Rolandt

Having been away for a spell, I have lots of email questions built up in my inbox. Here is another one that seemed apt considering we are well on our way to spring.

It is around this time of year that cycling clubs begin their annual schedule of group rides. Depending on the club, these can include anything from paceline training rides to brevet-style social jaunts, endurance rides, and 3-speed meet ups (see also: On Club Rides and Finding the Right One for You). Some hybrid of the formal training ride and the social ride seems like the most common style on offer. Typically, these rides will be divided into several groups, based on ability, with corresponding average speeds posted as a guide (i.e. Beginners' Group: 12mph, Intermediate Group: 15mph; Advanced/Fast Group: 18mph+). This way, cyclists who are considering joining for the first time can decide which group best suits their abilities.

It seems fairly straightforward. After all, most cyclists use computers nowadays, so we have a pretty good idea of what average speeds we are capable of doing. Join the group with the corresponding speed and it should be fine. However, what often happens (and I have experienced this myself!) is that the club ride feels far more difficult than expected, sometimes to the point that the first time out we just can’t hang on.

So… why?

A few readers have asked me this question over the years. And, having pondered the mysteries of this phenomenon myself after several rather humiliating club-ride initiations, here are some things I have noticed...

The Novelty of a Steady Pace
First of all, we all have our individual patterns of energy highs and lows. When we ride alone, we are able to make the best use of them. We speed up when we feel an energy burst, slow and rest when we hit a dip. In the end it averages out. By contrast, the club ride tends to proceed at a steady pace. And this in itself takes some practice. Being unable to take advantage of our natural energy rhythms can feel absolutely exhausting.

Rest Makes a Difference 
By the same token, when we ride alone,  or casually with a couple of friends, we probably also tend to take breaks whenever we feel like it. Tired in the middle of a ride? No worries. We get off the bike, walk around, eat a snack, maybe snap a photo. On club rides, there are usually no breaks (unless it's a super long ride with a lunch stop). A 30 mile training ride usually means 30 miles without stopping - which is a lot more demanding than a 30 mile ride with rest breaks.

What About Terrain?
Considering that terrain plays a role in the average speed we are able to put out, it helps to have a look at the route the club ride will be doing. If the route has more elevation gain than the routes you typically ride, you may not be able to hold your 'usual' average speed.

The Optimistic Self-Assessment   
Repeatedly psychological research shows that on the whole people tend to slightly overestimate their skills, abilities, favourable traits - even physical features such as height! - compared to what they actually are, despite the availability of correct data. It follows that we also tend to be overly optimistic about our average cycling speed - so that even when supplied with concrete evidence, such as cyclo-computer data, we might tend to cherry pick average speeds from our 'best' rides when deciding what speed we are capable of holding on a typical random day. Of course in the course of a club ride, held on a typical random day, the truth comes out!

...And is that a bad thing? Personally I think not, even if it does knock the ole self-esteem down a notch. After all, there is nothing quite like a few shattering club rides to turn one's aspirational average speed into their actual average speed!

On the other hand, structured, performance-oriented club rides really aren't for everyone. There is nothing wrong with going it alone or keeping it casual with a few close friends, sticking to one's natural energy rhythms, and taking plenty of breaks. It is useful to know there is a difference, is all.

I recall the first time I joined a club ride in Ireland. This particular ride was women's only, and it was funny, because the leaflet advertising it read something like 'This is a ladies' ride, not a beginner's ride!' I phoned the ride leader to clarify, and she said I should be comfortable holding 16-17mph. I was feeling good that summer. So I thought, well okay I can do that - especially in a group, where I'll be getting the benefit of drafting.

I lasted maybe two thirds of the ride. In fairness to this group, they didn't drop me; I peeled off voluntarily when we hit the meaty portion of a long climb and I just couldn't take the pace anymore. And as I hobble-pedaled defeatedly home in a stupor, I remember thinking 'Those girls must have gone faster just to mess with me!'

Later I looked over the ride stats on my computer. An average of 16.7mph (not counting my ride home), precisely as promised. They were, after all, ladies of their word.

I recently attended a panel discussion hosted by General Assembly in Singapore entitled, “So you want to be a Data Scientist/Analyst”. The panel featured professionals in different stages of their careers and offered a wealth of information to an audience of hopefuls, including tips on how to land a job as a data scientist, and stories debunking myths that color this field.

This talk was presented at The University of Edinburgh's Moray House School of Education

Let me begin with a story. In December 2012 – we all remember 2012 right? “The Year of the MOOC” – I was summoned to Palo Alto, California for a small gathering to discuss the future of teaching, learning, and technology. I use the verb “summoned” deliberately.

The event was organized by Sebastian Thrun, who at the beginning of the year had announced that he was resigning his full time professor position at Stanford in order to launch Udacity, his online education startup. It was held at Stanford in its artificial intelligence lab, which was a little awkward a venue as Thrun’s office – he still had an office on campus, of course – was right next to those of Daphne Koller and Andrew Ng, his fellow Stanford AI professors who’d announced in April that they were launching a competitor company, Coursera.

When Thrun first invited us all to this event – about ten of us – he promised that at the end of the weekend, we would take a ride in a zeppelin over San Francisco. And I thought “like hell I will.” I’ve seen A View to a Kill. I know what happened to the dissenters who got into a zeppelin in that movie. But as it turned out, the zeppelin company had gone out of business – I imagine that many people, like myself, could only think about Christopher Walken and Grace Jones’ characters and opted not to go.

So instead of a zeppelin, we got to ride in one of Google’s self-driving cars, which was of course the project that Thrun had been working on when he gave his famous TED Talk in 2011 – and that, in turn, was where he heard Salman Khan give his famous TED Talk. It was when and where Thrun decided that he needed to rethink his work as a Stanford professor in order to “scale” education.

Thrun “drove.” He steered the car onto I–280 and then let the car take over, and I have to say – and I say this as a professional skeptic of technology – it was this strange combination of the utterly banal and the utterly impressive. (It was 2012, I should reiterate, so it was right at the beginning of all this hype about a future of autonomous vehicles.)

The car was covered in cameras and sensors, inside and out – even a QR code on the driver’s side glove compartment that you were supposed to scan to sign Google’s Terms of Service before riding. Seemingly the most dangerous element of our little jaunt was that other drivers swerved and slowed down as they stared at the car, with its giant camera on top and Google logo on the sides. There was Thrun with his hands off the wheel, feet off the pedals, eyes not on the road, sometimes turning around entirely to face the passengers in the back seat, explaining how the car (and Google, of course) collected massive amounts of data in order to map the road and move efficiently along it.

Efficiency. That’s the goal of the self-driving car. (You’re free to insert here some invented statistic about the percentage of space and energy that are wasted by human-driven traffic and human driving patterns and that will be corrected by roads full of autonomous vehicles. I vaguely recall Thrun doing so at least.)

It was then and there on that trip that I had a revelation about how many entrepreneurs and engineers in Silicon Valley conceive of education and the role of technology in reshaping it: that is, if you collect enough data – lots and lots and lots of data – you can build a map. This is their conceptual framework for visualizing how “learners” (and that word is used to describe various, imagined students, workers, and consumers) get from here to there, whether it’s through a course or through a degree program or towards a job. With enough data and some machine learning, you can identify – statistically – the most common obstacles. You can plot the most frequently traveled path and the one that folks traverse most quickly. You can optimize. And once you’ve trained those algorithms, you can apply them everywhere. You can scale.

We can debate this model (we should debate this model) – how it works or doesn’t work when applied to education. (Is learning “like a map”? Is learning an engineering problem? Is the absence of “data” or algorithms really a problem?) But one of the most important things to remember is that this is (largely) a computer scientist’s model. It’s the model of human learning by someone who claims expertise in machine learning, a field of study which has aspired to model if not surpass the human mind. And that makes it a model in turn that rests on a lot of assumptions about “learning” – both how humans “learn” and how machines “learn” to conceptualize and navigate their worlds.

It’s a model. It’s a metaphor.

It’s an aspiration – a human aspiration, to be clear. This isn’t what machines “want.” (Machines have no wants.)

I think many of us quickly recognized back in 2012 that, despite the AI expertise in the executive offices of these MOOC companies, there wasn’t much “artificial intelligence” beyond a few of their course offerings; there wasn’t much “intelligence” in their assessments or in their course recommendation engines. What these MOOCs were, nonetheless, were (and still are) massive online honeypots into which we’ve been lured – registering and watching and clicking in order to generate massive education datasets.

Perhaps with this data, the MOOC providers can build a map of professional if not cognitive pathways. Perhaps. Someday. Maybe. In the meantime, these companies continue to collect a lot of “driving” data.

Who controls the mapping data and who controls the driving data and who controls the autonomous vehicle patents are, of course, a small part of the legal and financial battles that are brewing over the future of autonomous vehicles. Google versus Uber. Google versus Didi (a Chinese self-driving car company). We can speculate, I suppose, about what the analogous battles might be in education – which corporation will sue which corporation, claiming they “own” learning data and learning roadmaps and learning algorithms and learning software IP.

(Spoiler alert: it won’t actually be learners – just like it’s not actually drivers – even though that’s where the interesting data comes from: not from mapping the roads, but from monitoring the traffic.)

As we were driving on the freeways around Palo Alto in the Google autonomous vehicle, someone asked Sebastian Thrun what happens if there’s an unexpected occurrence while the car is in self-driving mode. Now, the car is constantly making small adjustments – to its speed, to its distance to other vehicles. “But what would happen if, say, a tree suddenly came crashing down in the road right in front of it,” the passenger asked Thrun.

“The car would stop,” he said. The human driver would be prompted to take over. Hopefully the human driver is paying attention. Hopefully there’s a human driver.

Of course, the “unexpected” occurs all the time – on the road and in the classroom.

Recently the “ride-sharing” company Uber flouted California state regulations in order to start offering an autonomous vehicle ride-sharing service in San Francisco. The company admitted that it hadn’t addressed at least one flaw in their programming: that its cars would make a right hand turn through a bicycle lane (the equivalent of a left-hand turn here in the UK). Uber didn’t have a model for recognizing the existence of “bike lane” (and as such “cyclists”). It’s not that the car didn’t see something “unexpected”; that particular “unexpected” was not fully modeled, and the self-driving car didn’t slow, and it didn’t stop.

In this testing phase of Uber’s self-driving cars, it did still have a driver sitting behind the wheel. Documents recently obtained by the tech publication Recode revealed that Uber’s autonomous vehicles drove, on average, less than a mile without requiring human intervention.

The technology simply isn’t that good yet.

At the conclusion of our ride, Thrun steered the Google self-driving car back to his house, where he summoned a car service to take us back to our hotel. Giddy from the experience, one professor boasted to the driver what we’d just done. He frowned. “Oh,” he said. “So, you just put me out of a job?”

