Figuring out what to read (and what to believe) about Big Data is becoming a Big Data problem in and of itself1. I wrote The hype, benefits, and dangers of Big Data a while ago to give an overview of what’s out there, but there are two more interesting articles from the last week that I’d like to highlight as well.

First, on the HBR blog Jake Porway talks about Big Data and social entrepreneurship and makes the point that You Can’t Just Hack Your Way to Social Change:

Any data scientist worth their salary will tell you that you should start with a question, NOT the data. Unfortunately, data hackathons often lack clear problem definitions. Most companies think that if you can just get hackers, pizza, and data together in a room, magic will happen. This is the same as if Habitat for Humanity gathered its volunteers around a pile of wood and said, “Have at it!” By the end of the day you’d be left with half of a sunroom with 14 outlets in it.

And on Wired, Does ‘Big Data’ Mean the Demise of the Expert — And Intuition? is a very interesting excerpt from Viktor Mayer-Schönberger and Kenneth Cukier’s new book on the topic:

In the same spirit, the biggest impact of big data will be that data-driven decisions are poised to augment or overrule human judgment.

The subject-area expert, the substantive specialist, will lose some of his or her luster compared with the statistician and data analyst, who are unfettered by the old ways of doing things and let the data speak. This new cadre will rely on correlations without prejudgments and prejudice. To be sure, subject-area experts won’t die out, but their supremacy will ebb. From now on, they must share the podium with the big-data geeks, just as princely causation must share the limelight with humble correlation.

It seems like an obvious conclusion, but everything I’ve read so far about Big Data confirms that if we think cutting the “messiness” of human decision-making out of data analysis will result in better decisions, we’re sorely mistaken.

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1. Sorry, I didn’t get much sleep last night, so even though I know this isn’t a particularly funny joke, I just can’t help myself. ↩

In Students to e-textbooks: no thanks Nicholas Carr reflects on a recent study (PDF link) out of Ryerson University in Toronto which shows that students still prefer paper textbooks over electronic textbooks:

What’s most revealing about this study is that, like earlier research, it suggests that students’ preference for printed textbooks reflects the real pedagogical advantages they experience in using the format: fewer distractions, deeper engagement, better comprehension and retention, and greater flexibility to accommodating idiosyncratic study habits. Electronic textbooks will certainly get better, and will certainly have advantages of their own, but they won’t replicate the particular advantages inherent to the tangible form of the printed book.

What makes this interesting is that it’s not the usual “I want to smell the pages” argument we see in most stories about the yearning for paper books. This study shows that paper textbooks help students learn better. That’s not to say that electronic textbooks won’t eventually catch up — they will — but it’s a reminder that in some spaces, e-books still have a long way to go.

A Readlist of all the articles referenced in this post is available here. Readlists allow you to send all the articles to your Kindle, read them on your iOS device, or download it as an e-book.

Despite the overly alarmist title, Andrew Leonard’s How Netflix is turning viewers into puppets1 is a fascinating article on how Netflix uses Big Data in their programming decisions:

“House of Cards” is one of the first major test cases of this Big Data-driven creative strategy. For almost a year, Netflix executives have told us that their detailed knowledge of Netflix subscriber viewing preferences clinched their decision to license a remake of the popular and critically well regarded 1990 BBC miniseries. Netflix’s data indicated that the same subscribers who loved the original BBC production also gobbled down movies starring Kevin Spacey or directed by David Fincher. Therefore, concluded Netflix executives, a remake of the BBC drama with Spacey and Fincher attached was a no-brainer, to the point that the company committed $100 million for two 13-episode seasons.

The article also asks what this approach means for the creative process, something I’ve written about before in The unnecessary fear of digital perfection, so I won’t rehash that argument here.

What’s interesting to me about the rise in Big Data approaches to decision-making is the high levels of inaccuracy inherent to the analysis process. Of course, this is something we don’t hear about often, but Nassim N. Taleb recently wrote a great opinion piece about it for Wired called Beware the Big Errors of ‘Big Data’, in which he states:

Big-data researchers have the option to stop doing their research once they have the right result. In options language: The researcher gets the “upside” and truth gets the “downside.” It makes him antifragile, that is, capable of benefiting from complexity and uncertainty — and at the expense of others.

