Comedy of the Commons

SOPA and the Supreme Court

Chris Sprigman just published a lament, "A Good and Bad Week for Free Speech," in the Chronicle of HIgher Education about the Supreme Court's decision in a copyright case.

Untrained in this discipline, but a teacher and administrator responsible for my school's balance sheet, I see in this discussion an agony over our culture's shift from material work to knowledge work. For a while there, property could be securely locked in a box or bounded by a hedge. No more. 

Cash-strapped and maybe myopic universities that want to defend their traditional revenue streams, and squeeze more from new ones, can not see how they profit from the release of intellectual property to the public domain. But this behavior results from their urgent wish to hold themselves apart, as branded institutions, in their still-medieval model.

The struggle begs for a more agile model: a redefinition of legal identities, corporations, and the corporate identities we call universities. A low bar for information-sharing is simply healthy: it has the longest pedigree of any biological meme. But it does come at the price of having to yield one's cherished sense of identity, one's family, one's patrimonial right to control the semantic value of the words "mine" and "ours."

Literary lights like Mikhail Bakhtin (The Dialogic Imagination) and mathematical prodigies like Douglas Hofstadter (I Am a Strange Loop) show that an utterance is by definition "out there," and that to the degree that our shared knowledge infrastructures lack a capacity for empathy, we find our identities--and our properties--are over leveraged, and we collapse from inability to repay the debt. Maybe that's why Andrew Carnegie decided to share his wealth rather than keep it in-house. He loved his kids that much.

From viruses to Free Speech, the fittest information-sharers have survived to become something new. So I agree with Sprigman.

The SOPA debate has clarified that knowledge is worth more and has a greater marginal utility when information is shared than when it is constrained. Other values than money--respect, trust, influence--accrue not only from consistently providing good formulations of ideas, but also from being able to graft those ideas efficiently onto other ideas, without the processing cost of citation--to say nothing of royalties. There's motivation besides money to invent and express new ideas. Those values make life worth living.

And regardless of philosophy, we are seeing in the biome the limits of a resource-intensive way of living. Everything--everything--will have to be redesigned around sharing if we are to avoid resurgences of nationalism and tribalism, confiscatory wars and sieges, to say nothing of the erosion and collapse of institutions--including schools, universities, and the Constitution--that we erected to defend our right to evolve and adapt.

Who are we again?

Europe is remembering its divided past. Nations are facing down their debt and getting ready to increase taxes. As they do so, and ask, "Who benefits?", I wonder if we won't see a surge of old-fashioned nationalism as a meme at all levels.

Even though the world economy is digital and globalized, and as Basel II asserted, the effects of capital management are shared by all nations, still what will Italy's leaders probably promise to do with their new tax money? "Rebuild a strong Italy."

It is far less likely that any leaders will stay in power--whether elected or not--who put the globe's interests before those of their own native populations. We are seeing this behavior from corporations, whose trillions in cash remain uninvested, even though the investment at even a low return would probably benefit the whole.

Schooled by the corporations over the past 50 years, why should nations act differently? And at that point it's a slippery slope. We will make sacrifices for our own people.

This may be the under-message of Gavin & Stacey, a very popular BBC comedy about a Welsh and an English community brought together by a young romance. Local habits are gently mocked, and ancient Celtic grudges hinted at, throughout the show. Love conquers all, and a lot of economic sharing builds bridges over Severn: potlatch, gifts, drinking parties, meals. But there is nothing of the vast Indian or Pakistani or Muslim cultural presence in Britain except the takeout order of a curry. The very narrowness of the show's scope is part of its rightness for the times: the heck with the Korean economy. Cultural differences within a few hours' drive on the M4 are enough to worry about, and the series ends laughingly on Smithy's adoption of a Welsh expression. The closeness of a tribe is a deep reckoning.

Jon Stewart famously made the point about Zuccotti Park, that it was divided into ghettoes.

As we come to the give and take of tax revenues, let's see who is defined as "we." My prediction is of tighter fists for the barbarians--with a very low hurdle for being included in that class--and open arms for folks who speak our language.

Of warehouses and marts

Prompt:

Big central warehouse, or local datamarts? That was once the question.

The cultural challenges of data management seep between the lines of this article. No catfight is fiercer than two modelers defending their models.

