways with regard to arguably similar options such as shoplifting or plagiarism? I’ve written about some of the underlying policy issues in Story of S, but here I decided to focus first on detected infringement.

riaa-logoIt turns out that virtually all takedown notices for music are sent by the Recording Industry Association of America, RIAA (the Zappa Trust and various other entities send some, but they’re a drop in the bucket).

MPAAMost takedown notices for movies and some for TV are sent by the Motion Picture Association of America, MPAA, on behalf of major studios (again, with some smaller entities such as Lucasfilm wading in separately). NBCUniversal and Fox send out notices involving their movies and TV shows.

sources chartI’ve now analyzed data from the major senders for both a twelve-month period (Nov 2011-Oct 2012) and a more recent two-month period (Feb-Mar 2013). For the more recent period, I obtained very detailed data on each of 40,000 or so notices sent to campuses. Here are some observations from the data:

  • Almost all the notices went to 4-year campuses that have at least 100 dormitory beds (according to IPEDS). To a modest extent, the bigger the campus the more notices, but the correlation isn’t especially large.
  • Over half of all campuses—even of campuses with dorms—didn’t get any notices. To some extent this is because there are lots and lots of very small campuses, and they fly under the infringement-detection radar. But I’ve learned from talking to a fair number of campuses that, much to my surprise, many heavily filter or even block peer-to-peer traffic at their commodity Internet border firewall—usually because the commodity bandwidth p2p uses is expensive, especially for movies, rather than to deal with infringement per se. Outsourced dorm networks also have an effect, but I don’t think they’re sufficiently widespread yet to explain the data.
  • Several campuses have out-of-date or incorrect “DMCA agent” addresses registered at the Library of Congress. Compounding that, it turns out some notice senders use “abuse” or other standard DNS addresses rather than the registered agent addresses.
  • Among campuses that received notices, a few campuses stand out for receiving the lion’s share, even adjusting for their enrollment. For example, the top 100 or so recipient campuses got about three quarters of the total, and a handful of campuses stand out sharply even within that group: the top three campuses (the leftmost blue bars in the graph below) accounted for well over 10% of the notices. (I found the same skewness in the 2012 study.) With a few interesting exceptions (interesting because I know or suspect what changed), the high-notice groups have been the same for the two periods.

utorrent-facebook-mark-850-transparentThe detection process, in general, is that copyright holders choose a list of music, movie, or TV titles they believe likely to be infringed. Their contractors then use BitTorrent tracker sites and other user tools to find illicit sources for those titles.

For the most part the studios and associations simply look for titles that are currently popular in theaters or from legitimate sources. It’s hard to see that process introducing a bias that would affect some campuses so much differently than others. I’ve also spent considerable time looking at how a couple of contractors verify that titles being offered illicitly (that is, listed for download on a BitTorrent tracker site such as The Pirate Bay) are actually the titles being supplied (rather than, say, malware, advertising, or porn), and at how they figure out where to send the resulting takedown notices. That process too seems pretty straightforward and unbiased.

argo-15355-1920x1200Sender choices clearly can influence how notice counts vary from time to time: for example, adding a newly popular title to the search list can lead to a jump in detections and hence notices. But it’s hard to see how the choice of titles would influence how notice counts vary from institution to institution.

This all leads me to believe that takedown notices tell us something incomplete but useful about campus policies and practices, especially at the extremes. The analysis led directly to two projects focused on specific groups of campuses, and indirectly to three others.

Role Model Campuses

Based on the results of the data analysis, I communicated individually with CIOs at 22 campuses that received some but relatively few notices: specifically, campuses that (a) received at least one notice (and so are on the radar) but (b) fewer than 300 and fewer than 20 per thousand student headcount, (c) have at least 7,500 headcount students, and (d) have at least 10,000 dorm beds (per IPEDS) or sufficient dorm beds to house half your headcount. (These are Group 4, the purple bars in the graph below. The solid bars represent total notices sent, and the hollow bars represent incidence, or notices per thousand headcount students. Click on the graph to see it larger.)

I’ve asked each of those campuses whether they’d be willing to document their practices in an open “role models” database developed jointly by the campuses and hosted by a third party such as groups charta higher-education association (as EDUCAUSE did after the HEOA regulations took effect). The idea is to make a collection of diverse effective practices available to other campuses that might want to enhance their practices.

High Volume Campuses

Separately, I communicated privately with CIOs at 13 campuses that received exceptionally many notices, even adjusting for their enrollment (Group 1, the blue bars in the graph). I’ve looked in some detail at the data for those campuses, some large and some small, and in some cases that’s led to suggestions.

For example, in a few cases I discovered that virtually all of a high-volume campus’s notices were split evenly among a small number of consecutive IP addresses. In those cases, I’ve suggested that those IP addresses might be the front-end to something like a campus wireless network. Filtering or blocking p2p (or just BitTorrent) traffic on those few IP addresses (or the associated network devices) might well shrink the campus’s role as a distributor without affecting legitimate p2p or BitTorrent users (who tend to be managing servers with static addresses).

Symposia

Back when I was at EDUCAUSE, we worked with NBCUniversal to host a DC meeting between senior campus staff from a score of campuses nationwide and some industry staff closely involved with the detection and notification for online infringement. The meeting was energetic and frank, and participants from both sides went away with a better sense of the other’s bona fides and seriousness. This was the first time campus staff had gotten a close look at the takedown-notice process since a Common Solutions Group meeting in Ann Arbor some years earlier; back then the industry’s practices were much less refined.

university-st-thomas-logo-white croppedBased on the NBCUniversal/EDUCAUSE experience, we’re organizing a series of regional “Symposia” along these lines on campuses in various cities across the US. The objectives are to open new lines of communication and to build trust. The invitees are IT and student-affairs staff from local campuses, plus several representatives from industry, especially the groups that actually search for infringement on the Internet. The first was in New York, the second in Minneapolis, the third will be in Philadelphia, and others will follow in the West, the South, and elsewhere in the Midwest.

Research

We’re funding a study within a major state university system to gather two kinds of data. Initially the researchers are asking each campus to describe the measures it takes to “effectively combat” copyright infringement: its communications with students, its policies for dealing with violations, and the technologies it uses. The data from the first phase will help enhance a matrix we’ve drafted outlining the different approaches taken by different campuses, complementing what will emerge from the “role models” project.

Based on the initial data, the researchers and NBCUniversal will choose two campuses to participate in the pilot phase of the Campus Online Education Initiative (which I’ll describe next). In advance of that pilot, the researchers will gather data from a sample of students on each campus, asking about their attitudes toward and use of illicit and legitimate online sources for music, movies, and video. They’ll then repeat that data collection after the pilot term.