But beyond that, big data means anyone can find fake statistical relationships, since the spurious rises to the surface. This is because in large data sets, large deviations are vastly more attributable to variance (or noise) than to information (or signal). It’s a property of sampling: In real life there is no cherry-picking, but on the researcher’s computer, there is. Large deviations are likely to be bogus.

He gets into more detail on the statistical problems with Big Data in the article, and his book Antifragile looks really interesting too.

Since I haven’t written about Big Data before, I also want to reference a few articles on the topic that I enjoyed. Sean Madden gives some interesting real world examples in How Companies Like Amazon Use Big Data To Make You Love Them2. But over on the skeptical side, Stephen Few argues in Big Data, Big Deal that “interest in big data today is a direct result of vendor marketing; it didn’t emerge naturally from the needs of users.” He also makes the point that data has always been big, and that by focusing on the “bigness” of it, we’re missing the point:

A little more and a little faster have always been on our wish list. While information technology has struggled to catch up, mostly by pumping itself up with steroids, it has lost sight of the objective: to better understand the world—at least one’s little part of it (e.g., one’s business)—so we can make it better. Our current fascination with big data has us looking for better steroids to increase our brawn rather than better skills to develop our brains. In the world of analytics, brawn will only get us so far; it is better thinking that will open the door to greater insight.

Alan Mitchell makes a similar point in Big Data, Big Dead End, a case for what he calls Small Data:

But if we look at the really big value gap faced by society nowadays, it’s not the ability to crunch together vast amounts of data, but quite the opposite. It’s the challenge of information logistics: of how to get exactly the right information to, and from, the right people in the right formats at the right time. This is about Very Small Data: discarding or leaving aside the 99.99% of information I don’t need right now so that I can use the 0.01% of information that I do need as quickly and efficiently as possible.

What I think we should take from all of this is that our ability to collect vast amounts of data comes with enormous predictive and analytical upside. But we’d be foolish to think that it makes decision-making easier. Because Big Data does not take away the biggest challenge of data analysis: figuring how to turn data into information, and information into knowledge. In fact, Big Data makes this harder. To quote Nassim again:

I am not saying here that there is no information in big data. There is plenty of information. The problem — the central issue — is that the needle comes in an increasingly larger haystack.

In other words: proceed with caution.

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1. Link via @mobivangelist ↩

2. It’s interesting that the phrasing of both this headline and the Netflix one implies that companies are using Big Data to persuade us to do things against our will. But I can’t figure out if that’s a real fear, or just clever linkbait. ↩

Mark Boulton argues that we need to think further than breakpoints in responsive design, and also spend time figuring out the “optimization points”. From The In-Between:

I think we’re missing a trick for using breakpoints to make lots of subtle design optimisations. […] Content-out design means defining your underpinning design structure from your content, and then focusing on what happens in between ‘layouts’. This approach of optimising your design by adding media queries (I like to call these optimisation points rather than break points, because nothing is broken without them, just better), means you are always looking at your content as you’re working. You become more aware of the micro-details of how the content behaves in a fluid context because your focus shifts from controlling the design in the form of pages, to one of guiding the design between pages.

He shares some examples and also links to more resources on how to accomplish this. One good example of this subtle optimization approach is Responsive Typography, a concept by Marko Dugonjić where the size of the typography displayed on the screen is based on the viewing distance of the reader, calculated via webcam.

I’ve been seeing more and more scams like this one in my Facebook News Feed:

You only have to think about it for 4 seconds to realize that making a comment on a photo on the web will result in you watching and seeing absolutely diddly-squat (“P.S.: This is not Insane after all!”). And yet, in this particular case, 259,304 people thought about it for 3 seconds or less, commented, waited and saw nothing, and then moved on to the next thing.

The question is, why do Page admins do this? What’s the use of tricking people into commenting on photos, especially when they’ll realize right away that they’ve been made to look like a fool? Well, because there’s money in it, of course.

This is a pretty transparent scam to beat Facebook’s EdgeRank system — the algorithm that Facebook uses to determine what articles should be displayed in a user’s News Feed. When someone comments on a picture it makes it more likely that the picture will show up in their friends’ News Feeds, so it’s an easy way for a Page to gain more exposure very quickly.

Once these Pages have built up hundreds of thousands of “Likes” using the scam, they usually do one of two things. They either start punting things they want to sell, or they sell the Page itself to a business that changes some of the details and uses it as their instantly enormously popular brand Page.

