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A mossy viz to try at home

Visualization purists beware. This “viz” is mossy — as in moss.

At first glance, it’s a visualization. It represents parks, forests, rivers, and lakes in Berlin, Germany. But then it invites you to touch it, and the map nudges you closer to the real thing.

The mossy-viz idea seems to have begun when Sebastian Meier sought a more sensual experience than he could give with an ordinary map. Meier, who works at at the Interaction Design Lab, part of the University of Applied Sciences in Potsdam, Germany, begins his explanation this way.

Most visualizations use the two-dimensional plane of paper or screens, even when visualizing spatial (three-dimensional) data. Green Berlin explores the opportunity space of tangible artefacts, created with rapid prototyping techniques, in this case a laser cutter. Since humans are multi-sensory beings, the physical, the haptic world gives us a certain sensation we cannot deny.

To make a mossy map, you need data (Meier got his from OpenStreetMap), a laser cutter, Photoshop, and a few other things.

Read more here. Instructions here.

Extra credit question: What’s the term for a map that’s more than visual? Should we call it a “viz,” the term that Tableau marketing people popularized years ago?

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Dumbstruck city: big, brittle, not so smart

A city that seems smart one moment can look like the dumbest thing alive the next moment. All it takes is one little cloud outage.

We’ve taken cloud outages in stride. After all, how often does it really matter if you place an Amazon order now or in an hour or two? But what if the cloud locks or unlocks your front door?

Fainting ladies

I’ve been reading up on “smart cities.” Anthony Townsend, in his article “Smart Cities: Buggy and Brittle” — from 2013 and still fresh — says yes, worry. Start with the cellular networks.

Cellular networks … are the fainting ladies of the network world — when the heat is on, they’re the first to go down and make the biggest fuss as they do so.

All networks are vulnerable, and outages have ever higher stakes.

Cloud-computing outages could turn smart cities into zombies. Biometric authentication, for instance, which senses our unique physical characteristics to identify individuals, will increasingly determine our rights and privileges as we move through the city — granting physical access to buildings and rooms, personalizing environments, and enabling digital services and content.

But biometric authentication is a complex task that will demand access to remote data and computation. The keyless entry system at your office might send a scan of your retina to a remote data center to match against your personnel record before admitting you. Continuous authentication, a technique that uses always-on biometrics — your appearance, gestures, or typing style — will constantly verify your identity, potentially eliminating the need for passwords. Such systems will rely heavily on cloud computing, and will break down when it does.

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Why “smart cities” is interesting, first look

At first, the term “smart cities” may sound like just another bit of fluff — another one of the data industry’s fascinations. It did to me. But I’ve been reading about it, and I’ve come to think that the reality may hold real benefits for the data industry — more than just a new market for bright, shiny products.

Definitions vary, of course, but the term seems to boil down to this: It’s the use of data from the Internet of Things and other sources to squeeze more use, more security, and even more pleasure from city facilities, utilities, roads, transit, and other public resources. City administrators can spend less, for example, to make data guide drivers around congestion than to build a new lane.

You might say, well, that’s just the old dream: data analysis fixes everything! Yeah, we’ve heard that one before. But I think this is different when you try this in cities instead of businesses.

Cities and businesses both know how to hide things. But they’re different. Business executives can pretend everything’s humming along like air conditioning blowing cool air on cool heads. City officials, meanwhile, fear the hot heads, the activists, the ever growing and ever-smarter legions of data crunchers.

In cities, smell and dysfunction is in everyone’s face and nose. If it’s impossible to drive across San Francisco at four on a weekday afternoon, you know it— and people learn to assume there’s data on it somewhere. They look in the data mirror to examine it. Is it as bad as it felt? How long did it last? What caused it, and what’s being done about it? The data mirror becomes part of life. You just step in it.

That’s a fine dream, of course. But users of restaurant ratings and other attempts at quasi-public data know that such visions don’t always come true. Who hasn’t relied on public raves for restaurants or movies to find they were fooled?

Even so, I suspect that the public nature of smart-city data will give a nudge to common data pathologies. If dysfunction is in your ears and nose, smart community organizers have a strong lever on reluctant officials. Hey guys, break down the silos. Where’s the data you promised? Hey, your data stinks.

Smart cities in full flower can do even more than offer efficiency and safety. They can also make people feel good. That might be the most interesting benefit of all.

Daniele Quercia, for example, offers an “alternate agenda.” He advocates, among other things, letting data point to slightly-less-than-efficient paths between A and B that are more fun, more beautiful, or more interesting. (See his crowdsourced “happy maps” and “smelly maps.”) Who wants to live in bare, cold efficiency? Not successful people, many of whom have a good pick of places to live and work.

Imagine: data that makes you feel good.

I’ve just started looking at smart cities. I’m probably naive about some things. But so far, I think smart cities is worth attention.

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A mossy viz to try at home

Visualization purists will reject this. It’s mossy — as in moss.

At first glance, it’s a visualization. It represents parks, forests, rivers, and lakes in Berlin, Germany. On further examination, you want to touch it.

The mossy-viz idea seems to have begun when Sebastian Meier sought a more sensual experience than he could give with an ordinary map. Meier, who works at at the Interaction Design Lab, part of the University of Applied Sciences in Potsdam, Germany, begins his explanation this way.

Most visualizations use the two-dimensional plane of paper or screens, even when visualizing spatial (three-dimensional) data. Green Berlin explores the opportunity space of tangible artefacts, created with rapid prototyping techniques, in this case a laser cutter. Since humans are multi-sensory beings, the physical, the haptic world gives us a certain sensation we cannot deny.