“Put me out of a job.” “Put you out of a job.” “Put us all out of work.” We hear that a lot, with varying levels of glee and callousness and concern. “Robots are coming for your job.”

We hear it all the time. To be fair, of course, we have heard it, with varying frequency and urgency, for about 100 years now. “Robots are coming for your job.” And this time – this time – it’s for real.

I want to suggest – and not just because there are flaws with Uber’s autonomous vehicles (and there was just a crash of a test vehicle in Arizona last Friday) – that this is not entirely a technological proclamation. Robots don’t do anything they’re not programmed to do. They don’t have autonomy or agency or aspirations. Robots don’t just roll into the human resources department on their own accord, ready to outperform others. Robots don’t apply for jobs. Robots don’t “come for jobs.” Rather, business owners opt to automate rather than employ people. In other words, this refrain that “robots are coming for your job” is not so much a reflection of some tremendous breakthrough (or potential breakthrough) in automation, let alone artificial intelligence. Rather, it’s a proclamation about profits and politics. It’s a proclamation about labor and capital.

And this is as true in education as it is in driving.

As Recode wrote in that recent article,

Successfully creating self-driving technology has become a crucial factor to Uber’s profitability. It would allow Uber to generate higher sales per ride since it would keep all of the fare. Uber has currently suffered losses in some markets partly because of having to offer subsidies to attract drivers. Computers are cheaper in the long run.

“Computers are cheaper in the long run.” Cheaper for whom? Cheaper how?

Well, robots don’t take sick days. They don’t demand retirement or health insurance benefits. You tell them the rules, and they obey the rules. They don’t ask questions. They don’t unionize. They don’t strike.

A couple of years ago, there was a popular article in circulation in the US that claimed that the most common occupation in every state is “truck driver.” The data is a little iffy – the US is a service economy, not a shipping economy – but its claim about why “truck driver” is still fairly revealing: unlike other occupations, the work of “truck driver” has not been affected by globalization, the article claimed, and it has not (yet) been affected by automation. (The CEO of Otto, a self-driving trucking company now owned by Uber, just predicted this week that AI will reshape the industry within the next ten years.)

Truck driving is also a profession – an industry – that’s been subject to decades of regulation and deregulation.

That regulatory framework is just one of the objects of derision – of “disruption” and dismantling – of the ride-sharing company Uber. Founded in 2008 – ostensibly when CEO Travis Kalanick was unable to hail a cab while in Paris – the company has become synonymous with the so-called “sharing” or “freelance” economy, Silicon Valley’s latest rebranding of technologically-enhanced economic precarity and job insecurity.

“Anyone” can drive for Uber, no special training or certification required. Well, anyone who’s 21 or older and has three years of driving experience and a clean driving record. Anyone with car insurance. Anyone whose car has at least four doors and is newer than 2001 – Uber will also help you finance a new car, even if you have a terrible credit score. Your loan payments are simply deducted from your Uber earnings each week.

All along, Uber has been quite clear, that despite wooing drivers to its platform, using “independent contractors” is only temporary. The company plans to replace drivers with driverless cars.

Since its launch, Uber has become infamous for its opposition to regulations and to unions. (Uber has recently been using podcasts broadcast from its app in order to dissuade drivers in Seattle from unionizing, for example.)

And I’ll note here in case this sounds too much like a talk on autonomous vehicles and not enough on automated education, I am purposefully putting these two “disruptions” side by side. After all, education is fairly regulated as well – accreditation, for example, dictates who gets to offer “real” degrees. There are rules about who gets to run a “real school.” Trump University, not a real school. And there are rules as to who gets to be in the classroom, rules about who can teach. But any semblance of job protections – at both the K–12 level and at the higher education level in the US – is under attack. (Again, this isn’t simply about replacing teachers with computers because computers have become so powerful. But it is about replacing teachers nonetheless.) You no longer need a teaching degree (or any teaching training) in Utah. And while the certification demands might still be in place in colleges and universities, they’ve been moving towards a precarious teaching labor force for some time now. More than three-quarters of the teaching staff in the US are adjuncts – short-time employees with no job security and often no benefits. “Independent contractors.” Uber encourages educators to earn a little cash on the side as drivers.

Like I said, I’m not sure I believe that the most prevalent job in the US is “truck driver.” But I do know this to be true: the largest union in the United States is the National Education Association. The other teachers’ union, the American Federation of Teachers, is the sixth largest. Many others who work in public education are represented by the second largest union in the US, the Service Employees International Union.

Silicon Valley hates unions. It loathes organized labor just as it loathes regulations (until it benefits from regulations, of course).

Now, for its part, Uber has also been accused of violating “regulations” like the Americans with Disabilities Act for refusing to pick up riders with service dogs or with wheelchairs. A fierce proponent of laissez-faire capitalism, Uber has received a fair amount of negative press for its price gouging practices – it uses what it calls “surge pricing” during peak demand, increasing the amount a ride will cost in order, Uber says, to lure more drivers out onto the road. It’s implemented surge pricing not just on holidays like New Year’s Eve but during several weather-related emergencies. The company has also actively sabotaged its rivals – attacking other ride service companies as well as journalists.

None of this makes the phrase “Uber for Education” particularly appealing. But that’s how Sebastian Thrun described his company Udacity in a series of interviews in 2015.

“At Udacity, we built an Uber-like platform,” he told the MIT Technology Review. “With Uber any normal person with a car can become a driver, and with Udacity now every person with a computer can become a global code reviewer. … Just like Uber, we’ve made the financials line up. The best-earning global code reviewer makes more than 17,000 bucks a month. I compare this to the typical part-time teacher in the U.S. who teaches at a college – they make about $2,000 a month.”

“We want to be the Uber of education,” Thrun told The Financial Times, which added that, “Mr Thrun knows what he doesn’t want for his company: professors in tenure, which he claims limits the ability to react to market demands.”

In other words, “disrupt” job protections through a cheap, precarious labor force doing piecemeal work until the algorithms are sophisticated enough to perform those tasks. Universities have already taken plenty of steps towards this end, without the help of algorithms or for-profit software providers. But universities are still bound by accreditation (and by tradition). “Anyone can teach” is not a stance on labor and credentialing that many universities are ready to take.

Udacity is hardly the only company that invokes the “Uber for Education” slogan. There’s PeerUp, whose founder describes the company as “Uber for tutors.” There’s ProfHire and Adjunct Professor Link, Uber for contingent faculty. There’s The Graide Network, Uber for teaching assistants and exam markers. There’s Parachute Teachers, which describes itself as “Uber for substitute teachers.”

Again, what we see here with these services are companies that market “on demand” labor as “disruption.” These certainly reflect larger trends at work dismantling the teaching profession – de-funding, de-professionalization, adjunctification, a dismissal of expertise and experience.

Anyone can teach. Indeed, the only ones who shouldn’t are probably the ones in the classroom right now – or so this story goes. The right wing think tank The Heritage Foundation has called for an “Uber-ized Education.” The right wing publication The National Review has called for “an Uber for Education.” Echoing some of the arguments made by Uber CEO Travis Kalanick, these publications (and many many others) speak of ending the monopolies that “certain groups” (unions, women, liberals, I don’t know) have on education – ostensibly, I guess, on public schools – and bringing more competition to the education system.

US Secretary of Education in a speech earlier this week also invoked Uber as a model that education should emulate: “Just as the traditional taxi system revolted against ridesharing,” she told the Brookings Institution, “so too does the education establishment feel threatened by the rise of school choice. In both cases, the entrenched status quo has resisted models that empower individuals.”

All this is a familiar refrain in Silicon Valley, which has really cultivated its own particular brand of consumerism wrapped up in the mantle of libertarianism.

Travis Kalanick is just one of many tech CEOs who have praised the work of objectivist “philosopher” and “novelist” Ayn Rand, once changing the background of his Twitter profile to the cover of her book The Fountainhead. He told The Washington Post in a 2012 Q&A that the regulations that the car service industry faced bore an “uncanny resemblance” to Rand’s other novel, Atlas Shrugged.

(A quick summary for those lucky enough to be unfamiliar with the plot: the US has become a dystopia overrun by regulations that cause industries to collapse, innovation to be stifled. The poor are depicted as leeches; the heroes are selfish individualists. Eventually business leaders rise up against the government, led by John Galt. The government collapses, and Galt announced that industrialists will rebuild the world. It is a terrible, terrible novel. It is nonetheless many libertarians’ Bible of sorts.)

I’ve argued elsewhere (and I’ve argued repeatedly) that libertarianism is deeply intertwined in the digital technologies developed by those like Uber’s Kalanick. And I don’t mean here simply or solely that these technologies are wielded to dismantle “big government” or “big unions.” I mean that embedded in these technologies, in their design and in their development and in their code, are certain ideological tenets – in the case of libertarianism, a belief in order, freedom, work, self-governance, and individualism.

That last one is key, I think, for considering the future of education and education technology – as designed and developed and coded by Silicon Valley. Individualism.

Now obviously these beliefs are evident throughout American culture and have been throughout American history. Computers didn’t cause neoliberalism. Computers didn’t create libertarians. (It just hooked them all up on Twitter.)

Indeed, there’s that particular strain of individualism that is deeply, deeply American which contributed to libertarianism and to neoliberalism and to computers in turn.

I’d argue that that strain of individualism has been a boon for the automotive industry – for car culture. Many Americans would rather drive their own vehicles rather than rely on – and/or fund – public transportation. I think this is both Uber’s great weakness and also, strangely, its niche: you hail a car, rather than take the bus. The car comes immediately; you do not have to wait. It takes you to your destination; you needn’t stop for others. As such, you can dismiss the need to develop a public transportation infrastructure as some cities in the US have done, some opting to outsource this to Uber instead.

In a car, you can move at your own pace. In a car, you can move in the direction you choose – when and where you want to go. In a car, you can stop and start, sure, but most often you want to get where you’re going efficiently. In a car – and if you watch television ads for car companies, you can see evidence of this powerful imaginary most strikingly – you are truly free.

Unlike the routes of public transportation – the bus route, the subway line – routes that are prescribed for and by the collective, the car is for you and you alone. The car is another one of these radically individualistic, individualizing technologies.

The car is a prototype of sorts for the concept of “personalization.”

Branded. Controlled. Manufactured en masse. Mass-marketed. And yet somehow this symbol of the personal, the individual.

We can think about the relationship too between education systems and individualism. I believe increasingly that’s how education is defined – not as a collective endeavor or a public good, but as an individual investment.

“Personalization” is a reflection of that.

