When I wrote The hype, benefits, and dangers of Big Data a few months ago I thought it would be my only post about Big Data, and then I’d move on. But 2013 appears to be the year of Big Data, so you can’t turn a corner on the web without bumping into an article about it. Looking at Google Trends, it’s clear that interest is at an all-time high:

So I wanted to point out just a few more articles that range from calling for a more tempered approach to Big Data to an all-out assault on its value and validity. Let’s start with the juicy one…

In A More Thoughtful but No More Convincing View of Big Data Stephen Few reviews the book Big Data: A Revolution That Will Transform How We Live, Work, and Think and uses it as a way to articulate his distaste with the whole thing:

Data exists in a potentially infinite supply. Given this fact, wouldn’t it be wise to determine with great care what we collect, store, retain, and mine for value? To the extent that more people are turning to data for help these days, learning to depend on evidence rather than intuition alone to inform their decisions, should we accept the Big Data campaign as helpful? We can turn people on to data without claiming that something miraculous has changed in the data landscape over the last few years. […]

As data continues to increase in volume, velocity, and variety as it has since the advent of the computer, its potential for wise use increases as well, but only if we refine our ability to separate the signals from the noise. More does not trump better. Without the right data and skills, more will only bury us.

It’s a long article, but very detailed and highly recommended as a well-reasoned counter-argument to the Big Data movement. Others are a little more pragmatic, suggesting that we improve on the promise of Big Data rather than destroy it. In Coffee & Empathy: Why data without a soul is meaningless¹ Om Malik states:

What will it take to build emotive-and-empathic data experiences? Less data science and more data art — which, in other words, means that data wranglers have to develop correlations between data much like the human brain finds context. It is actually not about building the fanciest machine, but instead about the ability to ask the human questions. It is not about just being data informed, but being data aware and data intelligent.

It’s important to take this further and say the soul Om talks about needs to come from qualitative methods like ethnography. That’s why I like Dave McColgin’s point in his article How Will Big Data Change Design Research?:

In our field of designing products and experiences, the ‘why’ stays at the center of our process and creativity. Many designers work mostly on new products and services for which there may not yet be reliable data available. […] While Big Data can inform designers on how to improve once they put something out there, it is design research that provides principled guidance towards good solutions all along the way. Big Data can’t help us do that right now.

Tricia Wang’s Big Data Needs Thick Data is another excellent plea for ethnographers to get involved in the Big Data movement, to produce what she calls “Thick Data”:

Big Data produces so much information that it needs something more to bridge and/or reveal knowledge gaps. That’s why ethnographic work holds such enormous value in the era of Big Data. […]

Big Data reveals insights with a particular range of data points, while Thick Data reveals the social context of and connections between data points. Big Data delivers numbers; thick data delivers stories. Big data relies on machine learning; thick data relies on human learning.

And finally, Martin U. Müller and Marcel Rosenbach look at some of the scarier implications of Big Data in Living by the Numbers: Big Data Knows What Your Future Holds:

Is it truly desirable for cultural assets like TV series or music albums to be tailored to our predicted tastes by means of data-driven analyses? What happens to creativity, intuition and the element of surprise in this totally calculated world?

Internet philosopher Evgeny Morozov warns of an impending “tyranny of algorithms” and is fundamentally critical of the ideology behind many current Big Data applications. Morozov argues that because formulas are increasingly being used in finance and, as in the case of Predictive Policing, in police work, they should be regularly reviewed by independent, qualified auditors — if only to prevent discrimination and abuses of power.

I personally think there is the same value in data that there has always been, and that the Big Data movement isn’t so much about the size of the data sets, but the ability to extract more of that inherent value (signal) from the noise. But an algorithm will only take you so far. As always, knowing what and how much is not very useful without knowing why. And Big Data will never be able to tell us why…

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Charles Miller starts his post On Google Glass with a story about the history of cell phones, and then makes a keen observation:

I’m pretty bad at predicting the success or failure of new technologies, but I just think it’s a little too early to write off something as potentially game-changing as Google Glass based on how it looks today, what it costs today, or based on the fact that we’re currently entrusting one of society’s most socially tone-deaf groups (nerds) with the question of when it’s appropriate to wear them.