To make a mossy map, you need data (Meier got his from OpenStreetMap), a laser cutter, Photoshop, and a few other things. (Instructions here.)

Read the rest here.

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Free-the-data movement meets privacy

Back when data was little and simple, self-service analysis advocates started the chant, “Free the data!” IT stood in the way, they said. Fast forward to 2016: “democratized data” has become common, but so has public concern over privacy.

That nettlesome struggle drove a discussion that now stands as the data industry’s’ most important discussion of 2016. Around the conference table at last summer’s Pacific Northwest BI Summit — the annual, invitation-only confab held in Grants Pass, Oregon — two dozen data leaders pondered the issue for almost two hours. They concluded with an idea that broke open industry assumptions.

UK-based consultant Mike Ferguson told of a meeting held on continental Europe. He expected the usual usual stakeholders, such as from marketing and HR. But alongside them were two staff from the corporate counsel’s office. The lawyers made it clear that anything decided there would need their approval.

Their worry? Compliance with the European Union’s impending data privacy law, the General Data Protection Regulation. When it takes effect in 2018, privacy violations — including failure to erase individuals’ online presence on request — could amount to 4 percent of global revenue. Other regions will likely comply, eager to ensure continuing access to the EU market and to ensure access to EU data. Across the globe, the old democratization-versus-privacy is just about to grow some big, sharp teeth.

It poses a dilemma. Everyday business now requires ready access to data. Even compliance with new privacy regulations requires access even as the regulations seek to limit it.

At the problem’s root, says Ferguson, is data integration. Multiple platforms and tools have evolved to serve big data’s proliferating, specific workloads. Streaming data, Hadoop, the enterprise data warehouse, NoSQL and others chug away, each one possibly processing another platform’s data. And all that data keeps coming in faster and faster.

Data integration’s too expensive

“What I hear from clients,” said Ferguson who is managing director of Manchester-based Intelligent Business Strategies, “is that the cost of data integration is way too high.” Skills are spread across lots of tools, everything gets re-invented continually, metadata is fractured or lost entirely as it runs through multiple tools, and there’s just too much repetition all around. Data integration among platforms seems to become more complex all the time.

Self-service data integration is cheap. Many in IT like it. But, says Ferguson, it quickly results in “a kind of Wild West.” Data moves uncontrollably, with no one guarding the sources. Users apply countless tools for data prep, ETL, data integration, and other functions, and silos proliferate.

“There’s got to be better way,” said Ferguson. He suggests supplanting the “data lake” with what he calls a “data reservoir”: a governed[ replaced “organized”; obviously it’s organized. “Governed” comes from the following slide.] collection of raw, in-progress and trusted data that incorporates multiple stores and streams. The “reservoir” would define data once to run anywhere and supply info fast.

“The smart thing is to offer virtual views, Amazon-like,” said Ferguson. Instead of copying the data, it would be offered in virtualized form, ready to use but not copied. Data’s “Wild West” would be tamed with riding stables: Ride a trusted horse on a known trail.

Local policies could be applied as the data’s dispensed. Users with proper rights would see the data. But not those without rights would be told, “Sorry, Dude, you can’t see it. Wrong jurisdiction!” 

Urgency

Underscoring the urgency of controlling data, vice president of marketing at IBM Harriet Fryman told about a crashed drone on her roof and an unsettling tweet. The tweet read, “I think my drone is on your roof. Can I have it back?” Fryman went to her roof and, sure enough, there was a crashed drone. As the owner explained later, his drone was equipped to send one last photo home before it crashed. From that image, he matched the visible roofline with a Google Maps satellite view, and from there he followed a circuitous path to Fryman’s Twitter account.

Meanwhile, explained SAS vice president of best practices Jill Dyché, executives are fed up waiting for a solution to the problem Ferguson described. Dyché has observed “an utter lack of confidence” among executives in the ability of organizations to govern data.

Donald Farmer, principal at TreeHive Strategy, raised another problem. “It’s incredibly difficult to prove something’s been deleted,” especially when the data’s already been propagated. “How do you track it back?”

Solutions

The typically voluble group went quiet for a moment, attesting to the challenge.

A surprising suggestion came from Donald Farmer, principal at TreeHive Strategy: The solution may be organizational, he said, not technological. The risk of violating privacy laws could be minimized if companies isolated risk with spinoffs. The mother company would grow as far as it could with the current technology, governance, and practices. Then it would spin off a subsidiary that would own the risky data along with the liability. Eventual innovations would transfer homeward, abandoning the risk with the spinoff’s shell.

Merv Adrian, however, disagreed. “I don’t believe that for a minute,” he said. “They’ll find a way around it,” he said. Later, he wrote to me in email, “Companies don’t do spinouts lightly. It’s disruptive, complex and costly.” The incentive would have to be strong.

Farmer had a second, even more intriguing idea: “One of the myths is that we need more information,” said Farmer. If we think again about the data we use and why we use it, he explained, we might find just about the same value with bayesian noise added. Data can be slightly wrong, with enough noise inserted to prevent hacks, and still have equal benefit to business users.

That is, the data doesn’t have to be right, just slightly wrong — at first glance an outlandish idea. It invited quips, perhaps a natural response to an implicit admission that technology may not be the answer. But who can even hope that until-now unknown difficulties, founded on a new world of unheard of complexity and an aroused public, could be solved with technology alone? Farmer’s idea, or something like it, may prove itself yet.

These are hard problems,” observed Robert Eve, director of product management, data and analytics software at Cisco Systems, with one last quip before lunch. With a colloquialism denoting the need for deep thought and newfound finesse, he added, “At run time, you have to understand the kung fu.”

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