“Personalized” education promises you can move at your own pace. You can (ostensibly) move in the direction you choose. You can stop and start, sure, but most often you want to get where you’re going efficiently. With “personalized” software – – and if you read publications like Edsurge, you can see evidence of this powerful imaginary most strikingly – the learner is truly free.

Unlike the routes of “traditional” education – the lecture hall, the classroom – those routes that are prescribed for and by the collective, “personalized software” is for you and you alone. The computer is a radically individualistic, individualizing technology; education becomes a radically individualistic act.

(I’ll just whisper this because I’d hate to ruin the end of the movie for folks: this freedom actually involves you driving.)

Let me pause here and note that there are several directions that I could take this talk: data collection and analysis as “personalization,” for example. The New York Times just wrote about an app called Greyball that Uber has utilized to avoid scrutiny from law enforcement and regulators in the cities into which it’s tried to expand. The app would ascertain, based on a variety of signals, when cops might be trying to summon an Uber and would prevent them from doing so. Instead, they’d see a special version of Uber – “personalized” – that misinformed them that there were no cars in the vicinity.

How is “personalized learning” – the automation of education through algorithms – a form of “greyballing”? I am really intrigued by this question.

Another piece of the automation puzzle for education (and for “smart car” and for “smart homes”) involves questions of what we mean by “intelligence” in that phrase “artificial intelligence.” What are the histories and practices of “intelligence” – how have humans been ranked, categorized, punished, and rewarded based on an assessment of intelligence? How is intelligence performed – by man (and I do mean “man”) and by machine? What do we read as signs of intelligence? What do we cultivate as signs of intelligence – in our students and in our machines? What role have educational institutions had in developing and sanctioning intelligence? How does believing there’s such a thing as “machine intelligence” challenge some institutions (and prop up others)?

But I want to press on a little more with a look at automation and labor: this issue of driverless cars and driverless school, this issue of “freedom” as being intertwined with algorithmic decision-making and precarious labor.

I am lifting the phrase “driverless school” for the title of this talk from Karen Gregory who recently tweeted something about the “driverless university.” I believe she was at a conference, but in the horrible way that Twitter strips context from our utterances, I’m going to borrow it without knowing who or what she was referring to and re-contextualize the phrase here for my purposes because that’s the visiting speaker’s prerogative.

I do think that in many ways MOOCs were envisioned – by Thrun and by others – as a move towards this idea of a “driverless university.” And that phrase and the impulse behind it should prompt us to ask, no doubt, who is currently “driving” school? Who do education engineers imagine is doing the driving? Is it the administration? The faculty? The government? The unions? Who is exactly going to be displaced by algorithms, by software that purport to make a university “driverless”?

What’s important to consider, I’d argue, is that if we want to rethink how the university functions – and I’ll just assume that we all do in some way or another – “driverlessness” certainly doesn’t give the faculty a greater say in governance. (Indeed, faculty governance seems, in many cases, one of the things that automation seeks to eliminate. Think Thrun’s comments on tenure, for example.) More troubling, the “driverlessness” of algorithms is opaque – even more opaque than universities’ decision-making already is (and that is truly saying something).

And despite all the talk of catering to what Silicon Valley has lauded in the “self-directed learner,” to those whom Tressie McMillan Cottom has called the “roaming autodidacts,” the “driverless university” certainly does not give students a greater say in their own education either. The “driverless university,” rather, is controlled by the engineers who write the algorithms, those who model the curriculum, those who think they can best navigate a learning path. There is still a “driver,” but that labor and decision-making power is obscured.

We can see the “driverless university” already under development perhaps at the Math Emporium at Virginia Tech, which The Washington Post once described as “the Wal-Mart of higher education, a triumph in economy of scale and a glimpse at a possible future of computer-led learning.”

Eight thousand students a year take introductory math in a space that once housed a discount department store. Four math instructors, none of them professors, lead seven courses with enrollments of 200 to 2,000. Students walk to class through a shopping mall, past a health club and a tanning salon, as ambient Muzak plays.

The pass rates are up. That’s good traffic data, I suppose, if you’re obsessed with moving bodies more efficiently along the university’s pre-determined “map.” Get the students through pre-calc and other math requirements without having to pay for tenured faculty or, hell, even adjunct faculty. “In the Emporium, the computer is teacher,” The Washington Post tells us.

“Students click their way through courses that unfold in a series of modules.” Of course, students who “click their way through courses” seem unlikely to develop a love for math or a deep understanding of math. They’re unlikely to become math majors. They’re unlikely to become math graduate students. They’re unlikely to become math professors. (And perhaps you think this is a good thing if you believe there are too many mathematicians or if you believe that the study of mathematics has nothing to offer a society that seems increasingly obsessed with using statistics to solve every single problem that it faces or if you think mathematical reasoning is inconsequential to twenty-first century life.)

Students hate the Math Emporium, by the way.

Despite The Washington Post’s pronouncement that “the time has come” for computers as teachers, the time has been coming for years now. “Programmed instruction” and teaching machines – these are concepts that are almost one hundred years old. (So to repeat, the push to automate education is not about technology as much as it’s about ideology.)

In his autobiography, B. F. Skinner described how he came upon the idea of a teaching machine in 1953: Visiting his daughter’s fourth grade classroom, he was struck by the inefficiencies. Not only were all the students expected to move through their lessons at the same pace, but when it came to assignments and quizzes, they did not receive feedback until the teacher had graded the materials – sometimes a delay of days. Skinner believed that both of these flaws in school could be addressed by a machine, so he built a prototype that he demonstrated at a conference the following year.

Skinner’s teaching machine broke concepts down into small concepts – “bite-sized learning” is today’s buzzword. Students moved through these concepts incrementally, which Skinner believe was best for “good contingency management.” Skinner believed that the machines could be used to minimize the number of errors that students made along the way, maximizing the positive behavioral reinforcement that students received. Skinner called this process “programmed instruction.”

Driverless ed-tech.

“In acquiring complex behavior the student must pass through a carefully designed sequence of steps,” Skinner wrote, “often of considerable length. Each step must be so small that it can always be taken, yet in taking it the student moves somewhat closer to fully competent behavior. The machine must make sure that these steps are taken in a carefully prescribed order.”

Driverless and programmatically constrained.

Skinner had a dozen of the machines he prototyped installed in the self-study room at Harvard in 1958 for use in teaching the undergraduate course Natural Sciences 114. “Most students feel that machine study has compensating advantages,” he insisted. “They work for an hour with little effort, and they report that they learn more in less time and with less effort than in conventional ways.” (And we all know that if it’s good enough for Harvard students…) “Machines such as those we use at Harvard,” Skinner boasted, “could be programmed to teach, in whole and in part, all the subjects taught in elementary and high school and many taught in college.” The driverless university.

One problem – there are many problems, but here’s a really significant one – those Harvard students hated the teaching machines. They found them boring. And certainly we can say “well, the technology just wasn’t very good” – but it isn’t very good now either.

Ohio State University psychology professor Sidney Pressey – he’d invented a teaching machine about a decade before B. F. Skinner did – said in 1933 that,

There must be an “industrial revolution” in education, in which educational science and the ingenuity of educational technology combine to modernize the grossly inefficient and clumsy procedures of conventional education. Work in the schools of the future will be marvelously though simply organized, so as to adjust almost automatically to individual differences and the characteristics of the learning process. There will be many labor-saving schemes and devices, and even machines – not at all for the mechanizing of education, but for the freeing of teacher and pupil from educational drudgery and incompetence.

Oh not replace you, teacher. To free you from drudgery, of course. Just like the Industrial Revolution freed workers from the drudgery of handicraft. Just like Uber drivers have been freed from the drudgery of full-time employment by becoming part of the “gig economy” and just like Uber will free them from the drudgery of precarious employment when it replaces them with autonomous vehicles.

Teaching machines – the driverless school – will replace just some education labor at first, the bits of it the engineers and their investors have deemed repetitive, menial, unimportant, and let’s be honest, those bits that are too liberal. But it doesn’t seem interested, however, in stopping students from having to do menial tasks. The “driverless university” will still mandate students sit in front of machines and click on buttons and answer multiple choice questions. “Personalized,” education will be stripped of all that is personal.

It’s a dismal future, this driverless one, and not because “the machines have taken over,” but because the libertarians who build the machines have.

A driverless future offers us only more surveillance, more algorithms, less transparency, fewer roads, and less intellectual freedom. Skinner would love it. Trump would love it. But we, we should resist it.

Interested in Context.IO but don’t know where to start? Glitch is a great way to test-drive an API without all the hassle of setting up a dev environment or figuring out your first steps. Glitch handles all that for you – one click and you have a working app with a great online editor. You can even invite other developers to join your project and export to GitHub when you’re ready to leave the playground!

We’re really excited about the platform that Glitch has built and we think it’s a fantastic tool for improving the way we work with other developers. As always: feel free to open issues or PR’s on the GitHub repo, check our docs for more information about the Context.IO api, and feel free to drop us a line if you get stuck. Or just to chat: we’d love to hear what you’re building !

This is a guest post by Biraj Karmakar, who has been active promoting Mozilla and Mozilla software in India for over 7 years. Biraj is taking the initiative of organizing a series of workshops throughout the country to convince technical people to (mozillians or not) that may be interested in getting involved in Mozilla to use Firefox Nightly.

 

 

Fellow mozillians, I am super excited to inform you that very soon we are going to release a new campaign in India  called “India uses Firefox Nightly“. Behind this campaign, our mission is to increases Firefox nightly usages in India.

Why India?

As we all know we have a great Mozilla community around India. We have a large number of dedicated students, developers and evangelists who are really passionate about Mozilla.

We have seen that very few people in India actually know about Firefox Nightly. So we have taken an initiative to run a pilot campaign for Firefox Nightly throughout India.

Firefox Nightly, as a pre-release version of Firefox targeting power-users and core Mozilla contributors, is a glimpse of what the future of Firefox will be for hundreds of millions of people. Having a healthy and strong technical community using and testing Nightly is a great way to easily get involved in Mozilla by providing a constant feedback loop to developers. Here you can test lots of pre-release features.

So it needs a little bit of general promotion, which will help bring a good number of tech-savvy, power-users who may become new active community members.

Few Key points

Time Frame: 2 months Max              Hashtag: #INUsesFxNightly

Event Duration: 3 – 5 Hours              Total events: 15

Who will join us: We invite students, community members, developers, open source evangelists to run this campaign.

Parts of Campaign

Online activities:

Mozillians spread the message of this campaign around India as well as through social media (Facebook, Twitter, Instagram), blogs, promotional snippets, email, mailing list, website news items etc.

Offline activities:

Here, any community member or open source enthusiast can host one event in their area or join any nearby event to help organizers. The event can be held at a startup company, Schools, Universities, Community centres, Home, Cafés.