Campus Online Entertainment Initiative

Last but least in neither ambition nor complexity, we’re crafting a program that will attempt to address both goals I listed earlier: encouraging campuses to take effective steps to reduce distribution of infringing material from their networks, and helping students to appreciate (and eventually prefer) legitimate sources for online entertainment.

maxresdefaultWorking with Universal Studios and some of its peers, we’ll encourage students on participating campuses to use legitimate sources by making a wealth of material available coherently and attractively—through a single source that works across diverse devices, and at a substantial discount or with similar incentives.

Participating campuses, in turn, will maintain or implement policies and practices likely to shrink the volume of infringing material available from their networks. In some cases the participating campuses will already be like those in the “role models” group; in others they’ll be “high volume” or other campuses willing to  adopt more effective practices.

I’m managing these projects from NBCUniversal’s Washington offices, but with substantial collaboration from company colleagues here, in Los Angeles, and in New York; from Comcast colleagues in Philadelphia; and from people in other companies. Interestingly, and to my surprise, pulling this all together has been much like managing projects at a research university. That’s a good segue to the next question.

Is it different on the dark side?

IMG_1224Newly hired, I go out to WRC, the local NBC affiliate in Washington, to get my NBCUniversal ID and to go through HR orientation. Initially it’s all familiar: the same ID photo technology, the same RFID keycard, the same ugly tile and paint on the hallways, the same tax forms to be completed by hand.

But wait: Employee Relations is next door to the (now defunct) Chris Matthews Show. And the benefits part of orientation is a video hosted by Jimmy Fallon and Brian Williams. And there’s the possibility of something called a “bonus”, whatever that is.

Around my new office, in a spiffy modern building at 300 New Jersey Avenue, everyone seems to have two screens. That’s just as it was in higher-education IT. But wait: here one of them is a TV. People watch TV all day as they work.

Toto, we’re not in higher education any more.

IMG_1274It’s different over here, and not just because there’s a beautiful view of the Capitol from our conference rooms. Certain organizational functions seem to work better, perhaps because they should and in the corporate environment can be implemented by decree: HR processes, a good unified travel arrangement and expense system, catering, office management. Others don’t: there’s something slightly out of date about the office IT, especially the central/individual balance and security, and there’s an awful lot of paper.

Some things are just different, rather than better or not: the culture is heavily oriented to face-to-face and telephone interaction, even though it’s a widely distributed organization where most people are at their desks most of the time. There’s remarkably little email, and surprisingly little use of workstation-based videoconferencing. People dress a bit differently (a maitre d’ told me, “that’s not a Washington tie”).

But differences notwithstanding, mostly things feel much the same as they did at EDUCAUSE, UChicago, and MIT.

tiny NBCUniversal_violet_1030Where I work is generally happy, people talk to one another, gossip a bit, have pizza on Thursdays, complain about the quality of coffee, and are in and out a lot. It’s not an operational group, and so there’s not the bustle that comes with that, but it’s definitely busy (especially with everyone around me working on the Comcast/Time Warner merger). The place is teamly, in that people work with one another based on what’s right substantively, and rarely appeal to authority to reach decisions. Who trusts whom seems at least as important as who outranks whom, or whose boss is more powerful. Conversely, it’s often hard to figure out exactly how to get something done, and lots of effort goes into following interpersonal networks. That’s all very familiar.

MIT_Building_10_and_the_Great_Dome,_Cambridge_MAI’d never realized how much like a research university a modern corporation can be. Where I work is NBCUniversal, which is the overarching corporate umbrella (“Old Main”, “Mass Hall”, “Building 10”, “California Hall”, “Boulder”) for 18 other companies including news, entertainment, Universal Studios, theme parks, the Golf Channel, Telemundo (which are remarkably like schools and departments in their varied autonomy).

Meanwhile NBCUniversal is owned by Comcast—think “System Central Office”. Sure, these are all corporate entities, and they have concrete metrics by which to measure success: revenue, profit, subscribers, viewership, market share. But the relationships among organizations, activities, and outcomes aren’t as coherent and unitary as I’d expected.

Dark or Green?

So, am I on the dark side, or have I left it behind for greener pastures? Curiously, I hear both from my friends and colleagues in higher education: Some of them think my move is interesting and logical, some think it odd and disappointing. Curioser still, I hear both from my new colleagues in the industry: Some think I was lucky to have worked all those decades in higher education, while others think I’m lucky to have escaped. None of those views seems quite right, and none seems quite wrong.

The point, I suppose, is that simple judgments like “dark” and “greener” underrepresent the complexity of organizational and individual value, effectiveness, and life. Broad-brush characterizations, especially characterizations embodying the ecological fallacy, “…the impulse to apply group or societal level characteristics onto individuals within that group,” do none of us any good.

It’s so easy to fall into the ecological-fallacy trap; so important, if we’re to make collective progress, not to.

Comments or questions? Write me: greg@gjackson.us

(The quote is from Charles Ess & Fay Sudweeks, Culture, technology, communication: towards an intercultural global village, SUNY Press 2001, p 90. Everything in this post, and for that matter all my posts, represents my own views, not those of my current or past employers, or of anyone else.)

3|5|2014 11:44a est

Perceived Truths as Policy Paradoxes

imagesThe quote I was going to use to introduce this topic — “You’re entitled to your own opinion, but not to your own facts” — itself illustrates my theme for today: that truths are often less than well founded, and so can turn policy discussions weird.

I’d always heard the quote attributed to Pat Moynihan, an influential sociologist who co-wrote Beyond the Melting Pot with Nathan Glazer, directed the MIT-Harvard Joint Center for Urban Studies shortly before I worked there (and left behind a closet full of Scotch, which stemmed from his perhaps apocryphal rule that no meeting extend beyond 4pm without a bottle on the table), and later served as a widely respected Senator from New York. The collective viziers of Wikipedia have found other attributions for the quote, however. (This has me once again looking for the source of “There go my people, I must go join them, for I am their leader,” supposedly Mahatma Gandhi but apparently some French general — but I digress.). The quote will need to stand on its own.

a0157b7d-9976-410d-bba8-6ccf1dbf4c48-The-ACT-Here’s the Scott Jaschik item from Inside Higher Education that triggered today’s Rumination:

A new survey from ACT shows the continued gap between those who teach in high school and those who teach in college when it comes to their perceptions of the college preparation of today’s students. Nearly 90 percent of high school teachers told ACT that their students are either “well” or “very well” prepared for college-level work in their subject area after leaving their courses. But only 26 percent of college instructors reported that their incoming students are either “well” or “very well” prepared for first-year credit-bearing courses in their subject area. The percentages are virtually unchanged from a similar survey in 2009.

This is precisely what Moynihan (or whoever) had in mind: two parties to an important discussion each bearing their own data, and therefore unable to agree on the problem or how to address it. The teachers presumably think the professors have unreasonable expectations, or don’t work very hard to bring their students along; the professors presumably think the teachers aren’t doing their job. Each side therefore believes the problem lies on the other, and has data to prove that. Collaboration is unlikely, progress ditto. This is what Moynihan had observed about the federal social policy process.