Data people get into needless religious wars because in action, all data is local, but until action, it is all central, "bloody, perjured, murderous, full of blame." Or to paraphrase the old philosophy teaser, if the data is stored but there's no one there to use it, is it data?

When a canonical source can be farmed out to local users who then cycle back to the source with feedback, and if the dw has a quality adjustment procedure for handling this feedback without religious wars, you have a coherent data system that can evolve with business climates.

Certain radical db modelers (read about one at fluidinfo.com) despise ontologies for this reason: they revere the chaotic coherence provided by local users' management of the data, by analogy with biological fitness landscapes: the fittest data shall survive.

But the beauty of the DW marriage with the data mart, if united in marriage by a data quality framework, is that the survival doesn't have to be brutal. Where the core model is stable (think of the 90%+ of DNA among vertebrates in nature, or rule of law among societies), and it is allowed by a "data constitution" to evolve (think of American democracy, however ugly it may look at present), these two approaches can allow local users to develop and maintain their data, and central modelers to learn from the masses.

The big win here, though, is not for the enterprise--the corporate entity--, which may be on the ropes as the global economy changes. It is for the agile enterprises that arise and dissipate to meet market needs. One can imagine persistent data stores that exist independently as core data libraries "rentable" to corporate entities that appear and disappear, developing their own marts as clones of the central one.

Is school technology a sham?

"Technology is all a sham." So said a colleague of mine who had just about had it with the effort it takes to live with logins.

It's an understandable position. But for information technology, as for great classroom teaching, design is everything. Some school people are still waiting to see technology's return on effort. iPads, beautifully designed to adapt to many environments, are pouring into schools, without proof yet that they will help students learn to make good decisions.

Trial and error, with access to information and freedom to change, is the best way to learn. Parents pay for a school to design--to give form to--the experiences that will provide that access to people who are not yet, er, formed.

Picture these steps:

1. Plan
2. Do
3. Check
4. Act
5. Repeat

This design cycle, which emphasizes the ability to "fail quickly," is also a good way to expose a sham.

1. Set a learning goal and plan a way to get there
2. Try the way
3. Check...how is your plan working?
4. List the efforts that turned out to be useless. (Here, look to see how big the pile is that you have to sweep away. Big pile = sham), and set a new learning goal
5. Start over.

I invite those teachers and administrators who think information technology is a sham to take the following steps:
1. Plan - Set a goal for students, and decide what resources you have to work with - including all the tools available
2. Do - Using these tools, build a scaffold to help students achieve that goal.
3. Check - Is your scaffold getting them there?
4. Act - Adjust your plan based your answer to step 3
5. Start over.

Teachers who involve information technology tools in this cycle can teach better, and reach more diverse abilities of students, more effectively than traditional school has ever done. Among other things, they can collaborate in ways that traditional classrooms have always forbidden.

If we dare apply this cycle to traditional classroom teaching, we'll uncover a whole lot of shams there, too. List the sham teachers you've had in your life, and you'll get a sense of what I mean, and you may be open to new ways of schooling.

We have not yet tested information technology this way, because we have not yet allowed it to reinvent the way schools are designed. Persisting in that course of obstinacy would indeed be a sham.

A Curriculum is a Promise

When people pay for an education, they are speculating. Tuition is money paid for a perceived value based on trust in the past, on the stories told by others who have been there, with the hope of increased value in the future. It's a risk.

Students or parents can visit your campus and talk to teachers, but they can't take a test drive or read product specifications or reviews that will tell the true story of what will happen once the child is enrolled. And I have not yet seen a school that offers an unconditional money-back guarantee.

Apart from reputation ("the bubble reputation," as Shakespeare said), a curriculum's scope and sequence are the most compelling promise you can make that your school is not just froth. Promising what sequence of events a child will experience, and delighting parents with the quality of your delivery on that promise, is a powerful way, if not the only way, to keep your tuition bubble from popping, and to make your school sustainable.

Ups and Downs of Hiring PhD's

American universities produce a surplus of PhD's. Should you hire one of these available, talented people? Here are a few general guidelines.