Goals for this initiative

Impact:

• 1000 Nightly Installed
• 20 New Active Contributors

Strength:

• 30 Mozillians run events (2 mozillians per event)
• 500 Attendees

 

BTW have you tried Firefox Nightly yet, download it now?

 

More details will come soon. Stay tuned!

 

We need many core campaign volunteers who will help us to run this initiative smoothly. If you are interested in joining the campaign team, please let me know.

Have design skills? We need a logo for this campaign, please come and help us here.

This rezoning in the Downtown Eastside has been anticipated for some time. The site is big, and has been vacant for several years. It was last owned by Concord Pacific, who designed a condo project here, but some years later handed it on to the City of Vancouver as part of a Community Amenity Contribution package that allowed Concord to build additional density near Cambie Bridgehead.

An initial proposal for a ten-storey mixed-use building designed by W T Leung was submitted in March. It had small scale retail uses on the ground floor, with a health care clinic and medical and dental offices occupying the rear portion of the ground floor, as well as the second and third floors. The upper floors had a 12 foot setback on the west side, needed there because of the windows on the side of the adjacent building.

The project has now been revised following the comments of the Urban Design Panel. It now has 231 non-market rental units with 54 of them 2-bed family apartments. It’s being developed by The Chinatown Foundation working with Vancouver Affordable Housing Agency. Given the location and the need in that area it will have a mix of low-income tenants, including some families, and will be managed by a third-party housing operator. In October last year it was reported that the $30 million that the foundation plans to contribute would allow 125 units to be rented out at subsidized rates.

Riding an e-bike to work puts a positive spin on your whole day. The bottom line is car commuting has negative impacts on your whole life. Whether it's health, time, or stress riding an electric bike for your commute will positively improve your daily experience. 

Give yourself extra time and soak in the ride.

There is a lot of scientific research that supports the fact that riding an electric bike to work is going to improve your mood and increase your productivity at work. How is this? Biking has a meditative like effects that prime your brain for optimal focus. Struggle with time in the morning? Consider this: with the time it takes to park and walk to your office building, biking can take just as long. Plan ahead, and get your gear ready the night before so you're less stressed before heading out. Then- breathe in the fresh air and enjoy the ride. 

 

Go Electric and Sweat-Free

Worried about sweat? There's certainly ways around that. For starters, switch to an electric bike and get some panniers for your frame. That way you reduce your chances of working up a sweat and trapping hit on your back from a backpack. However, it still might be more comfortable for some to bring a change of clothes for once you arrive at the office. 

Get in a good mental state

Electric biking is proven to put you in a positive mental state. Similar to yoga, biking can have a meditative like effect on your mental state. Additionally, aerobic exercise creates endorphins and primes your brain so that you arrive at work feeling energized and ready to focus.

Be Friendly

One of the fantastic benefits of biking is the community! Most bike commuters will agree- one of the best parts is how cordial the riders you meet along the way are. Smile to fellow bikers and even strike up a conversation if it seems appropriate. It will help the ride feel even more fun.

Get in Exercise

 

If you know you're about to head to work for sit for 8 hours, doing your body the favor of stretching out and working it up can get back triple fold
The benefits of electric biking to work can be felt triple fold. Plus, electric bike owners are twice as likely to pedal to work than regular bike owners. If this is your point of struggle and you know you're headed to the office to sit for 8 hours do yourself a favor and e-bike to work, you won't regret it.

When the Trump administration banned electronics larger than a mobile phone for passengers from several Middle East airports, airlines needed to respond. A press release from Etihad Airways tries to explain the problem and their solution, but circles around aimlessly instead. Here’s how to use a ROAM analysis to make communications like this better. Etihad’s email … Continued

The post How to fly readers directly to their destination: a lesson from Etihad Airways appeared first on without bullshit.

“I took my camera out of the pocket of my windproof and clumsily opened it with my thickly gloved hands. I clipped on the lenshood and ultra-violet filter and then shuffled down the ridge a little so that I could get the summit into my viewfinder. Tenzing had been waiting patiently, but now, at my request, he unfurled the flags wrapped around his ice axe and, standing on the summit, held them above his head…

…the thought drifted through my mind that this photograph should be a good one if it came out at all. I didn’t worry about getting Tenzing to take a photograph of me – as far as I knew, he had never taken a photograph before and the summit of Everest was hardly the place to him how.”

1. How will this meta layer of digital augmentation place photos at a point in time? Will the trend for photos with superimposed weather and emojis, for instance, allow viewers in 50 years time to instinctively know, ‘Oh, that’s a 2010s shot’, in the same way certain photographic stock denotes the 1950s or 1970s?
2. What will be the next large-scale creative trend after selfies? The human desire to preserve themselves in a moment is timeless, but surely the smartphone snap is not the zenith of this desire for self-regard?

Part of Friday Inspirations, an ongoing MEX series exploring tangents and their relationship to better experience design. We explain the origins of the Inspirations series in this MEX podcast and article.  Share your own inspirations on Twitter at #mexDTI.

Next week, I’m running a couple of workshops on behalf of We Are Open Co-op at the Co-operative Education and Research Conference. Perhaps unsurprisingly, I’m going to focus on the overlap of co-operativism and Open Badges, to explore the concept of ‘co-operative character’. This is something that was emphasised by the early pioneers of the co-operative movement, and feels like something that badly needs resurrecting.

As part of the research for my sessions, I came across a paper by Keith Crome and Patrick O’Connor that they published after presenting at last year’s conference. It’s entitled ‘Learning Together: Foucault, Sennett and the Crisis of Co-operative Character’, and was published in the Autumn 2016 issue of The Journal of Co-operative Studies (49:2, ISSN 0961 5784). The authors were kind enough to help me find a copy to help with my preparations and thinking.

It’s a well-written paper, and the kind that the reader feels could almost be unpacked into something book-length. As the paper is so wide-ranging in scope, it sparked all kinds of ideas in my mind, so I had to be disciplined to retain a focus on how co-operative character might be encouraged through the use of badges. Pivotal to this, I feel, is the authors’ persuasive argument that co-operative character is a virtue, rather than a collection of skills.

Co-operation is a matter of character – it designates an attitude, a disposition, a way of being and acting. And getting to grips with co-operation is essential, so that what is needed is not an account of the various skills that are held to make it up, but a description that conveys the vivacity of the co-operative character as it is inculcated in teaching and learning…

Initially, I was slightly dismayed by this, as I thought co-operative character might not be the kind of thing that is badge-able. However, although badges do tend to be used to scaffold skills development, there’s no reason why they shouldn’t be used to in developing co-operative character. We just need a slightly different approach.

Crome and O’Connor explore two main arguments against co-operative character being a collection of skills:

1. Co-operation is inherently positive — “A skill can be put to good use, but it can also be used to harm… A virtue, on the other hand, is fixed: it always looks to the good, otherwise it is not a virtue but a vice.” The authors suggest that the opposite of co-operation is ‘collusion’ – an approach that actively prevents co-operation with other groups.
2. Co-operation is distinct from technical proficiency — Imagining the case of a callous doctor or rude builder, the authors state: “Even if it is not the case, we would see why someone might say that it ought to be the case that the character of the builder or the doctor is of no significance – what matter is how well they do their job, how technically proficient they are within their respective sphere of expertise.” In other words, co-operation transcends particular techniques and practices, “as co-operation is a virtue relevant to broader society”.

So, to develop co-operative character, we need a more orthogonal approach than the usual skills grid or competency framework. We need something that recognises that people are on a character-building journey. This journey is likely to look very different in various contexts.

I don’t have all the answers yet, that will only come through – yep, you guessed it – co-operation, but our own co-op has done some thinking in a related area. We want to encourage people to learn more about co-ops as business entities, but also about the co-operative movement more generally. Back in December, we wondered what it would look like to badge Principles 5, 6, and 7 of the International Principles of Co-operation.

We’re believers in minimum viable badges so have begun to issue the Co-op Curious badge to recognise those who have taken the first step in the journey to finding out about more about co-ops. The first ones we issued were to those people who came across to our in-person meetup in a co-operatively owned pub. Other badges we thought up as part of this process were Co-operative Collaborator (issued to members of two or more co-operatives who work together on a joint project), Co-operative Convenor (issued to people who form relationships between co-operatives), and Co-op Convert (issued to people who contribute knowledge or time to co-op educational projects or programs).

There are a whole series of badges that could be used to evidence the seven principles that make up the International Principles of Co-operation. Embarking on this kind of journey feels more like what Crome and O’Connor were getting at in their article.

The closest analogy I can think of with the process I’m going through with my preparations to become a Mountain Leader. While this does focus explicitly on evidencing knowledge and skills, the outcome is actually character-based. Among other things, Mountain Leaders should be resilient, encouraging, and prepared. So, in rejecting co-operative character as a process of skills development, Crome and O’Connor are effectively putting it on a different phenomenological footing:

When we speak of character – when we give witness to the good character of an acquaintance, or when we say that someone is of a generous character – we are speaking about someone’s disposition to act or behave in a certain way. Moreover, if character is tied to ethical values, it nonetheless does not denote a purely interior attitude or set of principles; character is expressed in action and behaviour…

All of this begs the question of why you would even need badges for co-operative character at all. Surely, we know co-operation when we see it? In this regard, co-operative character is no different from anything else: the reason we require credentials is for those times when the person we’re trying to convince is at a distance. We are already known to our immediate community, but need ways to provide data points so that others can do enough triangulation to be convinced of the type of person we are.

Returning to our own worker-owned co-op, we’ve been thinking recently about our membership rules and how to grow. Other co-ops in a similar position to us have based eligibility criteria for membership on the amount of time worked. We, on the other hand, are considering a badge-based approach. This would be more tricky to do to begin with, but instead of being focused on the kind of work that could be done in any organisation, co-operative or otherwise, a badge-based approach would lend a distinctive co-operative character to the application and onboarding process.

To conclude, then, I think that badges can be used to develop co-operative character. However, the importance is not the earning of the badge itself, but the way that it evidences the International Principles of Co-operation. Eventually, as with all credentials, the rubber hits the road, and the credential itself, as a proxy for the thing, is no longer required. Credentials are a means to an end.

I’m looking forward to the workshops, as I’m sure people will bring their own thinking, and experience to this particular area!

Main image CC BY-NC Karen Horton. We Are Open Co-op artwork CC BY-ND Bryan Mathers.

In the past, popular movements have forced major industries to confront their need for ethical reform. But today‘s media, policymakers and activists don’t yet seem prepared to fix the tech sector’s problems. So how will reform happen?