This is obviously pretty dirty, and also nothing new — we’ve had black hat SEO and dark patterns since the dawn of the web. But what I can never understand about the people who use these tactics is why they don’t long for the satisfaction and personal growth that comes from doing real work and reaping the rewards of that. Why create a community of people who couldn’t figure out that you’re scamming them, as opposed to a real community that values your company and what you do? I’ve written about this before in my defense of doing things the hard way:

When we do things the hard way, we invest in ourselves in the best possible way. We kick off an endless cycle of learning and mastery that helps us grow and lead fulfilling lives of purpose. When we take shortcuts, we become mere pretenders. We learn how to play the part, but there is no substance or continued growth. The instant gratification makes us build the house of cards ever higher, which brings anxiety about the whole thing coming tumbling down. Why would we shortchange ourselves like that?

So what can we do about these scams? Well, for one, obviously don’t comment on it. But I also recommend clicking on the little arrow on the right and hiding the post. That will tell EdgeRank that the person who commented on the photo is not worth paying attention to, so in time you’ll see less and less of those kinds of posts. Who says we can’t all be EdgeRank Optimization specialists?

Francisco Inchauste did a great interview with MIT Technology Review about the user experience challenges of gestural interfaces. From Does Gestural Computing Break Fitts’ Law?:

I think there are a lot of usability/UX rules and laws that will come into question as we move forward into more of these experimental kinds of interfaces. I know many of them already have been retested/validated by other researchers.

A lot of newer interaction paradigms aren’t naturally intuitive as we like to think. Tapping and swiping at “pictures under glass” (or in this case, content) is always going to be a learned thing, like when we were introduced to the desktop metaphor or icons.

I think we’re in a period of generational transition when it comes to fully gestural interfaces1. Despite living on the Internet, I still struggle to remember some of the newer gestures that are popping up in iOS apps. On the other hand, my 3½-year old daughter has zero problems figuring out (and remembering) gestures, because this is the world she’s growing up in. There is no major shift in mental model needed — to her, this is just how technology works. It reminds me of something Chuck Skoda said a while ago in The touchscreens are coming:

While I fully expect the future to have keyboards and mice (or some precision pointing device), touch is already precluding the ubiquity of both in the minds of children. When the upcoming generation is running the show, we will find another absurd idea, that a computer built for human interaction will have a screen that doesn’t respond to touch.

And when that generational transition is complete, what we once thought of as “newer interaction paradigms” will simply be “the way things are”.2

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1. By the way, check out Rise, a fantastic, fully gestural alarm clock app by Francisco and the team at Simplebots. ↩

2. I think I deserve a special Internet high five for not making a “the future is already here…” reference here. ↩

It’s a somewhat uneven article, but Dave Wiskus makes some good points in The secret of Apple’s design success: the humane interface. I did get a bit uneasy when I got to this part:

Where Apple differs from its competition isn’t in aesthetic beauty, it’s in the company’s ability and willingness to make decisions on behalf of its users. […] Apple’s take is to remove complexity and make choices long before the user sees the product.

It’s an argument that’s often used by Apple fans to defend the “Apple prison” accusations — I’ve used this line of reasoning myself. But here’s the thing — and I’m saying this as a die-hard Apple fan — even though this used to be true when the iPhone and iOS first came out, I don’t think our argument holds water any more. We’re starting to see more complexity sneaking into iOS and Mac OS X, and even though the veneer of simplicity is still highly visible, there be trouble beneath the surface.

And then I read Federico Viticci’s excellent interview with John Siracusa, in which John sums up the situation perfectly:

Simplicity is great, as iOS has shown. But there’s a difference between conceptual simplicity and visual simplicity. Just hiding controls does make things appear simpler, but it doesn’t actually make them any simpler. The complexity is now just hidden. Similarly, removing features that few people use is a good idea, but like any good idea, it can be taken too far. At a certain point, you’re just making your application worse for everyone, even new users.

You can’t always tweak or refactor an existing application into the beautiful thing you’re envisioning. Sometimes the only way to achieve true simplicity is to start over with a new concept for the whole app.

The first iterations of iOS and Mac OS X were great because they did just that — they started over with a new concept. But complexity creep is inevitable, and the big challenge for Apple now is how they’re going to manage that. Jonathan Ive’s influence will certainly help, but perhaps there’s even a case to be made for (gasp!) borrowing a page from Google’s playbook.