Upside:
1. Salary. A PhD has been trained not to associate the degree with a high salary, and may be hired for at least 40% less than you would pay for others with advanced degrees.
2. Drive. A typical PhD will work harder, for longer, than the typical knowledge worker, and will require less supervision.
3. Gadfly Factor. If your organization can stand it, the typical American PhD has been trained to identify patterns and point out opportunities for improvement.
4. Information management. PhD's in almost all disciplines have been trained to find, organize, and share complex information.
5. Technology. Usually the PhD has been exposed to next-generation technology at the university, and may see good alternatives to your current technologies.

Downside:
1. Spotlight. A PhD expects to be listened to, and at length, without first having to prove why.
2. Team. A PhD may have the habit of treating an organization as a resource provider and a necessary political evil, so a PhD may not understand how to work on a team.
3. Fault finder. PhD's are trained to find fault with others' work, and credentialed for doing so. In most organizations this habit may be perceived as negative, defeatist, or cynical.
4. Results. Approval of peers and citation by others is the definition of success for a PhD. You may need to work harder to train the PhD to focus on the results that matter for your organization.
5. Smarts. The PhD's intelligence and ability to think in abstractions can alienate others if the PhD fails to acknowledge the many other smart, non-academic ways to get things done.

In Praise of Despair

Breathe out. All the way. More.

How does that feel?

I've heard it said that our purpose is to exhale. Just before we draw breath again, we are at a peak. At that turning point, imagination is blank, all else has been yielded up, and all we have left is to draw breath.

I suggest a practice of exhaling deeply.

The millisecond just at the end of exhaling is a moment of despair. When we despair, we have heaved all hopes away. And that does not seem like a good thing, especially if you've felt despair lasting longer than a breath. But when we despair, we also let go of fixed ideas and pre-judgments that had shaped our hopes, and we make room to draw in new ways to live, unchoked by those fixed ideas and pre-judgments.

A corruption spectrum

Where are you on a law - bureaucracy - corruption spectrum?

Let's give corruption its due: Cabernet sauvignon and Manchego cheese are regulated rot, made beautiful.  And fallen trees make good fertilizer for undergrowth.

But among people, corruption is a form of friction in the machinery of trust.  Despite caveat emptor, economies thrive where trust rules.  From a social perspective, trust depends on sincerity, competence, and reliability.  From an economic perspective, though, even the most intimate trust, depends on governance and law, supported by rules that allow transparency, predictability, and fairness.

At one end of a corruption spectrum, think of bribes or turning a blind eye to crimes.  At the other, think of self-serving bureaucracy.  The latter can measure its corruption by asking whether it conduces to those three terms:  transparency (availability of information), predictability (efficiency of function), and fairness (freedom from irrational prejudice).

The Economist publishes a ranking of countries by corruption, including public-service corruption.  The US is not at the very top of their good-guys list. 

As we watch events unfold against corrupt governments in the Middle East, including monarchies or aristocracies, let us take care not to link these types of rule with corruption.  A free-market democracy, which requires a bureaucracy to function, risks corruption equally, especially when the bureaucracy makes the rules for that democracy.  At the very least, a bureaucracy badly governed is prone to self-preservation rather than ensuring transparency, predictability, and fairness.

Consider:  A classical caste society restricted social mobility.  You couldn't just earn (or buy) a title and move up a level.  And yet, within and across the caste levels, people could trust each other enough to improve their conditions, if transactions are transparent, predictable, and fair.

As the story goes, a breach of trust among aristocrats sent the Greeks to level Troy.  In the epic account of that war, Odysseus smacks "a man of the people" on the back with his sceptre, but makes logical argument with one of his peers.  Caste?  Yes.  Corruption?  No.  Some describe the Iliad as the first illustration of a Western republic, because the leaders gathered together in honest debate to resolve their issues.  There's also a long and beautiful discussion between two noble warriors, Sarpedon and Glaukos, who ask each other why they must stand at the forefront of the battle.  The answer?  They are publicly accountable for the choicest goods that they enjoy for being aristocrats.

Bureaucrats need not be cowardly or servile. The ancient Chinese invented a grueling Civil Service Exam to cull the best-educated and most ethical workers for an effective administration.  Confucianism evolved as a practical and ethical philosophy to help this system maintain internal--even personal--checks and balances on a civil service that might risk serving itself, instead of serving its ends.  In short, education was a check against the corruption of self-dealing.  Education did not just provide skills. It provided ethical foundation of an economy that could count on transparency, predictability, and fairness.