First things first: Why does tech need to be reformed? The short answer is, tech is changing everyone’s lives, but while there are many benefits of today’s tech that we love, there are significant new economic and social risks that tech companies are introducing to society. When tech companies make decisions that affect our lives, we don’t have any way to appeal those decisions, or to meaningfully effect change. That’s a situation ripe for reform.

As President Obama phrased it:

[A] capitalism shaped by the few and unaccountable to the many is a threat to all.

The single biggest threat to the long-term health and growth of the tech sector is the backlash that could be caused by tech’s worst abuses and excesses.

---

Which issues matter most? Let’s look at three key issues that have the broadest negative impact on the widest range of people:

1. Most major tech companies are deeply exclusionary. There’s been a tremendous amount of conversation about the systematic exclusion of women/people of color within tech companies, especially when it comes to career advancement, compensation, funding and equity. Yet despite all the talk, precious little measurable progress has been made. This disparity has reached the point of crisis, and worse, this systematic underrepresentation has helped cause the following two major issues.
2. Tech is increasing economic insecurity for many. In addition to the familiar “robots taking our jobs” concerns amongst industrial workers, new tech startups are often predicated on destabilizing fields that aren’t traditionally thought of as being at risk. Worse, these new “disruptors” often rely on entering new markets using methods that are illegal or unethical. Since many jobs are no longer protected by labor organizations or unions, workers are often ill equipped to defend against attacks from extremely well-funded tech companies which ignore regulations.
3. Tech is enabling widespread public surveillance and threatening privacy. People across the political spectrum have deep-seated and well-considered objections to widespread government surveillance. But few technology companies have acknowledged or addressed their complicity in designing systems that helped enable such surveillance. Worse, many companies (particularly those reliant on advertising) misuse the massive amounts of personal data they gather from users, making corporate surveillance as objectionable as government surveillance. Trading personal privacy for “free” online services is increasingly seeming like a bad deal.

To summarize these three points: Tech companies are making people worry about their jobs and feel creeped out, without offering the chance to benefit and profit from tech success. That doesn’t mean people hate consumer technology like smartphones and apps — we love them! But the fact that we are investing enormous amounts of time and money in using these products makes it more likely that people will resent when those companies betray their trust.

Is it too late?

It’s important to acknowledge another perspective, people who do believe tech has overstepped its bounds, but who think it’s too late, and that the forces behind today’s tech companies are too powerful to fight. While the concern is understandable, we should assume that the same factors that let tech companies capture so much power so also quickly make them vulnerable to smart reform efforts. And I’m an optimist, so I’m not willing to give up without trying.

Can we just write new laws?

Today, technology is essentially the secular religion of America. Tech companies are far too large and influential to think of them as just a narrow “sector”. There is no technology industry.

This means that conventional activism alone, while important, is not going to lead to the kind of broad-scale reform that’s necessary. When Ralph Nader decided to critique the automotive industry, there was a broad base of support amongst both policy makers and media who were willing to entertain his arguments. There was even a natural constituency for his skepticism about the excesses of the automakers. This led to the opportunity for meaningful reform of even an enormously powerful industry.

Similarly, the financial crisis provided enough cultural capital for Elizabeth Warren’s thoughtful and forceful critique of the financial industry to gain a foothold, and to eventually support the creation of the Consumer Financial Protection Bureau. Good policy only followed once there was widespread concern about the issues at stake.

In these previous examples, we see industries being meaningfully reined in by fairly conventional methods. But many of the worst systemic imbalances in society have arisen when we treat the rise of a new sector as an inevitability, or as synonymous with “progress”. For example, though we now understand the last century of highway and transportation policy in the U.S. to have been deeply discriminatory, exacerbating existing inequities in everything from housing policy to education, at the time even many ordinarily-skeptical communities were hopeful about the potential that the highway system provided.

Even where we have seen policy successes, as in the successful regulation of the financial industry’s credit card fees with the CARD Act, it has been due to both consumers being (painfully!) familiar with the issue and policy makers being literate in the market being regulated. By contrast, with today’s consumer technologies, most end users are only literate in the human-facing aspects of the products they’re using, not in the technical decisions being made behind the scenes. It’s analogous to how an ordinary consumer might have some sense of the terms of their own mortgage, but very little awareness of how credit default swaps function.

And worse, most of the policy makers who might be in a position to rein in the tech industry are proudly, profoundly illiterate about today’s technology, both in terms of implementation and of its implications.

The bottom line: appropriate regulation to rein in tech abuses is likely to lag significantly behind the market, reactively responding to problems rather than proactively anticipating them and preventing them. In the meantime, the companies that are getting things wrong will continue to get richer and more powerful. We need better laws, but we can’t just sit and wait for them.

Can boycotts work?

Typically, social movements lean on product boycotts and direct consumer purchasing pressure to force change in big companies. But the interconnected, networked nature of today’s technology, combined with the increasingly rapid centralization onto a small handful of platforms owned by a cabal of tech titans, makes it almost impossible to refuse to participate. Refusing to buy tuna if it wasn’t dolphin-safe was straightforward, but it’s unreasonable to ask people to forgo seeing photos of their grandkids on Facebook on the off chance that Facebook is secretly sharing their data with the NSA.

Indeed, the marginalized people most victimized by tech’s excesses are also the ones who most need the opportunities that tech like smartphones or social networks can provide. We can’t just tell people “dont’ buy it” or “don’t use it” when it comes to tech that has negative impacts because it also cuts off opportunity to learn essential job skills and to make vital professional connections. There’s too much baby in the bathwater.

Direct, networked action

If lawmakers are going to follow instead of lead, and boycotts would hurt consumers worse than companies, what can we do?

The answer lies in mimicking the techniques by which tech companies amassed so much power in the first place: building powerful networks that are too resilient to easily be destroyed.

In this model, we adopt aspects of traditional activist movements, including clearly articulated policy goals, strong and coherent language to describe the problem, a lightweight participatory model that gives everyone a role in the movement, and direct actions that people can personally perform on a daily basis.

Similarly, we look at regulatory reforms that have had significant impact on other powerful industries, and adopt the best techniques from those efforts: model legislation for policy makers at every level of government, education through documentation like white papers and direct conversation with regulators, incremental policy changes that can be incorporated into other legislation affecting these companies, and direct court challenges for transgressions that violate existing laws.

But in contrast to prior industry reforms, many of these activist and policy efforts will happen without an organizing entity, or a political action committee, or a lobbying group. Learning from modern movements like #BlackLivesMatter and #FightFor15, we can provide a way for individuals to act and to coordinate without necessarily being official members of an organization. This will be especially critical as we’ll need participation from people who are currently working within the major tech companies, and most will be understandably wary of visibly participating in such efforts.

What about media?

Perhaps the biggest wildcard in an effort to reform tech is the shifting role of media. Most major media organizations have journalists who are willing to be critical of tech on the grounds of being insufficiently diverse, violating privacy, or threatening people’s jobs. But the chilling effect of tech titans like Peter Thiel working to destroy a media outlet can’t be overstated — media outlets are going to be far less likely to be critical of the Uber-wealthy leaders of the sector.

Worse, tech has in many cases succeeded in marginalizing and sidelining the academic researchers who traditionally critique and interrogate powerful industries, either by being a dominant source of funding for their academic institutions or by successfully reframing public opinion to such a degree that thoughtful critics are dismissed as Luddites or cranks. It’s no wonder that a thoughtful analysis of contemporary tech criticism explains:

Some of the most novel critiques about technology and Silicon Valley are coming from women and underrepresented minorities, but their work is seldom recognized in traditional critical venues. As a result, readers may miss much of the critical discourse about technology if they focus only on the work of a few, outspoken intellectuals.

We can’t rely on media alone to hold tech companies accountable in an era when tech moguls have destroyed some major media outlets and invested in many of the others.

The answer to these challenges is the same as the answer to the shortcomings of conventional activism and policy making: we’ll need to form lightweight networks of individuals committed to doing the work ourselves. Obviously, individuals can’t personally sustain long-term investigative research, but individual contributors can identify and articulate issues, and the community as a whole can support, fund and amplify more ambitious efforts.

The bottom line

Ultimately, people who love technology, who are still enamored of its amazing potential, need to feel a sense of urgency about changing the culture and practices of the big tech companies. The era of being seen only as positive contributors to society is quickly coming to an end, and without a healthy culture of self-criticism, the backlash will be so strong that it takes the good tech products down with the bad.

But tech has long had a wonderful tradition of accepting bug reports and trying to get those bugs fixed. As I mentioned in Wired a few years ago, there are examples of successful user revolts pushing back overreach or abuse from even the most powerful tech companies. And everyone at the top of the tech ecosystem, from investors to board members to CEOs is finding it increasingly impossible to ignore the reality that cultural headwinds may pose as much of a threat to their success as any technical challenges.

People in tech love to talk about disruption, love to talk about how we’re changing the world. In this moment where reform is necessary, we face the opportunity to prove if those intentions are truly sincere.

Back in 2014 I blogged about first snowfall dates for a given U.S. state. It’s April 1, 2017 and we’re slated to get 12-18″ of snow up here in Main and @mrshrbrmstr asked how often this — snow in May — has occurred near us.

As with all of these “R⁶ posts, expository is minimal and the focus is generally to demonstrate one small concept.