5-financial-aid-tips-1The ACT survey reminded me of a similar finding that emerged back when I was doing college-choice research. I can’t locate a citation, but I recall hearing about a study that surveyed students who had been admitted to several different colleges.

The clever wrinkle in the study was that the students received several different survey queries, each purporting to be from one of the colleges to which he or she had been admitted, and each asking the student about the reasons for accepting or declining the admission offer. Here’s what they found: students told the institution they’d accepted that the reason was excellent academic quality, but they told the institutions they’d declined that the reason was better financial aid from the one they’d accepted.

131More recently, I was talking to a colleague in a another media company who was concerned about the volume of copyright infringement on a local campus. According to the company, the campus was hosting a great deal of copyright infringementl, as measured by the volume of requests for infringing material being sent out by BitTorrent. But according to the campus, a scan of the campus network identified very few hosts running the peer-to-peer applications. The colleague thought the campus was blowing smoke, the campus thought the company’s statistics were wrong.

Although these three examples seem similar — parties disagreeing about facts — in fact they’re a bit different.

  • In the teacher/professor example, the different conclusions presumably stem from different (and unshared) definitions of “”prepared for college-level work”.
  • In the accepted/decline example, the different explanations possibly stem from students’ not wanting to offend the declined institution by questioning its quality, or wanting think of their actual choice as good rather than cheap.
  • In the infringement/application case, the different explanations stem from divergent metrics.

compass-badgeWe’ve seen similar issues arise around institutional attributes in higher education. Do ratings like those from US News & World Report gather their own data, for example, or rely on presumably neutral sources such as the National Center for Educational Statistics? This is critical where results have major reputational effects — consider George Washington University’s inflation of class-rank admissions data, and similar earlier issues with Claremont McKenna, Emory, Villanova, and others.

I’d been thinking about this because in my current job it’s quite important to understand patterns of copyright infringement on campuses. It would be good to figure out which campuses seem to have relatively low infringement rates, and to explore and document their policies and practices lest other campuses might benefit. For somewhat different reasons, it would be good to figure out which campuses seem to have relatively high infringement rates, so that they could be encouraged adopt different policies and practices.

But here we run into the accept/decline problem. If the point to data collection is to identify and celebrate effective practice, there are lots of incentives for campuses to participate. But if the point is to identify and pressure less effective campuses, the incentives are otherwise.

Compounding the problem, there are different ways to measure the problem:

  • One can rely on externally generated complaints, whose volume can vary for reasons having nothing to do with the volume of infringement,
  • one can rely on internal assessments of network traffic, which can be inadvertently selective, and/or
  • one can rely on external measures such as the volume of queries to known sources of infringement;

I’m sure there are others — and that’s without getting into the religious wars about copyright, middlemen, and so forth I addressed in an earlier post).

There’s no full solution to this problem. But there are two things that help: collaboration and openness.

  • By “collaboration,” I mean that parties to questions of policy or practice should work together to define and ideally collect data; that way, arguments can focus on substance.
  • By “openness,” I mean that wherever possible raw data, perhaps anonymized, should accompany analysis and advocacy based on those data.

As an example what this means, here are some thoughts for one of my upcoming challenges — figuring out how to identify campuses that might be models for others to follow, and also campuses that should probably follow them. Achieving this is important, but improperly done it can easily come to resemble the “top 25″ lists from RIAA and MPAA that became so controversial and counterproductive a few years ago. The “top 25″ lists became controversial partly because their methodology was suspect, partly because the underlying data were never available, and partly because they ignored the other end of the continuum, that is, institutions that had somehow managed to elicit very few Digital Millennium Copyright Act (DMCA) notices.

PirateBay_1_NETT_26916dIt’s clear there are various sources of data, even without internal access to campus network data:

  • counts of DMCA notices sent by various copyright holders (some of which send notices methodically, following reasonably robust and consistent procedures, and some of which don’t),
  • counts of queries involving major infringing sites, and/or
  • network volume measures for major infringing protocols.

Those last two yield voluminous data, and so usually require sampling or data reduction of some kind. And not all queries or protocols they follow involve infringement. It’s also clear, from earlier studies, that there’s substantial variation in these counts over time and even across similar campuses.

This means it will be important for my database, if I can create one, to include several different measures, especially counts from different sources for different materials, and to do that over a reasonable period of time. Integrating all this into a single dataset will require lots of collaboration among the providers. Moreover, the raw data necessarily will identify individual institutions, and releasing them that way would probably cause more opposition than support. Clumping them all together would bypass that problem, but also cover up important variation. So it makes much more sense to disguise rather than clump — that is, to identify institutions by a code name and enough attributes to describe them but not to identify them.

It’ll then be important to be transparent: to lay out the detailed methodology used to “rank” campuses (as, for example, US News now does), and to share the disguised data so others can try different methodologies.

big_dataAt a more general level, what I draw from the various examples is this: If organizations are to set policy and frame practice based on data — to become “data-driven organizations,” in the current parlance — then they must put serious effort into the source, quality, and accessibility of data. That’s especially true for “big data,” even though many current “big data” advocates wrongly believe that volume somehow compensates for quality.

If we’re going to have productive debates about policy and practice in connection with copyright infringment or anything else, we need to listen to Moynihan: To have our own opinions, but to share our data.

Story of S, and the Mythology of the Lost Generation

argo_ver7_xlgDinner talk turned from Argo and Zero Dark Thirty to movies more generally. A 21-year-old college senior—I’ll call her “S”—recognized most of the films we were discussing. She had seen several, but others she hadn’t, which was a bit surprising, since S was an arts major, wanted to be a screenwriter, and was enthusiastic about her first choice for graduate school: the screenwriting program at a major California institution focused on the movie industry.

S had older brothers in the movie business, and she already had begun writing. What she needed, S said, was broader and deeper exposure to what made good screenplays. Graduate school would provide “deeper.” Her plan for “broader” was to watch as many well-regarded classics as possible, and apparently we were helping her map out that strategy.

But many of the films she wanted to see weren’t available on cable in her dormitory, even as pay-per-view. “Buying” or “renting” them online she found too expensive and awkward, especially given the number of films she wanted to see. So S was doing what unfortunately many students (and others) do: looking for movies on the Internet, and then streaming or downloading the least expensive version she could find. Since S’s college dormitory provided good Internet connectivity, S used that to download or stream her movies. Bluebeard_PirateUsually, she said, the least expensive version was an unauthorized copy, a so-called “pirate” version.

Some of us challenged her: Didn’t S realize that downloading or streaming “pirated” copies was against the law? Was she not concerned about the possible consequences? As a budding screenwriter, would she want others to do as she was doing, and deprive her of royalties? Didn’t it just seem wrong to take something without the owner’s permission?