Fast forward about 7,000 years. Early Modern bureaucracy in Europe (from about 1450 on) was a way to free up economies of scale in a way that benefited centralized governments. It increased control, yes, but it also provided information exchange.  Early bureaucrats in remote areas informed the locals about centralized laws, and gathered high-quality information in notebooks (along with tribute/rent/taxes) to send to the Court. On the strength of this transparency, the Court in turn used these notebooks and reports to decide what would be predictable and fair for the federated markets governed locally by nobility and merchants.

Fast forward to now.  Debate in Washington about right-sizing regulations focuses on these same questions. It's not really a matter, as Tea Party activists suggest, of what the American people want, or of confiscatory federal trade unionists, or of a blunt-object reduction of government spending.  It's a question of having an efficient free-market democracy.  If an administrative program acts to preserve itself, even if it's led by honorable people, it may, from an economic welfare perspective, stand somewhere in the darker side of the corruption spectrum.

Where does your work stand on this spectrum?  For the sake of what are you working?  And if you can't answer that, what are you doing to move it where it needs to be?

Remember the Formalists? If you do, you're probably wrong.

     Somewhere on the Internet, someone has quoted D.H. Lawrence as saying, "If you want to get over a woman, fictionalize her."  At least that's what I remember.  It's good advice.  At a party recently, I wound up standing by the food table with another divorced male in his 50's who was trying to get over not just A Woman, but The Women of Our Generation.  That group is a fiction, though for college-educated Western men who survived the 70's, it's a pretty darned familiar one.  I think it's time we all started fictionalizing more assertively.

     This post is the seed for an upcoming series on the tremendous value of fictionalization, falsified memory, and using the wrong data.

     On fictionalization:  The Russian Formalists argued that the biography of an artist was irrelevant to the value of what he produced.  Spend time with the thing itself (hence "form"alism), they argued, and you'll get more from it than from any study of the author's life. They also claimed that the direct experience of a coherent work of art transformed the person who consumed it.   The artwork necessarily "made strange" whatever it represented, often by making the medium itself (like language, or paint, or cardboard) leap forward to the senses and sensibilities that apprehended it. 

     Examples:  When Shakespeare writes, "Th'expense of spirit in a waste of shame / Is lust in action,"  all the hissing sounds jump out at us, almost before we sense what the words themselves mean, or what the moral of the lines might be.  And it's fitting because the hissing sound wakes up the topic for us, breathy and serpentine and lusty.  When a sculptor creates flowing robes in marble, we're confused for a minute, because we know it's stone, but it flows like fabric.  And we grow aware, as one of the Formalists wrote, of "what makes the stone stony."

     When we "grow aware," we grow more powerful, because we seize reality again, and our senses, sharpened, help us to move ahead with conviction and to succeed in a real world.

     This "making-strange,"  as the Russians called it, broke away the "glassy armor of familiarity" that automates daily perceptions, and thanks to art, people regain access to the truth of possibilities, or, at least, to the possibility of a truth.

     All this free-thinking happened in the early days of the Russian Revolution.  It got crushed as the Soviets started to think it was decadent, and a throwback to "art for art's sake," which was Bad For People.

      But it persists in memory, and is now maybe more valuable than ever for people interested in thriving in a culture built of information and memory.  That's where fictionalization comes in. 

     Fictionalization is the making-strange of lived experience (that's the medium for a storyteller): it renders one person's experience into a "not-true-but-true" form that can be seized and processed by another one, for some purpose to be determined. According to psychologists like Daniel Gilbert of Harvard University, author of "Stumbling on Happiness" and lots of other research reports on human expectation (he calls this "affective forecasting"),  falsified memory works in much the same way that novelists do.  

     Each of us (and by extension, an enterprise made up of groupthinking individuals) predicts a future by spinning up in our collected neurons the possible worlds of failure or success that might be.  We head in that direction guided by this GPS-like picture of where we are.  Then, once we get there, we look around, and, looking back, we find those same people (i.e. ourselves) who predicted how it would all be, kind of strange and foolish, for having left out all the important details that actually matter now that we're here.