What I’ve done here (first) is make a full tidyverse update to the snowfirst code posted in the aforementioned blog post. You’ll need to clone that repo if you’re trying to work verbatim from the code below (otherwise just change file path code):

library(rprojroot)
library(stringi)
library(hrbrthemes)
library(tidyverse)

pre <- find_rstudio_root_file()

# Get and read in Maine precip ------------------------------------------------------

URL <- "http://cdiac.ornl.gov/ftp/ushcn_daily/state17_ME.txt.gz"
fil <- file.path(pre, "data", basename(URL))
if (!file.exists(fil)) download.file(URL, fil)

read_fwf(file = fil,
         col_positions = fwf_widths(c(6, 4, 2, 4, rep(c(5, 1, 1, 1), 31)),
                                    col_names = c("coop_id", "year", "month", "element",
                                                  flatten_chr(map(1:31, ~paste("r_", c("v", "fm", "fq", "fs"),
                                                                               .x, sep=""))))),
         col_types = paste0("ciic", paste0(rep("iccc", 31), collapse=""), collapse=""),
         na = c("", "NA", "-", "-9999")) %>%
  gather(day, value, starts_with("r_v")) %>%
  select(-starts_with("r_")) %>%
  mutate(day = as.numeric(stri_replace_first_fixed(day, "r_v", ""))) %>%
  mutate(date = sprintf("%s-%02d-%02d", year, month, day)) -> daily_wx

# Read in stations ------------------------------------------------------------------

URL <- "http://cdiac.ornl.gov/ftp/ushcn_daily/ushcn-stations.txt"
fil <- file.path(pre, "data", basename(URL))
if (!file.exists(fil)) download.file(URL, fil)

read_fwf(file = file.path(pre, "data", "ushcn-stations.txt"),
         col_positions = fwf_widths(c(6, 9, 10, 7, 3, 31, 7, 7, 7, 3),
                                    col_names = c("coop_id", "latitude", "longitude",
                                                  "elevation", "state", "name",
                                                  "component_1", "component_2",
                                                  "component_3", "utc_offset")),
         col_types = "cdddcccccc") -> stations

closestStation <- function(stations, lat, lon, restrict_to = NULL) {
  if (!is.null(restrict_to)) stations <- filter(stations, state == restrict_to)
  index <- which.min(sqrt((stations$latitude-lat)^2 +
                            (stations$longitude-lon)^2))
  stations[index,]
}

# compute total snow amounts per month ----------------------------------------------

(near_me <- closestStation(stations, 43.2672, -70.8617, restrict_to="ME"))

Now that we have the data, the short lesson here is just exposing the fact that you can get blank facets for free with ggplot2. I’m pointing this out as many folks seem to not like reading R documentation or miss things in said documentation (in fact, I had to be instructed today by @thomasp85 about a ggplot2 theme element setting that I didn’t know about and should have since I do try to keep up).

filter(daily_wx, coop_id == near_me$coop_id, element=="SNOW", value>0) %>%
  count(year, month, wt=value) %>%
  ungroup() %>%
  mutate(
    n = n / 10, # readings are in 10ths of inches
    date = as.Date(sprintf("%s-%02d-01", year, month)),
    month_name = lubridate::month(date, TRUE, FALSE)
  ) %>%
  ggplot(aes(x=date, y=n)) +
  geom_segment(aes(xend=date, yend=0), size=0.75, color="#9ecae1") +
  scale_y_continuous(limits=c(0, 65)) +
  facet_wrap(~month_name, ncol=3, drop=FALSE, scales="free") +
  labs(x=NULL, y="inches", title="Total snowfall in a given month by year",
       subtitle="Data for Station id 176905 — Portland (Maine) Jetport") +
  theme_ipsum_rc(grid="Y", axis_text_size=8)

Without ggplot2 helping us out we would have had to do some work to have those no-value facets to show up. I also like how there are no x-axis labels since there’s no data. ggplot2::facet_wrap() has many, very granular options for customizing the appearance of facets:

facet_wrap(facets, nrow = NULL, ncol = NULL, scales = "fixed",
           shrink = TRUE, labeller = "label_value", as.table = TRUE,
           switch = NULL, drop = TRUE, dir = "h", strip.position = "top")

If you haven’t played with them, you can use this example to try them out.

Fin

Even though that visualization gets the message across, I kinda like this view a bit better:

filter(daily_wx, coop_id == near_me$coop_id, element=="SNOW", value>0) %>%
  count(year, month, wt=value) %>%
  ungroup() %>%
  mutate(n = n / 10) %>%
  complete(year, month=1:12) %>%
  mutate(
    date = as.Date(sprintf("%s-%02d-01", year, month)),
    month_name = factor(lubridate::month(date, TRUE, FALSE), levels=rev(month.name))
  ) %>% 
  ggplot(aes(year, month_name)) +
  geom_tile(aes(fill=n), color="#b2b2b2", size=0.15) +
  scale_x_continuous(expand=c(0,0.15), position="top") +
  viridis::scale_fill_viridis(name = "Total inches", na.value="white") +
  labs(x=NULL, y=NULL, title="Total snowfall in a given month by year",
       subtitle="Data for Station id 176905 — Portland (Maine) Jetport") +
  theme_ipsum_rc(grid="", axis_text_size = 10) +
  guides(fill=guide_colourbar(label.position = "top", direction = "horizontal", title.vjust = 0)) +
  theme(legend.title = element_text(size=10)) +
  theme(legend.key.height = unit(0.5, "lines")) +
  theme(legend.position = c(0.9, 1.25))

The precision is lacking in the heatmap view, but you get a quick impression of when it has/hasn’t snowed. Plus you get to use viridis 💙

All the updated code in in the snowfirst repo.

Crank you your own, small code snippets or ideas to the R community. R⁶ is an open tag and perhaps we can band together to make a distributed cadre of helpful, digestible posts the R community can benefit from.

Beware the barrenness of a busy life.

— Socrates

According to THIS by Richard Zussman on the CBC web site, we can expect the Province of BC to match the $ 2.2B Federal contribution to transit expansion in the Metro Vancouver area. Official word at 9 a.m.

Isn’t April Fools’ day tomorrow?  Or did I wake up in an alternate universe?

No matter.  This is a good announcement.  Glad to hear it.

But this does leave open the question as to where the other 20% will come from:

CBC Story:  The municipalities, represented by the TransLink Mayors Council, will now be responsible for finding the additional 20 per cent to cover the estimated $5.5 billion it will cost for the two major projects.

A senior member of the provincial government says it is willing to work with the municipalities to figure out how to fund their share.

Following last week’s federal budget, de Jong said the province’s suggested option is for municipalities to use money from the developments along the transit lines to pay for the transit projects

The street can be used for many things, and disrupting the travelled portion of the road for some of the best show tunes out of an epic Broadway musical fits right in. Just as long as the performances mesh with the length of the crosswalk walk signal.

As reported in the Rolling Stone, James Corden enlisted Dan Stevens, Josh Gad and Luke Evans to join his ragtag Crosswalk theater company’s production of Beauty and the Beast on the street outside CBS on The Late Late Show Wednesday. 

Despite the celebrity of the actors and the obvious quality of the costumes and the performance, the video shows that drivers were not too amused. But the impact of this crosswalk performance is priceless.

“After a frantic parking lot rehearsal, the show got underway with Corden leading a spirited performance of “Belle” – the opener from the 1991 animated musical – on the crosswalk before the lights changed. The company tore through similarly dangerous performances of “Gaston” and “Be Our Guest,” with car horns serving as applause. “

A  shortened version of the performance is linked below.

City of Vancouver has published this summary report on the recent Arbutus Greenway consultations.  Peoples’ ideas and hopes heard via around 4,000 public interactions.

Consider the increasing likelihood of a subway terminus station at — guess where — Arbutus and Broadway.  Imagine the delightful synergies available from integrating the Arbutus Corridor, the subway station and a grade-separated crossing of Broadway at Arbutus for people on foot and people on bikes.

New to me was the City’s inherent contractual obligation for streetcar that is part of their purchase agreement with CP Rail.

Otherwise, hear (and see) what the people have said (PDF stuff HERE):

Here’s a one-page summary:  click to view entire report

Jumpstarted by Elijah Meeks asking why visualization people are leaving the field for less visually-centric industry jobs, there’s been ample discussion about data visualization’s role in companies.

This naturally leaks over to the ongoing discussion about what visualization is and should be. Moritz Stefaner, who’s been at it since before I even knew what visualization really was, chimed in with his experiences and what he’s seen as a freelancer.

Yet, as I argued earlier already, I don’t think we gain much from overemphasizing the (supposedly) fundamental differences between “serious/functional” and “aesthetic/entertaining” data visualizations, or, conversely, diminishing Excel dataviz work as “not really data visualization”.

I am thinking back to the time when it was fashionable to “draw lines in the sand” or to attack designers on live TV. The harsh, narrow-minded criticism that novel designs and approaches faced for a while did not always lead to better results, but, in contrast, scared talented folks away from the community. I am really quite happy that, by now, we have a data visualization community that understands the many purposes of data visualization beyond scientific analysis.

Many purposes. That’s the key here.

Visualization can be a tool or a skill set that aids in the overarching goal of understanding data, whether it be quantitatively, qualitatively, or emotionally. Maybe you use the tools. Maybe you make the tools. Maybe you use the tools that you make. You can go as far as you want with any of these routes, and the one you choose brings various job titles.

I’m completely detached from industry. (I mean, I’m one guy running a site from a home office, so I’m detached from a lot of things.) But in my experience, visualization can and should be a stand-alone profession. It’s not a big conceptual jump — if you go far enough — to see how the person who knows how to make charts can become the chart-maker.

Tags: industry, jobs

Whether you’re already a diehard Pokémon Go enthusiast or wondering what all the hype is about (and why people are walking aimlessly around public spaces), one thing is certain: every brand marketer can learn some great gamification lessons from this worldwide phenomenon. Pokémon Go designers got their user experience right, and here are four things you can learn from them.

 

1. Use the Power of Mixed Reality

One of the most prominent features of this game is mixed reality: the blur between the digital and the physical world. It features augmented reality plus a real map of the players’ physical surroundings.

 

Here we learn that digital doesn’t mean you can forget about the physical. You need to provide a great customer experience for both. In Pokémon Go, even the social elements have both physical and digital counterparts. For example, you can catch Pokémon with a real friend in the physical world.

 

You can also battle over “gyms” digitally, team up with friends in the digital world, and then come full circle and meet them in real person.

Part of the fun of this game is that you have to walk and explore the physical world rather than sit in front of the computer or stare at the phone. This is also part of the game’s novelty factor.

 

Augmented reality (AR) is still in its infancy and for many brands it may seem cost prohibitive to experiment with AR to engage customers.

However, one of the concepts of Pokémon Go is location sensitive engagement. Players must be at certain real, physical locations to engage in certain activities (e.g. catch a Pokémon, battle over gyms, or stock up supplies).

 

While there is controversy around brands sponsoring locations (e.g. the rumored McDonald’s location sponsorships), brands need to evaluate if sponsoring locations could be a way to raise brand awareness, or even participate in your own reward program linked to the game.

 

Bottom line: We should all be thinking in new ways in light of this game. How can your brand leverage the popularity of Pokémon Go?

 

2. Engage & Provide Value Before Asking Anything From the User

This strategy lets people engage with the game before they have to provide any information whatsoever. You are not compelled to purchase any items. You can enjoy the game as is. You’re not even asked to register and create a username before you learn how to capture a Pokémon and experience how easy and fun it is to engage with their AR.

 

What brands can learn from this is that it pays to be generous before asking anything from a potential customer. Creating engagement by providing value to your potential customers is a smart way to get people hooked. This value may be purely entertainment (i.e. fun) or something beyond. Radical generosity is an effective yet overlooked approach to win people’s loyalty.

 

A related point is that Pokémon Go is first a game for you before it’s a game with friends. This game is very good at focusing on the player first before leveraging any social element (e.g. friends and social graph). This is often hard for brands, because so many are so used to leveraging people’s social graph to spread the word of mouth, and ask people to share brand content.