S listened carefully—she was pretty sharp—but she didn’t seem convinced. Indeed, she seemed to feel that her choice to use unauthorized copies was reasonable, given the limited and unsatisfactory alternatives provided by the movie industry.

cary-shermanIn so believing, S was echoing the persistent mythology of the lost generation. I first heard Cary Sherman, the President of the Recording Industry Association of America (RIAA), use “the lost generation” to describe the approximately 25 million students who became digital consumers between two milestones: Napster‘s debut in 1999, which made sharing of MP3s ripped from CDs easy, and Apple’s discontinuing digital rights management (DRM) for most iTunes music in 2009, which made buying tracks legally almost as easy and convenient.

Even without the illusion that infringing materials were “free,” there were ample incentives to infringe during that period: illegal mechanisms were comprehensive and easy to use, for the most part, whereas legal mechanisms did not exist, were inflexible and awkward, and/or did not include many widely-desired items.

Age_of_Mythology_LinerBecause of this, many members of the lost generation adopted a mythology comprising some subset of

  • digital materials are priced too high, since it costs money to manufacture CDs and DVDs but the Internet is free,
  • profits flow to middlemen rather than artists, and so artists aren’t hurt by infringement,
  • DRM is just the industry’s mechanism for controlling users and rationing information,
  • people who stream or download unauthorized copies wouldn’t have bought legal copies anyway, and so copyright holders don’t lose any revenue because of unauthorized copying,
  • there’s no way to sample material before buying it, and so unauthorized sources are the only easy way to explore new or arcane stuff,
  • the entertainment  industry has no interest in serving customers, as evidenced by its keeping so much material unavailable,
  • copyright is wrong, since information should be free and users should just pay what they think it’s worth, and
  • (the illegitimate moral leap S and others make) therefore it’s “okay” to copy and share digital materials without permission.

Unfortunately, the lost generation’s beliefs, most of which have always been exaggerated or invalid, have been passed down to successor generations, a process accelerated rather than slowed by the current industry emphasis on monitoring and penalizing network users.

cool-hand-luke-martinWhy does the mythology persist?

There are the obvious technical and financial arguments: if illegal technology is more convenient that legal, and illegal content costs less than legal, then it’s not surprising that illegal stuff remains prominent.

But in addition, as the Captain might observe, what we have here is failure to communicate:

  • There’s lots of evidence that convenient, comprehensive services like Netflix, Amazon Prime Instant Video, Hulu, Pandora, and Spotify draw users to them even when there are illegal “free” alternatives. But for this to happen, users must know about those services. S clearly didn’t—we asked her specifically—and that’s a marketing failure.
  • Shoplifting and plagiarism are relatively rare, at least among individuals like S. Yet they have the same appealing features as “pirate” music and video. Somehow S and her peers have come to understand that shoplifting, plagiarism, and various similar choices are unethical, immoral, or socially counterproductive. Yet they don’t put copyright infringement in the same category. That’s a social, educational, and parental failure.
  • LSb_120504_345.jpgFor all kinds of arguably irremediable licensing, contractual, competitive, and anti-trust reasons, it remains stubbornly difficult to “give the lady what she wants“: in S’s case, a comprehensive, reasonably priced, convenient service from which she could obtain all the movies she wanted. Whether this is customers not conveying their wants to providers (in part because they can bypass the latter), or whether this is providers stuck on obsolete delivery models, it’s a business failure.
  • Colleges and universities are supposed at least to tell their students about copyright infringement, and to implement technologies and other mechanisms to “effectively combat” it. S had no idea that the consequences of being caught downloading or streaming unauthorized copies were anything beyond being told to stop. So far as she knew, no one, at least no one at her college, had ever gotten in trouble for that. And she’d never heard anything from her college—which was also her Internet service provider—about the issue. That’s a policy failure.

To be fair, S’s dinner comments endorsed only a small subset of the lost generation’s tenets, she seemed generally interested in the streaming services we told her about, and she was now thinking about the consequences of being caught downloading or streaming unauthorized copies—and about how lots of people doing that might affect her future earnings. So there was progress.

But ganging up on 21-year-olds at dinner parties is a very inefficient way to counteract the mythology of the lost generation. We—and by this I mean everyone: users, parents, schools, artists, producers, network providers—need  to find much better ways to communicate about copyright infringement, to help potential infringers understand the choices they are making, and to provide and use better legal services.

Especially until we do that last, this will be hard, and progress will be slow. But it’s progress we need if the intellectual-property economy is to endure.

Three Fallacies: Optimal Diet, Best Practices, and Key Indicators

ntn(126788, 10)Just before writing this (and then losing most of it to a Chrome freeze, and then rewriting it), I had a sort-of-Ploughman’s lunch: a couple of Wasa Wholegrain crackers spread with about 1 ounce of nice smelly Buttermilk Blue cheese, and a Pink Lady apple, and a glass of water.

For yesterday’s lunch I mixed some canned white tuna with with nonfat Greek yogurt and mayo, and put it on Wasa crackers.

Which lunch was better? How might I measure that?

Here are some data from Peapod prices (even though I didn’t buy these ingredients there): prices per serving and nutrition info from the standard labels:

servings cost/serving fat (g) calories fiber (g) protein (g)
cheese 1 $1.16 8.0 100 1.0 6.0
crackers 2 $0.18 0.0 40 2.0 1.0
apple 1 $0.75 0.3 95 4.4 0.5
Today   $2.26 8.3 275 9.4 8.5
tuna 1 $0.95 1.0 70 0.0 14.0
crackers 2 $0.18 0.0 40 2.0 1.0
yogurt 1 $0.28 0.0 15 0.0 2.6
mayo 1 $0.18 10.0 90 0.0 0.0
Yesterday   $1.76 11.0 255 4.0 18.6

 

Today’s lunch cost $2.26, which is about 1/3 more than yesterday’s. Were I focused on cost, therefore, I couldn’t rate today’s lunch as highly as yesterday’s. I have excellent, robust cost indicators to make this judgment, and so it’s pretty clear how to assess practice if minimizing lunch cost is my goal.

ntn(191171, 10)Then again, I’m of the age where I need to be careful what I eat (note “need to be”, not “am”), and so maybe minimizing cost isn’t the right goal. Instead, perhaps I should look at nutritional indicators. Today’s lunch had 8.3 grams of fat, mostly from the cheese, and 275 calories. Yesterday’s was similar in those respects, with 11 grams of fat, mostly from the mayo, and 255 calories. So fat and calories don’t give me a clear indication which lunch is better.

I’m told that fiber is good, though. Today’s lunch is better than yesterday’s fiber-wise: 9.4 versus 4 grams. Then again, protein is also good, and here the indicator tilts the other way: yesterday’s 18.6 grams of protein trumps today’s 8.5 grams.