     Of course we can require that the future match our forecast, and correspond to our data about the past, which we used to forecast the future.  As my many divorced friends will attest, this almost never works.  And as a famous economist is also quoted as having said, "The only value of economic forecasting is to build respect for astrology."

     And so it is with using the wrong data.  For a long time the Information Quality world has insisted on teaching enterprises to produce the RIGHT data:  accurate, timely, coherent, relevant, complete.  But clearly the financial world, well equipped with the RIGHT data, ignored the broader sweep of probabilities and possibilities, because (using affective forecasting) they decided that the data they were using were the data that mattered for the future they wished for--and ignored the other possibilities.

     Now that we are trying to clean up their mess, I would like to advocate for a Formalist approach to information quality:  deliberately, artistically, use the wrong data in your risk models and your CAD systems and your accounting.  Set up labs for possible worlds with the wrong data (and there are so very many ways for data to be wrong), and ask your analysts to waste some time in these fictionalized worlds. Encourage them to falsify memory.  From this diversion into fictionalized worlds, you will discover ways to open up innovation, to "make strange" the assumptions we are making today, and to uncover insights that only used to be available to artists.

I Don't Know Much About Data, but I Know What I Like

The Sancho Panza Test for Data Quality

I just had a conversation with Dylan Jones, editor and publisher of DataQualityPro, in Stratford-upon-Avon, England.  During the conversation we agreed that data-management professionals who "speak data," and people who benefit from the quality, often do not speak the same dialects.  

This made me realize a delicious disambiguator, and I'm sure I'm not the first to taste it:  SELECT value FROM Experience WHERE DQ < > DQ;

To resolve ambiguities, it's useful to listen to the Sancho Panzas of the organization: the humble workers who stand by, deal with, and adjust to the company's data, of whatever quality, on a daily basis.   Find out what they say and how they say it, and you may find the fast route to the data that needs correcting. 

I call this the Sancho Panza test:  if you can explain your DQ initiative's objective in proverbial terms--terms that make intuitive sense to the people in the organization--, it probably will succeed.

Influences and Influencers

It's not just IT vs. the business.  It's hard for a person with specific data management expertise, who "speaks data," to reach the people in the organization who are the influencers--people who "speak WITH data" and can give a DQ effort the boost it needs to succeed.   Similarly, these influencers are effective because of hidden forces in the organization, tribal undercurrents, that are the influences on how information gets produced and consumed.

As a result, more often than not, the promised claims of data quality initiatives look or sound quixotic--hard to understand, nice to dream about, impossible to achieve.

People who understand data, who "speak data" at the concrete level of data management, may speak very differently from people who use information.  They bring wizard-like wisdom to how data systems actually work.  For working this magic, they are both trusted and mistrusted by their business managers.  These two tribes need each other, and mining that dialectic is critical to a successful data-quality initiative.

This mining includes understanding the social-status dimension of the organization.  Both tribes--and both types of DQ specialist--claim that their contributions are the ones that really matter.  The "data speakers" insist that if you can't model/collect/store/move/publish data correctly, you won't have a business at all.  The "context-speakers" are too busy to be confused with these details--the business pays for this data, so it needs to serve the business.

Miguel de Cervantes to the Rescue

Consider this analogy: After he's read lots of exciting romantic novels about knights and giants, Don Quixote (let's call him DQ) really believes that a windmill is a giant.  Off he goes off to attack it, like an organization trying to do business with weak data and a whole lot of enthusiasm for its business logic.  And Sancho Panza represents the faithful DQ consultant, who patiently and persistently picks up his bruised master while pointing out that, uh, that's a windmill.

But this is why they call it a great book:  the analogy reverses without losing value.  And when we reverse the analogy, we uncover the Sancho Panza test for DQ consultants.

This time, think of Don Quixote as the data-quality specialist or even the data management specialist or software vendor, bringing to the world his specialist's perspective and vocabulary and enthusiasm, influenced by the books he's read, visioning everyday business practices, with his value added, as goldmines for the organization.  Meanwhile Sancho Panza represents the person who does a practical job every day, who knows what works around here and what doesn't.