 

Pokémon Go is initially a game just for you to play yourself and enjoy alone, exploring the world and catching Pokémon. Only after you’ve gained proficiency, skills and level up to level five can you compete with others in “gym” battles. Why does this matter?

 

The designers ensured that the game has to be fun and entertaining for a player before you get to play with friends and share the experience. The counter side of this clever design is that if you don’t find it fun, you just stop. Your friends are not affected by you stopping. This is unlike other social games that depend critically on the fact that your friends have to play (e.g. Draw Something). If you stop, their experience is impacted negatively.

 

Bottom line: Focusing on the individual first is a smart way to engage the individual and also to protect other people’s user experience.

 

3. Follow the Steps of the Gamification Spectrum to Keep the Game Going

The Gamification Spectrum is a patented framework that outlines the level of reward and corresponding level of challenge needed to keep the player engaged with the available gamification tools in the market. It provides the design paradigm for keeping the players engaged over long-term and drive the behaviors you want from them.

 

Pokémon Go follows this Spectrum well by starting out very simple to engage the largest possible audience. Then it levels up in baby steps with many front loaded rewards to keep as many of the initially engaged audience as possible.

 

Bottom line: Step-by-step, players are challenged and rewarded correspondingly to keep their engagement momentum going.

 

4. Track the Data

Lastly, the game does a good job at tracking everything the player does, as well as all the Pokémon a player has captured. Pokémon have different types, heights and weights and can be powered up and evolved. This provides the player many different opportunities for rewards and recognitions. For example, you get a badge for catching five flying Pokémon, or a badge for 10 poisonous Pokémon, etc. Players can also get badges for the different behaviors they exhibit (e.g. collector, breeder, scientist, backpacker, ace trainers, etc.). No matter how you much (or how little) you play, or how you do or like to play the game, there are always numerous, individualized rewards that are relevant to your specific playing behavior.

 

Bottom line: No two customers are exactly alike. Don’t put them into big general buckets as with traditional demographics. Use the wealth of social and behavioral data to segment them finely in multiple dimensions and understand them individually.

 

Getting Gamification Right

Although gamification is a great way to engage people, few brands have gotten it right. The success of Pokémon Go provides many great insights and design strategies as a game.

 

For brands, they need to use this as inspiration to help them leverage the power of gamification to help engage their customers.

Learn what you can from it and see how you can create your own “go” for your brand.

 

Image Credit: iphonedigital.

This article originally appeared in CMSWire.

 

Related Blogs

• #OnAirWithLithium: Gamification Best Practices
• Recap: Lithium SoCal Meetup on 8/17
• An Interview on The Value of Gamification for Today’s Brands and Consumers

 

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Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.

 

Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.

It’s been a while since I blogged. I took a hiatus on my writing due to the hectic travel schedule I had this year. For the rest of this year, I will attempt to focus more on the subject of data science. I feel this is an area where the industry is most confused and looking for guidance. Based on my background, I feel its where I can contribute the most.

 

Before I dive into the more technical subject, I will finish my story on how I became a data scientist. This is the last chapter I plan to write on my data science journey. The real journey is obviously much longer, but I can’t possibly cover all the details in my blog. The previous installments of this mini-series are linked below:

1. Where It All Started: How I Became a Data Scientist—(1) Follow the Data
2. How I Became a Data Scientist—(2) Hurdles Along the Way
3. How I Became a Data Scientist—(3) Do Good Research
4. How I Became a Data Scientist—(4) A Eureka Moment

 

Let’s start with a question. What are some of the most important attributes of a good data scientist?

 

If you do some research, you will often find that beyond the technical prowess in math, statistics, and computer science, you also need to have good communication and storytelling skills. A good data scientist must be able to explain the complex math and statistics to non-technical decision makers without compromising the rigor and accuracy. Ironically, verbal communication was never my strength.

 

Today I am going to share a story that I don’t talk about much, because I generally don’t like to talk about myself. However, I thought there are perhaps some lessons we can all learn from it. It’s a story from my distant past about how I overcome my greatest fears of communications (i.e. speaking and writing).

 

Learning to Socialize

Most people in the industry probably haven’t known me long enough to realize that I am actually an extreme introvert. Many years ago (when I was in college and the early years of my PhD), I was the stereotypical “nerd.” My wife often says that part of me is like a milder version of Sheldon Cooper from The Big Bang Theory. She would further clarify that it is not the part of his genius or eccentricity, but the rest of his quirkiness. Quite literally, I was one of those people who would write complex formulae on napkins at bars and play with my fingers at parties (there were no smartphones back then). I never really knew how to socialize, let alone present and speak publically. Consequently, I never talked much.

 

So how did I improve and grow into an international speaker? It was a very long journey, but there are a few things that propelled me forward along the way. One of these was a failed relationship during the second year of my graduate study. While trying to deal with a breakup, I decided to travel and get away from it all. I didn’t have any time to plan, so I just picked up a Lonely Planet Guide and off I went. I explored as I read, backpacking from city to city until my savings ran out and I barely had enough cash to get a ticket back home.

 

Then something amazing happened! When I was in a country where I didn’t speak the language, I felt much more comfortable to be alone and silent. I had a perfectly legitimate reason to not interact at all (i.e. I don’t know anyone and I can’t even speak the language). I was an outlier everywhere I went, but I was treated as normal and accepted. Even when I was forced to communicate with the locals due to necessity, I didn’t get any weird disdainful looks from them because it was only natural that I struggled to communicate. In fact, people became more helpful, more patient, more friendly, more caring, despite my reluctance and inability to communicate effectively. Strangely, this opened me up and made me feel more comfortable having a conversation with the locals. It’s ironic, but it was much easier for me (psychologically) to talk to strangers in a foreign country than in my hometown.

 

Subsequently, I made a resolution to travel every year to a country that I’ve never been before. I learned how to socialize by traveling to faraway places where even the strangest data geek isn’t seen as strange. I learned how to talk to other human beings where my peculiarity is treated as natural and not weird. I still wasn’t any good at it, because I only got to practice it about once a year. But when I did, it was a total immersion. It became something I looked forward to every year, because ultimately I am not that different after all. Like the rest of the world, I do enjoy the warmth of human connection, even though it was something very difficult for most of my life. 

 

You might not believe it, but this was me 15+ years ago. And yes! I had long hair.

 

Learning to Write

If you’ve been following my blog, it might not have occurred to you that writing was a challenge for me. My inability to express myself verbally translated directly into my writing. As a result, I was never any good at expressing complex thoughts coherently in written words. Furthermore, English wasn’t my mother tongue (in fact, it’s my third language). I felt my English was so shamefully inadequate that sometimes I wondered growing up if I was dyslexic.

 

This wasn’t all bad and it’s why I chose to focus on math and science. These subjects have their own languages, except that they are universal and based purely on logic and reason. When I was in school I probably wrote more codes, formulae, and proofs than English sentences (which is why no one understood me). However, this also gave me a strong sense for logical reasoning and factual consistency. Math and science taught me how to write logically flawless proofs and put forth indisputable mathematical arguments. But I still needed to learn how to translate them into layman’s term, so everyone could understand them.

 

Today, a large part of my work as a thought leader involves writing, even though it used to be one of my greatest fears. I’m not such a prolific writer that I can just turn my thoughts into eloquent sentences with perfect grammar. It takes me a long time to put my ideas into words, because I often think in imageries and abstract logics.

 

This blog post that you are reading now is actually the result of piecing together hundreds of short incoherent phrases much like a jigsaw puzzle. When I write, I spend more time reading and moving the words around (cutting and pasting) then actual writing. Writing is a manual optimization process for me. As I trial and error each move, I try to read and optimize for logical coherence. When there are logical gaps, I would fill them with new phrases, which may in turn be moved and optimized further. This is definitely not the most efficient way to write, but it’s how I am able to write coherently. I basically applied my strengths in logic and reason, to creating sentences, instead of solving equations. What I lacked in skill, I had to overcome with sheer hard work and perseverance.

 

Conclusion

Over the years, I have improved my speaking and writing skills. I even learned to love it. However, as one of my mentors once said, your weakness is your weakness, because you never truly master them. It’s something you work on constantly. Communications have never been my strength compared to the more analytical side of me. Although I’ve came a long way, it is something that I still have to practice all the time.

 

Today, I am fundamentally still an introvert, albeit little less extreme. If you’ve ever been in a meeting with me, you might notice that I almost never talk unless directly asked. It still stresses me out to speak up when I am in a group of more than 3 or 4 people, especially when I wasn’t explicitly given the floor to do so. It still takes me forever to write, because writing is still a manual optimization process for me. If you are a perceptive reader, you might notice this blog post is a bit rusty, since I took such a long break in blogging. It takes a lot of determination for me to get back into the writing mode.

 

As scientists and engineers aspiring to be data scientists, you probably had your fair share of logical reasoning and mathematical rigor. You can probably code up any statistical inference procedures and apply machine learning packages to any data sets you can get your hands on. So what’s stopping you from being a successful data scientist and what’s your greatest fear? Is it your communication and storytelling skills?

 

Rest assured that you that you are definitely not alone there. As with everything in life, if you can overcome the greatest fears that are holding you back, you will be one more step closer to success.

 

Want to share your story here? I’d love to hear it.

 

Related Blogs

• Where It All Started: How I Became a Data Scientist—(1) Follow the Data
• How I Became a Data Scientist—(2) Hurdles Along the Way
• How I Became a Data Scientist—(3) Do Good Research
• How I Became a Data Scientist—(4) A Eureka Moment

 

Image Credit: MarketingDistillery.com, geralt and mikekanuta0.

 

---

 

Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.

 

Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.

With technology continually evolving and changing, so does its vocabulary. The enterprise world is littered with jargon, one of the buzzwords du jour being “digital transformation (DT)” which I’m sure you’ve heard of by now. But what does it mean? It’s like Dan Ariely’s humorous comment on big data, “everyone talks about it, nobody really knows how to do it, but everyone thinks everyone else is doing it, so everyone claims they are doing it.”

 

At a high level, DT is very easy. It’s simply the adoption of digital technologies to transform your business. So just choose the digital technology you want, and use it to change how your business operates. Done!

 

Why Digital Transformation Fails

Sounds easy. But it’s not. Numerous sources report that roughly 70% (ranging from 66% to as high as 84% via Forbes) of the DT initiatives fail. Clearly, it can’t be as simple as deploying a digital technology (given such high failure rate), even though that could pose challenge in some cases.

 

So why is DT so difficult? The reason is because that a true transformation of your business requires more than just the adoption of new technology. DT usually starts with some kind of technology upgrade, but that’s only the first step. Subsequently, it requires changes in your business processes, your employee and leadership behavior, and ultimately your corporate culture. Changing technology might be easy, but changing the people, processes and culture is hard.