I desperately wanted to dismiss Sherry Turkle’s answer to the question What should we be worried about? as alarmist, but she makes a terribly convincing argument about the impact that technology has on kids and their development. After discussing the issues in detail, she concludes:

Thus my worry for kindergarten-tech: the shiny objects of the digital world encourage a sensibility of constant connection, constant distraction, and never-aloneness. And if you give them to the youngest children, they will encourage that sensibility from the earliest days. This is a way of thinking that goes counter to what we currently believe is good for children: a capacity for independent play, the importance of cultivating the imagination, essentially, developing a love of solitude because it will nurture creativity and relationship.

The essay echoes Nicholas Carr’s thoughts:

We don’t like being bored because boredom is the absence of engaging stimulus, but boredom is valuable because it requires us to fill that absence out of our own resources, which is process of discovery, of doors opening. The pain of boredom is a spur to action, but because it’s pain we’re happy to avoid it. Gadgetry means never having to feel that pain, or that spur. The web expands to fill all boredom. That’s dangerous for everyone, but particularly so for kids, who, without boredom’s spur, may never discover what in themselves or in their surroundings is most deeply engaging to them.

But perhaps Stephen Hacket said it best — and most succinctly — in Why I Don’t Play Games on my iPhone:

Boredom isn’t a bad thing. But strangling it with Angry Birds probably is.

Preston de Guise wrote a very interesting post on how Sci-Fi interfaces have changed over the years. He concludes as follows in The changing face of computers on screen:

The shift was profound yet entirely subtle, something that a lot of people wouldn’t have really noticed at all – we shifted from portraying computer hardware to portraying computer software. […]

At some point, fiction and the future aligned, and the way in which computers were presented changed to being all about the interface – the software. This was of course just holding up a mirror to society in general: since computers have been around, their usage model has been undergoing a significantly powerful evolution from being a specific tool to being a general purpose piece of equipment; the logical continuance from a “piece of equipment” is an appliance, and that’s the era we’re starting to straddle into now, thanks in no small part to interfaces such as iOS.

Preston includes some great movie screenshots to make his case, so it’s definitely worth reading the whole article. For more, check out the collection of movie UIs in Ridiculous User Interfaces In Film, and the Man Who Designs Them. And here’s a highlight reel of Mark Coleran’s UI work in various movies:

Coleran Reel 2008.06 HD from Mark Coleran on Vimeo.

For bonus points (and if you don’t mind random pagination and small white text on a grey background), check out the Top 10 Worst Portrayals of Technology in Film.

Andy Budd’s most recent contribution to The Pastry Box Project got quite a bit of traction yesterday. This part, in particular, seems to have struck a chord in our corner of the Internet:

Good design takes time—more time than most of us are allowed. […] Sadly we see too many potentially amazing designers stuck by the glass ceiling of time. So they settle on the first solution that looks viable and are never allowed to sweat the details. They are forced to rely on 1% of inspiration without the benefit of perspiration.

So this is the dirty little secret in our industry. The best designers and developers rarely have more talent. They simply have more time.

This rings true, but I’d like to expand on that and say that it’s not just a problem in our industry. Things have become very, very fast all around us, and our impatience has reached remarkable levels. We pirate movies because we can’t wait 1 minute for the anti-piracy warnings on DVDs to play through (oh, the irony). We microwave pop tarts for 3 seconds because we can’t wait for them to finish toasting. Brian Regan has a pretty funny standup bit about this (the microwave thing starts at 2:35):

Frank Partnoy sums up the consequences of our addiction to speed very well in Wait: The Art and Science of Delay:

The essence of my case is this: given the fast pace of modern life, most of us tend to react too quickly. We don’t, or can’t, take enough time to think about the increasingly complex timing challenges we face. Technology surrounds us, speeding us up. We feel its crush every day, both at work and at home.

Yet the best time managers are comfortable pausing for as long as necessary before they act, even in the face of the most pressing decisions. Some seem to slow down time. For good decision-makers, time is more flexible than a metronome or atomic clock. As we will see over and over, in most situations we should take more time than we do.

We should take more time than we do, yes. But we don’t. Because business doesn’t work that way. Technology doesn’t work that way. And, most of all, release schedules don’t work that way.

We all know the saying Fast, good, and cheap — pick two. We live in an environment where everything has to be “fast”, so we’re inevitably left with choosing between “good” or “cheap”. And guess which one we end up having to choose most of the time…