My mom was one of the first people in our neighborhood to have a cell phone, but she was so embarrassed by the thing that she ran into a bathroom every time she received a call. So, yes, Google Glass sounds pretty creepy (now), and they look pretty silly (now), but it might not be like that 2 years from now.

Simon Kuper wrote a very interesting counter-argument to the common assertion that social media is making us a post-literate society. From his piece How social media improved writing:

Before the internet, only professional writers wrote. […] Email kicked off an unprecedented expansion in writing. We’re now in the most literate age in history. I remember in 2003 asking someone, “What’s a blog?” By 2006, the analysis firm NM Incite had identified 36 million blogs worldwide; five years later, there were 173 million. Use of online social media rises every month. In fact, writing is overtaking speech as the most common form of interaction.

The fact that we write to each other more than we talk to each other comes with its own set of problems, of course, but Simon argues that all this texting and IM’ing and status updating is turning us into better writers. And he makes a lot of sense.

Liat Ben-Zur wrote a great post for AllThingsD called Connecting Things to the Internet Does Not an Internet of Things Make. His main issue with the current crop of connected devices is lack of cross-platform integration:

Each specific device seems to connect to its particular cloud service. There isn’t really one cloud. Every manufacturer has their own cloud service, and often these clouds are closed, proprietary environments. Devices that live in their own siloed cloud cannot speak to one another, meaning they cannot benefit from the data, context or control of nearby IoT devices. That is why we currently need a separate app to control — and interface with — each connected thing we buy. This may be acceptable in the near term, but it cannot scale.

This made me think of Ian Bach’s article Designing Connected Products:

What’s more, when it comes to creating a smooth connected experience, focusing on the ‘things’ from the start can actually be somewhat of a decoy. Spend some time with any service or product that relies on data jumping from place to place and you’ll quickly realise it’s in the ‘gaps’ between things that design really matters. Problem is, gaps are easy to overlook, incredibly tough to design for and much less sexy than the ‘things’.

Image source: Ian Bach

Ian comes from a different angle, but I think these points are related. Cloud services connect the ‘gaps’ between things, but it’s incredibly hard to fill the gaps well, so most companies keep their solutions proprietary since they see it as a competitive advantage. And that’s why we’re in the situation we’re in: great physical products with reasonably ok cloud services, but because the services don’t talk to each other the products aren’t nearly as useful as they could be.

(First link via @kbaxter)

Jason Fried, co-founder of 37signals, in an interview with The Great Discontent:

People should consider the value of a long-term investment in something. Can you make your idea your life’s work instead of your life’s work being 30 ideas?

I’m more of a fan of constant, steady growth because it feels more sustainable over a long period of time. Creating things that are lasting is what great cultures do. […] What are we creating today that’s going to last for 20, 50, or 100 years? I like to think about that and I’d love to have more people think that way rather than thinking about what they can do for two years until they get bought out.

This is such a good point. We just don’t think about building things that last any more, because that takes time, and we’re not exactly known for our patience. Tangentially related, the recent Radiolab episode called “Speed” is absolutely brilliant — you have to listen to it.

Also see: The elusive goal of lasting beauty in web design.

Nick Bilton in Disruptions: Even the Tech Elites Leave Gadgets Behind, an article on the growing (not just hipster any more?) trend to step away from technology every once in a while:

As every aspect of our daily lives has become hyperconnected, some people on the cutting edge of tech are trying their best to push it back a few feet. Keeping their phone in their pocket. Turning off their home Wi-Fi at night or on weekends. And reading books on paper, rather than pixels.

The “phone stack” is becoming increasingly popular as a way to force people to talk to each other over dinner. Sad, but necessary.

Photo credit: Roo Reynolds on Flickr

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