 

The challenge of DT is not a digital or even a technological problem; it’s a business transformation problem. If we try to understand why DT fails, the most common causes of failure boil down to the following 4 categories of reasons.

 

Technology:

1. Using outdated technologies
2. Failure to integrate with legacy or other digital systems
3. Believing that it’s only a technology problem

 

People:

4. Lack of clarity and vision
5. Lack of leadership support
6. Too much top down imposition without grass root support
7. Lack of a digitally savvy workforce

 

Process:

8. Silo effort that didn’t engage the broader stakeholders
9. Process misalignment
10. Not agile enough for faster innovation

 

Culture:

11. Short term thinking
12. Not customer centric
13. Too little cross-functional collaboration

 

Since these are failure modes, they are all important. As it only takes one broken link to break the whole chain, any one of these failure modes could undermine the success of your entire DT initiative. So every one of them must be addressed, which is a lot for businesses to undertake.

 

But here’s the bright side: Although all the common failure modes must be addressed, not all of them need to be addressed at once. And if you are embarking on the DT journey, not all of them need to be addressed at the beginning. So which ones should you focus on first?

 

Upon analyzing the natural dependency among these failure modes, there are only 3 that must be addressed from the get-go. And I will explain this with the video blog below.

 

1) Customer Centricity

A customer-centric strategy is imperative, simply because every business needs customers. Moreover, in an increasingly service-oriented subscription economy, every business is striving to retain their customers, because not only is the competition more intense, the switching cost for consumers is often minimal. While this is a given from a business standpoint, customer centricity is equally as important for your digital transformation (DT) initiative for several reasons.

 

It’s easier to rally for support when you have a customer-centric strategy, precisely because it makes business sense. Very few people would argue against serving your customers. A well thought-out customer-centric strategy could easily win both leadership and grassroot support. You still need to sell the strategy within your enterprise, but it shouldn’t be a difficult sell.

 

It’s also less challenging to create processes that are aligned across different departments with a customer-centric mindset. Traditional business processes are often created to optimize some business KPIs while meeting their operating constraints. However, different departments and teams often operate under disparate constraints and have unique set of KPIs. Consequently, their processes are typically misaligned because they were created irrespective of one another. Customer-centricity serves as the glue that binds different departments and teams together. It helps you create processes that are aligned with giving your customers a great experience.

 

When all your processes are aligned, it facilitates cross-functional collaboration. At the very least, the processes are not adding friction that could hinder collaboration. Although this doesn’t automatically drive collaboration, it certainly makes it easier when there is a business need to do so. When that happens, your DT is suddenly no longer a siloed effort.

 

Finally, a customer-centric mindset fosters long-term thinking because most businesses want to have loyal (long-term) customers, especially in a subscription economy.

 

2) A Clear Vision

Despite the simplicity of the definition, digital transformation (DT) could be confusing because it’s different for every company. Myriads of digital technologies are on the market, which can change any one of the multitude of business operation within your enterprise.

 

For example, DT for one company may be using iPads (a digital technology) to scale onboarding of new employees (a perfectly valid HR function). It could also be using social media (another digital technology) to engage and support your customers throughout their customer journey (a marketing and customer support operation). It could even be using big data (yet another class of digital technology) to predict sales, using IoT and augmented reality to improve customer experience, or anything in between.

 

DT can mean many different things, so you must have a clear vision of what DT means for your enterprise. Which digital technology are you using? And which part of your business operation are you trying to improve with these technologies initially? Most importantly, what business outcome are you trying to achieve? As alluded earlier, a customer-centric mindset could help you answer some of these questions and shape your vision.

 

Armed with a clear vision of what DT means for your business makes it even easier to garner both leadership and grassroot support. And if you are a leader, a clear vision probably means that you are bought in and committed to supporting this change.

 

3) The Right Technology

Since digital transformation (DT) almost always starts with a technology upgrade, it is important to choose the right technology at the beginning. Having a clear DT vision that is customer-centric helps you choose the digital technologies to realize your vision, but there are other factors to consider.

 

Certainly, the right technology must have all the functionality required by your specific DT project. It must meet all the security, reliability, and legal compliances for your enterprise, and must built to scale with robust technologies that last. This is unique to each business, but there are two elements that are often overlooked at the beginning which may impact the long-term success of your DT initiatives.

 

First, the right technologies should be easily integrated into with the rest of your company’s technology ecosystem. And that includes both your legacy systems and other newly adopted digital systems. Keep in mind that when you kick off a digital initiative, your core business will still be running on your legacy system. Failing to integrate with these systems means your DT project will remain a siloed effort. While DT initiatives often start small in one area of the company, it must permeate throughout your enterprise to achieve lasting transformation.

 

Second, the right technologies should be simple and intuitive to use. It should be so intuitive that even your non-digital workforce should be able to pick it up and immediately carry out rudimentary functions without much training. Of course, training and education will always be required to reach proficiency.

 

The key is to make sure that the learning curve does not offset the efficiency gain from the use of your new digital technology for the “digital novice,” even at the very beginning. Furthermore, when there is residual efficiency gain, even during the adoption phase of your DT project, innovative minds within your enterprise will have the cognitive surplus to innovate and be more agile.

 

Transformation means lasting change

Digital transformation is a journey. It always starts with the adoption of digital technologies, but it must also change the people, process and the culture to be truly transformative. It typically begins as a siloed technology project, but must permeate throughout your enterprise. Although digital transformation can seem difficult, concentrating on the above focuses at the very start will help pave the road for long-term success.

 

*This article originally appeared on CMSWire.

*Image Credit: Pexels and tpsdave.

 

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Michael Wu, Ph.D. is Lithium's Chief Scientist. His research includes: deriving insights from big data, understanding the behavioral economics of gamification, engaging + finding true social media influencers, developing predictive + actionable social analytics algorithms, social CRM, and using cyber anthropology + social network analysis to unravel the collective dynamics of communities + social networks.

 

Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics + its application to Social CRM. He's a blogger on Lithosphere, and you can follow him @mich8elwu or Google+.

Yesterday I was riding along Bloor when I came up behind a rider wearing a bright yellow jacket. It turns out it was one of the new Proviz Reflect 360 CRS jackets, a new version of my jacket, with the “CRS” indicating that it was a bright colour during the day, while still being retroreflective at night. In comparison, my jacket is a dull grey.

I had heard about this new model since I got an email from the vendor offering at an introductory price about six months ago. I didn’t go for it since:

• my jacket was still going strong
• a little correspondence with the vendor revealed that it is less breathable than my Reflect 360+, while still being a little better than the older 360 model.

The rider said that he had the jacket for a couple of months, and he pointed me to this video that shows that the CRS is a bit less reflective than mine. Still, the daytime visibility was definitely a nice feature.

My jacket is a little over a year old. I’ve worn it quite a bit, and it is holding up well. The only place where the reflective coating has worn off is in the center of the collar.

This might have something to do with the fact that I’m not very careful when I hang up the jacket. If you always use the loop on the inside of the collar, you might avoid this issue.

The jacket still attracts a lot of attention. More than once in the dead of night, I’ve had drivers slow down, roll down their windows, and ask about the jacket!

I see from the website that the vendor appears to be clearing out the 360+ model in favour of the CRS model.

Might be a good time to pick one up.

 

I took some photos yesterday between Nanton and 41st. I didn’t get around to putting them on the blog yesterday – but maybe you already follow me on Instagram or Flickr – in which case you need read no further.

A new crossing sign has appeared together with much paint on the road where the Arbutus Greenway crosses Nanton. While the elements used in the sign are standard the combination is not actually shown in the Uniform Manual of Traffic Control Devices. (But I have now seen it also used at the Highway #1 on ramp at Main Street, North Vancouver southbound to the Ironworkers’ Memorial Second Narrows Bridge.)

In general the Greenway street crossings are anything but uniform or standard, and many (not this one) have railway signalling equipment and crossbucks still in place.

One of my Instagram contacts commented

I believe it is telling you it’s okay to stand on your bike while jumping a snow fence. But I could be wrong.

This house and its delightful surrounding garden seems to me to worthy of consideration for preservation.

The city defines a “character home” as a structure built before 1940 that meets “established criteria for integrity and character of original features”. In addition, character homes are not listed on the Vancouver Heritage Register.

Georgia Straight

New access stairs near 35th Avenue

Former signalling gear – used to trigger the crossing bells and wig-wags – are still in place. I am a bit surprised that the metal thieves have not scavenged all the copper from this box.

Filed under: Arbutus Greenway

The case for doing something about traffic congestion on both sides of the Massey Tunnel at peak periods is very strong. No-one would dispute that. What is in dispute is why that congestion occurs and what can be done about it.

This week the well known left wing cabal at the West Vancouver City Council reported that “Buses only 2% of vehicles that cross the Lions Gate Bridge but carry 25% of the people.” Actually the data came from Translink and is five years out of date but the principle holds. Single occupant vehicles are dreadfully wasteful of road space and are the cause of traffic congestion. Trying to get people to share their vehicles – there are usually at least three empty seats – has not been a huge success. If you could get everybody else to use the bus, then you would have lots of space to drive on – until all the other drivers caught on.

We know that widening roads and building ever wider bridges is a temporary fix at best. Actually what will happen if you cure the bottleneck at one point is that you simply shift it somewhere else. That is why no extra lanes were added to the Lions’ Gate Bridge  – and why a multi-lane expansion of Highway 99 across the Fraser won’t do very much either.

But in the case of the Massey Tunnel it is NOT all about traffic congestion. The Port wants to dredge a deeper channel to allow for bigger ships up the river. The tunnel is an obstacle to that ambition. The port also controls access to its operations – and has a policy of making congestion at the tunnel worse in order to promote its campaign for removal. Now that is an assertion that I am not able to back up with direct evidence. I have no way of eavesdropping on the conversations between board members. But John Berktyo – of Fraser Voices – has been doing some digging and this is what he found

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Deltaport runs the business is a separate corporate entity from Port of Vancouver, which owns the land.  Deltaport is owned by DP World, headquartered in Dubai. It employs 37,000 people worldwide. Sultan Ahmed Bin Sulayem became Chairman of DP World on 30 May 2007. He is a citizen of the United Arab Emirates. Their Board of Directors is shown here: http://web.dpworld.com/about-dp-world/board-of-directors/ .

Their professed commitment to the environment and sustainability contacts are shown here: http://web.dpworld.com/sustainability/sustainability-contacts-and-policies/.

The Deltaport web site is here: http://globalterminalscanada.com/

Their hours of operation are here:  http://globalterminalscanada.com/gct-deltaport/(half way down page)

The hours are divided into  “day gate” and “night gate” as shown here on their website:

Filed under: Transportation

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