I’ve got measures of my two lunches’ nutritional attributes — not as robust as my cost measures (see, for example, this recent Fox News story), but still pretty good. However, unlike my single measure of cost, I have multiple measures of nutrition, and they’re divergent: even the few measures on the standard label value the lunches differently. I may choose different indicators than someone else — and I may choose differently some other day  if, say, my cholesterol levels change.

imgresTrying to incorporate both nutritional goals (as standards to be met) and cost (as an outcome to be minimized) into an optimal diet, George Stigler, in a 1945 article, tried to determine the least expensive nutritionally adequate diet for a 70-kg male economist — that is, himself. Using 1944 products, prices, and then-current Recommended Daily Allowances (which included calories, protein, calcium, iron, and five vitamins), and after laborious analysis, he proposed an optimal daily diet comprising about 23 ounces of wheat flour (!), 5 ounces of cabbage, 1 ounce of spinach, 6 ounces of pancake flour, and 1 ounce of pork liver. Stigler estimated this diet would cost 16¢/day, which would be $2.08 in 2011 dollars. (Stigler never tried this diet, and neither did his son, who was a faculty member at UChicago during my tenure there.)

Seven years later, George Dantzig developed the Simplex algorithm for solving linear-programming problems like this. Today there are simple online or sophisticated spreadsheet tools available to explore the now-famous Diet Problem — for example, Stefan Warner’s  web tool, or a more comprehensive Excel-based one developed by Samir Khan.

Playing around with the various tools, one thing becomes clear immediately: results vary dramatically depending on exactly how one bounds the problem. As we already know from my lunches, the “optimal” diet depends on what foods are considered, and on which nutritional requirements one chooses to impose on them.

imgresThus, for example, Warner’s default settings include 20 foods, use 2008 RDAs, allow no more than 2 servings of any one food, and impose requirements for 10 nutrients (minima for calories, fat, carbohydrates, protein, vitamin C, sodium, fiber, vitamin A, and calcium, and a maximum for cholesterol —  Warner  adds fat and fiber to Stigler’s 9 nutrients, and omits iron). These settings yield a daily diet comprising 1.6 servings of spaghetti with sauce plus 2 servings each of broccoli, potatoes, banana, wheat bread, lowfat milk, eggs, and white rice. According to the web tool, the default Warner diet costs $2.87/day, or $3.14 in 2011 dollars.

imgresUsing Khan’s Excel-based tool, its more limited list of foods, and its longer list of 14 nutrients (most of which have both a minimum and a maximum), a cost-optimized diet runs $10.71/day: 3.1 servings of lentils, 2.9 of bagels, 2.3 of roast chicken, 2.2 of Brussels sprouts, 2.1 of oatmeal, 1.7 of 1% milk, 1.2 of oranges, and 1 of broccoli.

No one who’s ever paid close attention to dietary recommendations is surprised. The best diet depends on what one has at hand, and what one means by “best” — what micro-economists would call one’s “utility function.” Given this, it’s hard to make sense of all-in-one, impersonal key indicators like the ANDI (Aggregate Nutrient Density Index) numbers prominently displayed at Whole Foods.

Back to lunch. Although yesterday’s lunch was good, I liked today’s much more.  It was satisfying and tasty. The crackers were firm and crisp, an excellent base for the strongly-flavored cheese. The apple tied it all together very nicely, adding sweetness and juiciness. In gastronomic terms, the lunch rated very highly.

But maybe that’s just because I was in the mood for cheese today. Not only is there no obvious robust measure of gastronomic appeal, but if there were, it might vary both from person to person and time to time.

So which lunch was better? It depends on what I choose to value.

As For Diet,
So For  IT “Best Practices” & “Key Performance Indicators”

imgresBest practices in IT depend on context and goals even more than diet does. Yet somehow we’ve come to believe there’s a one-size-fits-all optimum out there — documented by an ANDI for IT — and that if we can find it all will be well. We’ve spent large amounts of time and money on the quest for these “best practices,” often hiring consultants to “optimize” IT practice.

A recent cursory survey of research university CIOs, for example, found that at least 18 of them had undertaken optimization projects, most of them involving outside firms such as Bain, McKinsey, PriceWaterhouseCoopers, Accenture,  and their kith. (I’ve been through two of these: an early effort at MIT involving CSC Index, and a more recent one at UChicago involving McKinsey.)

Here’s what I think: too often our quest for best practices — especially when it’s a quest guided by outside entities — is based on goals that may not be what we want, or at least may not be all that we want. What Washington College wants IT to achieve may differ from what George Washington University wants IT to achieve, and probably neither has the same goals as the Chicago City Colleges or the University of Phoenix or the EdX consortium.

imgresConsider IT support, for example. Users seem to be most satisfied when they can choose their own technologies, and the institution provides a knowledgeable IT support person they trust — ideally down the hall, so that sticking one’s head out the door and saying “Pat, can you come help me please?” brings an expert Pat running.

Unfortunately, that “local” support model is expensive, both because it requires flexible IT support staff who are skilled with diverse technologies and because it inhibits economy of scale. Conversely, central administrators and their consultants focus on costs, and so tend to value support strategies that reduce cost: standardization, tiered support, and even consolidated outsourcing.

But cost-reduction strategies are almost precisely antithetical to user-satisfaction strategies. Neither is “right” in any objective sense; indeed, the point is that although it’s quite possible to develop each strategy and measure whether it’s working, whether that’s “right” depends on the original goal.

Measurement Isn’t the Same as Evaluation

The point of all this should be obvious: it’s pointless to talk about “best practices” or to use “key performance indicators” until those involved at least understand and appreciate each other’s goals — in lunch terms, they know who wants cheap, who wants nutritious, and who wants tasty, and maybe have negotiated some compromises. Don’t read that sentence as being anti-data: data are good, and the more of them and the higher their quality the better, but that one has data on some attribute doesn’t mean that attribute signifies value — or that the absence of data signifies the absence of value. Language is important: a “datum” is value-neutral, for example, whereas a “score” isn’t. “Index tilts toward “score”, “indicator” toward “datum”. “Practice” is neutral, “best practice” isn’t. And so on.

Also, “those involved,” in higher-education IT, typically entails an awkward triangle: IT organizations provide services to users, who seek maximum service levels, but IT organizations get resources from central administration, which seeks minimum expenditure. Goal divergence results, one of many reason there’s such frustration within higher-education IT these days. Without mutual understanding and agreement on goals, there’s no such thing as “best practice,” no matter how many “key performance indicators” are available.

urlThat I hate watermelon and love bacon can’t govern our family diet. The bacon is intrinsically controversial (tasty but fatty, so even without talking to anyone else I’m conflicted), whereas the watermelon admits compromise (my wife and son eat it, and I don’t).