I advocate to Data Quality (let's call it DQ) consultants that they listen to this Sancho Panza, and consider themselves as Don Quixote.  Sancho doesn't know much about data, but he knows what he likes.  He likes information that he can process in his proverbial way.  He's open to listening, but slow to change, and he'll tell you what he thinks.

Cervantes created the wise fool Sancho Panza to accompany DQ on his picaresque journey. Thanks to this foiling arrangement, readers of the novel discover that while they find Sancho Panza a relief from the knight's wacky imagination, in the end they enjoy Don Quixote just as much, for his willingness to see an elevated world.  The two together help to clarify each one's value.

In contrast to DQ's high-flown language about common things, Sancho spouts sayings like, "Hunger is the best sauce." 

(The poor father of a poor family, he knows what he's talking about. If all politics is local, and all information is local, it's possible that this little proverb speaks to the politics of data quality.  Power issues about data happen where the business context (the hunger) realizes the data's value (the sauce).  And DQ professionals must admit that the pain and shame of getting the data wrong, or the wrong data, are also realized in the live business context, not when the data's sitting there in the warehouse.)

Here's an example from the novel:  Sancho Panza has just stopped Don Quixote from attacking a group of actors who were putting on a play that DQ didn't like.

"Sancho," replied Don Quixote, "if thou hadst let me attack them as I wanted, at the very least the emperor's gold crown and Cupid's painted wings would have fallen to thee as spoils, for I should have taken them by force and given them into thy hands."

"The sceptres and crowns of those play-actor emperors," said Sancho, "were never yet pure gold, but only brass foil or tin."

"That is true," said Don Quixote, "[...] in the comedy and life of this world, where some play emperors, others popes, and, in short, all the characters that can be brought into a play; but when it is over, that is to say when life ends, death strips them all of the garments that distinguish one from the other, and all are equal in the grave."

"A fine comparison!" said Sancho; "though not so new but that I have heard it many and many a time, as well as that other one of the game of chess; how, so long as the game lasts, each piece has its own particular office, and when the game is finished they are all mixed, jumbled up and shaken together, and stowed away in the bag, which is much like ending life in the grave."

"Thou art growing less doltish and more shrewd every day, Sancho," said Don Quixote.

"[A]lways, or mostly, when Sancho tried to talk fine and attempted polite language, he wound up by toppling over from the summit of his simplicity into the abyss of his ignorance; and where he showed his culture and his memory to the greatest advantage was in dragging in proverbs." (Chapter XII)

Applying the Sancho Panza Test

To understand the "culture and memory" of a company, keep a notebook of the habits and sayings of information workers who would be affected by the data quality initiative.  Mining these sayings can help to assess the culture to which you are bringing the DQ message, and can help to identify where DQ might attack successfully, and where DQ might go on fruitless and painful quests.

I worked with a company of 25,000 employees where a singular speech habit prevailed throughout the company:  Everybody who wanted to be anybody used the word "So" to start a sentence.  They did it in place of "Um"--as if the speaker were announcing a sophisticated conclusion (like "therefore," but more casual) from data, but instead of using data, they were invoking the spirit of data, like Don Quixote's magic books.

This happened when a conversation was started, when a question was answered, even in the middle of an otherwise normal conversation.  It was a "language gesture," a way of establishing authority because it borrowed from the logic of inductive conclusion that was a meme in the company's culture.  It also meant that managers made their mark not by performing to specifications, but by coming up with insightful ideas.  In this culture, more data is better, and quality is second-rate, because at the mid-manager level, the behavior of LOOKING and SOUNDING analytic got jumbled up (like the kings with the pawns) with the true value-added analysis that led to dollars and cents.

Moreover, in this culture, actual data quality, if it were delivered, risked exposing these off-the-cuff analysts. Their analysis might easily be flawed, which carried with it political risk:  in a culture that valued analysis, the ability to convince others that you are analytic carried as much political weight as the analysis itself. To be exposed would mean to lose face.

Solution?

The best step we made in this environment was to ask senior executives to declare themselves owners of key reports. From this position we could identify the metrics on those reports that mattered most, and then drill down from there to the data elements whose quality would have the greatest effect.  When we asked the report generators to identify the data that mattered, we got quixotically unmanageable numbers of attributes, because they regarded everything they did as important--and they claimed to "speak data" by naming granular systems and sources, rather than principles of correction that would add lasting value.