All of this is making me hungry…

 

 

 

 

The Importance of Being Enterprise

…as Oscar Wilde well might have titled an essay about campus-wide IT, had there been such a thing back then.

Enterprise IT it accounts for the lion’s share of campus IT staffing, expenditure, and risk. Yet it receives curiously little attention in national discussion of IT’s strategic higher-education role. Perhaps that should change. Two questions arise:

  • What does “Enterprise” mean within higher-education IT?
  • Why might the importance of Enterprise IT evolve?

What does “Enterprise IT” mean?

Here are some higher-education spending data from the federal Integrated Postsecondary Education Data Service (IPEDS), omitting hospitals, auxiliaries, and the like:

Broadly speaking, colleges and universities deploy resources with goals and purposes that relate to their substantive mission or the underlying instrumental infrastructure and administration.

  • Substantive purposes and goals comprise some combination of education, research, and community service. These correspond to the bottom three categories in the IPEDS graph above. Few institutions focus predominantly on research—Rockefeller University, for example. Most research universities pursue all three missions, most community colleges emphasize the first and third, and most liberal-arts colleges focus on the first.
  • Instrumental activities are those that equip, organize, and administer colleges and universities for optimal progress toward their mission—the top two categories in the IPEDS graph. In some cases, core activities advance institutional mission by providing a common infrastructure for the latter. In other cases, they do it by providing campus-wide or departmental staffing, management, and processes to expedite mission-oriented work. In still other cases, they do it through collaboration with other institutions or by contracting for outside services.

Education, research, and community service all use IT substantively to some extent. This includes technologies that directly or indirectly serve teaching and learning, technologies that directly enable research, and technologies that provide information and services to outside communities—for examples of all three, classroom technologies, learning management systems, technologies tailored to specific research data collection or analysis, research data repositories, library systems, and so forth.

Instrumental functions rely much more heavily on IT. Administrative processes rely increasingly on IT-based automation, standardization, and outsourcing. Mission-oriented IT applications share core infrastructure, services, and support. Core IT includes infrastructure such as networks and data centers, storage and computational clouds, and desktop and mobile devices; administrative systems ranging from financial, HR, student-record, and other back office systems to learning-management and library systems; and communications, messaging, collaboration, and social-media systems.

In a sense, then, there are six technology domains within college and university IT:

  • the three substantive domains (education, research, and community service), and
  • the three instrumental domains (infrastructure, administration, and communications).

Especially in the instrumental domains, “IT” includes not only technology, but also the services, support, and staffing associated with it. Each domain therefore has technology, service, support, and strategic components.

Based on this, here is a working definition: in in higher education,

“Enterprise” IT comprises the IT-related infrastructure, applications, services, and staff
whose primary institutional role is instrumental rather than substantive.

Exploring Enterprise IT, framed thus, entails focusing on technology, services, and support as they relate to campus IT infrastructure, administrative systems, and communications mechanisms, plus their strategic, management, and policy contexts.

Why Might the Importance of Enterprise IT Evolve?

Three reasons: magnitude, change, and overlap.

Magnitude

According data from EDUCAUSE’s Core Data Service (CDS) and the federal Integrated Postsecondary Data System (IPEDS), the typical college or university spends just shy of 5% of its operating budget on IT. This varies a bit across institutional types:

We lack good data breaking down IT expenditures further. However, we do have CDS data on how IT staff distribute across different IT functions. Here is a summary graph, combining education and research into “academic” (community service accounts for very little dedicated IT effort):

Thus my assertion above that Enterprise IT accounts for the lion’s share of IT staffing. Even if we omit the “Management” component, Enterprise IT comprises 60-70% of staffing including IT support, almost half without. The distribution is even more skewed for expenditure, since hardware, applications, services, and maintenance are disproportionately greater in Administration and Infrastructure.

Why, given the magnitude of Enterprise relative to other college and university IT, has it not been more prominent in strategic discussion? There are at least two explanations:

  • relatively slow change in Enterprise IT, at least compared to other IT domains (rapidly-changing domains rightly receive more attention that stable ones), and
  • overlap—if not competition—between higher-education and vendor initiatives in the Enterprise space.

Change

Enterprise IT is changing thematically, driven by mobility, cloud, and other fundamental changes in information technology. It also is changing specifically, as concrete challenges arise.

Consider, as one way to approach the former, these five thematic metamorphoses:

  • In systems and applications, maintenance is giving way to renewal. At one time colleges and universities developed their own administrative systems, equipped their own data centers, and deployed their own networks. In-house development has given way to outside products and services installed and managed on campus, and more recently to the same products and services delivered in or from the cloud.
  • In procurement and deployment, direct administration and operations are giving way to negotiation with outside providers and oversight of the resulting services. Whereas once IT staff needed to have intricate knowledge of how systems worked, today that can be less useful that effective negotiation, monitoring, and mediation.
  • In data stewardship and archiving, segregated data and systems are giving way to integrated warehouses and tools. Historical data used to remain within administrative systems. The cost of keeping them “live” became too high, and so they moved to cheaper, less flexible, and even more compartmentalized media. The plunging price of storage and the emergence of sophisticated data warehouses and business-intelligence systems reversed this. Over time, storage-based barriers to data integration have gradually fallen.
  • In management support, unidimensional reporting is giving way to multivariate analytics. Where once summary statistics emerged separately from different business domains, and drawing inferences about their interconnections required administrative experience and intuition, today connections can be made at the record level deep within integrated data warehouses. Speculating about relationships between trends is giving way to exploring the implications of documented correlations.
  • In user support, authority is giving way to persuasion. Where once users had to accept institutional choices if they wanted IT support, today they choose their own devices, expect campus IT organizations to support them, and bypass central systems if support is not forthcoming. To maintain the security and integrity of core systems, IT staff can no longer simply require that users behave appropriately; rather, they must persuade users to do so. This means that IT staff increasingly become advocates rather than controllers. The required skillsets, processes, and administrative structures have been changing accordingly.

Beyond these broad thematic changes, a fourfold confluence is about to accelerate change in Enterprise IT: major systems approaching end-of-life, the growing importance of analytics, extensive mobility supported by third parties, and the availability of affordable, capable cloud-based infrastructure, services, and applications.

Systems Approaching End-of-Life

In the mid-1990s, many colleges and universities invested heavily in administrative-systems suites, often (if inaccurately) called “Enterprise Reporting and Planning” systems or “ERP.” Here, again drawing on CDS, are implementation data on Student, Finance, and HR/Payroll systems for non-specialized colleges and universities:

The pattern of implementation varies slightly across institution types. Here, for example, are implementation dates for Finance systems across four broad college and university groups:

Although these systems have generally been updated regularly since they were implemented, they are approaching the end of their functional life. That is, although they technically can operate into the future, the functionality of turn-of-the-century administrative systems likely falls short of what institutions currently require. Such functional obsolescence typically happens after about 20 years.

The general point holds across higher education: A great many administrative systems will reach their 20-year anniversaries over the next several years.

Moreover, many commercial administrative-systems providers end support for older products, even if those products have been maintained and updated. This typically happens as new products with different functionality and/or architecture establish themselves in the market.

These two milestones—functional obsolescence and loss of vendor support—mean that many institutions will be considering restructuring or replacement of their core administrative systems over the next few years. This, in turn, means that administrative-systems stability will give way to 1990s-style uncertainty and change.

Growing Importance of Analytics

Partly as a result of mid-1990s systems replacements, institutions have accumulated extensive historical data from their operations. They have complemented and integrated these by implementing flexible data-warehousing and business-intelligence systems.

Over the past decade, the increasing availability of sophisticated data-mining tools has given new purpose to data warehouses and business-intelligence systems that have until now have largely provided simple reports. This has laid foundation for the explosive growth of analytic management approaches (if, for the present, more rhetorical than real) in colleges and universities, and in the state and federal agencies that fund and/or regulate them.

As analytics become prominent in areas ranging from administrative planning to student feedback, administrative systems need to become better integrated across organizational units and data sources. The resulting datasets need to become much more widely accessible while complying with privacy requirements. Neither of these is easy to achieve. Achieving them together is more difficult still.

Mobility Supported by Third Parties

Until about five years ago campus communications—infrastructure and services both—were largely provided and controlled by institutions. This is no longer the case.

Much networking has moved from campus-provided wired and WiFi facilities to cellular and other connectivity provided by third parties, largely because those third parties also provide the mobile end-user devices students, faculty, and staff favor.

Separately, campus-provided email and collaboration systems have given way to “free” third-party email, productivity, and social-media services funded by advertising rather than institutional revenue. That mobile devices and their networking are largely outside campus control is triggering fundamental rethinking of instruction, assessment, identity, access, and security processes. This rethinking, in turn, is triggering re-engineering of core systems.

Affordable, Capable Cloud

Colleges and universities have long owned and managed IT themselves, based on two assumptions: that campus infrastructure needs are so idiosyncratic that they can only be satisfied internally, and that campuses are more sophisticated technologically than other organizations.

Both assumptions held well into the 1990s. That has changed. “Outside” technology has caught up to and surpassed campus technology, and campuses have gradually recognized and begun to avoid the costs of idiosyncrasy.

As a result, outside services ranging from commercially hosted applications to cloud infrastructure are rapidly supplanting campus-hosted services. This has profound implications for IT staffing—both levels and skillsets.

The upshot is that Enterprise, already the largest component of higher-education IT, is entering a period of dramatic change.

Beyond change in IT, the academy itself is evolving dramatically. For example, online enrollment is becoming increasingly common. As the Sloan Foundation reports, the fraction of students taking some or all of their coursework online is increasing steadily:

This has implications not only for pedagogy and learning environments, but also for the infrastructure and applications necessary to serve remote and mobile students.

Changes in the IT and academic enterprises are one reason Enterprise IT needs more attention. A second is the panoply of entities that try to influence Enterprise IT.

Overlap

One might expect colleges and universities to have relatively consistent requirements for administrative systems, and therefore that the market for those would consist largely of a few major widely-used products. The facts are otherwise. Here are data from the recent EDUCAUSE Center for Applied Research (ECAR) research report The 2011 Enterprise Application Market in Higher Education:

The closest we come to a compact market is for learning management systems, where 94% of installed systems come from the top 5 vendors. Even in this area, however, there are 24 vendors and open-source groups. At the other extreme is web content management, where 89 active companies and groups compete and the top providers account for just over a third of the market.

One way major vendors compete under circumstances like these is by seeking entrée into the informal networks through which institutions share information and experiences. They do this, in many cases, by inviting campus CIOs or administrative-systems heads to join advisory groups or participate in vendor-sponsored conferences.

That these groups are usually more about promoting product than seeking strategic or technical advice is clear. They are typically hosted and managed by corporate marketing groups, not technical groups. In some cases the advisory groups comprise only a few members, in some cases they are quite large, and in a few cases there are various advisory tiers. CIOs from large colleges and universities are often invited to various such groups. For the most part these groups have very little effect on vendor marketing, and even less on technical architecture and direction.

So why do CIOs attend corporate advisory board meetings? The value to CIOs, aside from getting to know marketing heads, is that these groups’ meetings provide a venue for engaging enterprise issues with peers. The problem is that the number of meetings and their oddly overlapping memberships lead to scattershot conversations inevitably colored by the hosts’ marketing goals and technical choices. It is neither efficient nor effective for higher education to let vendors control discussions of Enterprise IT.

Before corporate advisory bodies became so prevalent, there were groups within higher-education IT that focused on Enterprise IT and especially on administrative systems and network infrastructure. Starting with 1950s workshops on the use of punch cards in higher education, CUMREC hosted meetings and publications focused on the business use of information technology. CAUSE emerged from CUMREC in the late 1960s, and remained focused on administrative systems. EDUCOM came into existence in the mid-1960s, and its focus evolved to complement those of CAUSE and CUMREC by addressing joint procurement, networking, academic technologies, copyright, and in general taking a broad, inclusive approach to IT. Within EDUCOM, the Net@EDU initiative focused on networking much the way CUMREC focused on business systems.

As these various groups melded into a few larger entities, especially EDUCAUSE, Enterprise IT remained a focus, but it was only one of many. Especially as the y2k challenge prompted increased attention to administrative systems and intensive communications demands prompted major investments in networking, the prominence of Enterprise IT issues in collective work diffused further. Internet2 became the focal point for networking engagements, and corporate advisory groups became the focal point for administrative-systems engagements. More recently, entities such as Gartner, the Chronicle of Higher Education, and edu1world have tried to become influential in the Enterprise IT space.

The results of the overlap among vendor groups and associations, unfortunately, are scattershot attention and dissipated energy in the higher-education Enterprise IT space. Neither serves higher education well. Overlap thus joins accelerated change as a major argument for refocusing and reenergizing Enterprise IT.

The Importance of Enterprise IT

Enterprise IT, through its emphasis on core institutional activities, is central to the success of higher education. Yet the community’s work in the domain has yet to coalesce into an effective whole. Perhaps this is because we have been extremely respectful of divergent traditions, communities, and past achievements.

We must not be disrespectful, but it is time to change this: to focus explicitly on what Enterprise IT needs in order to continue advancing higher education, to recognize its strategic importance, and to restore its prominence.

9/25/12 gj-a  

The Rock, and The Hard Place

Looking into the near-term future—say, between now and 2020—we in higher-education IT have to address two big challenges. Neither admits easy progress. But if we don’t address them, we’ll find ourselves caught between a rock and a hard place.

  • The first challenge, the rock, is to deliver high-quality, effective e-learning and curriculum at scale. We know how to do part of that, but key pieces are missing, and it’s not clear how will find them.
  • The second challenge, the hard place, is to recognize that enterprise cloud services and personal devices will make campus-based IT operations the last rather than the first resort. This means everything about our IT base, from infrastructure through support, will be changing just as we need to rely on it.

“But wait,” I can hear my generation of IT leaders (and maybe the next) say, “aren’t we already meeting those challenges?”

If we compare today’s e-learning and enterprise IT with that of the recent past, those leaders might rightly suggest, immense change is evident:

  • Learning management systems, electronic reserves, video jukeboxes, collaboration environments, streamed and recorded video lectures, online tutors—none were common even in 2000, and they’re commonplace today.
  • Commercial administrative systems, virtualized servers, corporate-style email, web front ends—ditto.

That’s progress and achievement we all recognize, applaud, and celebrate. But that progress and achievement overcame past challenges. We can’t rest on our laurels.

We’re not yet meeting the two broad future challenges, I believe, because in each case fundamental and hard-to-predict change lies ahead. The progress we’ve made so far, however progressive and effective, won’t steer us between the rock of e-learning and the hard place of enterprise IT.

The fundamental change that lies ahead for e-learning
is the the transition from campus-based to distance education

Back in the 1990s, Cliff Adelman, then at the US Department of Education, did a pioneering study of student “swirl,” that is, students moving through several institutions, perhaps with work intervals along the way,before earning degrees.

“The proportion of undergraduate students attending more than one institution,” he wrote, “swelled from 40 percent to 54 percent … during the 1970s and 1980s, with even more dramatic increases in the proportion of students attending more than two institutions.” Adelman predicted that “…we will easily surpass a 60 percent multi-institutional attendance rate by the year 2000.”

Moving from campus to campus for classes is one step; taking classes at home is the next. And so distance education, long constrained by the slow pace and awkward pedagogy of correspondence courses, has come into its own. At first it was relegated to “nontraditional” or “experimental” institutions—Empire State College, Western Governors University, UNext/Cardean (a cautionary tale for another day), Kaplan. Then it went mainstream.

At first this didn’t work: fathom.com, for example, a collaboration among several first-tier research universities led by Columbia, found no market for its high-quality online offerings. (Its Executive Director has just written a thoughtful essay on MOOCs, drawing on her fathom.com experience.)

Today, though, a great many traditional colleges and universities successfully bring instruction and degree programs to distant students. Within the recent past these traditional institutions have expanded into non-degree efforts like OpenCourseWare and to broadcast efforts like the MOOC-based Coursera and edX. In 2008, 3.7% of students took all their coursework through distance education, and 20.4% took at least one class that way.

Learning management systems, electronic reserves, video jukeboxes, collaboration environments, streamed and recorded video lectures, online tutors, the innovations that helped us overcome past challenges—little of that progress was designed for swirling students who do not set foot on campus.

We know how to deliver effective instruction to motivated students at a distance. Among policy issues we have yet to resolve, we don’t yet know how to

  • confirm their identity,
  • assess their readiness,
  • guide their progress,
  • measure their achievement,
  • standardize course content,
  • construct and validate curriculum across diverse campuses, or
  • certify degree attainment

in this imminent world. Those aren’t just IT problems, of course. But solving them will almost certainly challenge IT.

The fundamental change that lies ahead for enterprise technologies
is the transition from campus IT to cloud and personal IT

The locus of control over all three principal elements of campus IT—servers and services, networks, and end-user devices and applications—is shifting rapidly from the institution to customers and third parties.

As recently as ten years ago, most campus IT services, everything from administrative systems through messaging and telephone systems to research technologies, were provided by campus entities using campus-based facilities, sometimes centralized and sometimes not. The same was true for the wired and then wireless networks that provided access to services, and for the desktop and laptop computers faculty, students, and staff used.

Today shared services are migrating rapidly to servers and systems that reside physically and organizationally elsewhere—the “cloud”—and the same is happening for dedicated services such as research computing. It’s also happening for networks, as carrier-provided cellular technologies compete with campus-provided wired and WiFi networking, and for end-user devices, as highly mobile personal tablets and phones supplant desktop and laptop computers.

As I wrote in an earlier post about “Enterprise IT,” the scale of enterprise infrastructure and services within IT and the shift in their locus of control have major implications for and the organizations that have provided it. Campus IT organizations grew up around locally-designed services running on campus-owned equipment managed by internal staff. Organization, staffing, and even funding models ensued accordingly. Even in academic computing and user support, “heavy metal” experience was valued highly. The shifting locus of control makes other skills at least as valuable: the ability to negotiate with suppliers, to engage effectively with customers (indeed, to think of them as “customers” rather than “users”), to manage spending and investments under constraint, to explain.

To be sure, IT organizations still require highly skilled technical staff, for example to fine-tune high-performance computing and networking, to ensure that information is kept secure, to integrate systems efficiently, and to identify and authenticate individuals remotely. But these technologies differ greatly from traditional heavy metal, and so must enterprise IT.

The rock, IT, and the hard place

In the long run, it seems to me that the campus IT organization must evolve rapidly to center on seven core activities.

Two of those are substantive:

  • making sure that researchers have the technologies they need, and
  • making sure that teaching and learning benefit from the best thinking about IT applications and effectiveness.

Four others are more general:

  • negotiating and overseeing relationships with outside providers;
  • specifying or doing what is necessary for robust integration among outside and internal services;
  • striking the right personal/institutional balance between security and privacy for networks, systems, and data; and last but not least
  • providing support to customers (both individuals and partner entities).

The seventh core activity, which should diminish over time, is

  • operating and supporting legacy systems.

Creative, energetic, competent staff are sine qua non for achieving that kind of forward-looking organization. It’s very hard to do good IT without good, dedicated people, and those are increasingly difficult to find and keep. Not least, this is because colleges and universities compete poorly with the stock options, pay, glitz, and technology the private sector can offer. Therein lies another challenge: promoting loyalty and high morale among staff who know they could be making more elsewhere.

To the extent the rock of e-learning and the hard place of enterprise IT frame our future, we not only need to rethink our organizations and what they do; we also need to rethink how we prepare, promote, and choose leaders for higher-education leaders on campus and elsewhere—the topic, fortuitously, of a recent ECAR report, and of widespread rethinking within EDUCAUSE.

We’ve been through this before, and risen to the challenge.

  • Starting around 1980, minicomputers and then personal computers brought IT out of the data center and into every corner of higher education, changing data center, IT organization, and campus in ways we could not even imagine.
  • Then in the 1990s campus, regional, and national networks connected everything, with similarly widespread consequences.

We can rise to the challenges again, too, but only if we understand their timing and the transformative implications.