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Empathy is a state of mind, not a specific technique

A design process paved with empathic observations will lead you, slowly and iteratively, to a better product.

Editor’s note: this post was originally published on the author’s blog, Exploring the world beyond mobile; this lightly edited version is republished here with permission.

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If I’m ever asked what’s most important in UX design, I always reply “empathy.” It’s the core meta attribute, the driver that motivates everything else. Empathy encourages you to understand who uses your product, forces you to ask deeper questions, and motivates the many redesigns you go through to get a product right.

But empathy is a vague concept that isn’t strongly appreciated by others. There have been times when talking to product managers that my empathy-driven fix-it list will get a response like, “We appreciate that Scott, but we have so much to get done on the product, we don’t have time to tweak things like that right now.” Never do you feel so put in your place when someone says that your job is “tweaking.”

The paradox of empathy is that while it drives us at a very deep level, and ultimately leads us to big, important insights, it usually starts small. The empathic process typically notices simple things like ineffective error messages, observed user workarounds, or overly complicated dialog boxes. Empathy starts with very modest steps. However, these small observations are the wedge that splits the log; it’s these initial insights, if you follow them far enough, that open up your mind and lead you to great products.

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Four short links: 4 March 2015

Four short links: 4 March 2015

Go Microservices, Watch Experience, Multithreading Bugs, and Spooks Ahoy

  1. Microservices in Go — tale of rewriting a Ruby monolith as Go microservices. Interesting, though being delivered at Gophercon India suggests the ending is probably not unhappy.
  2. Watch & Wear (John Cross Neumann) — Android watch as predictor of the value and experience of an Apple Watch. I believe this is the true sweet spot for meaningful wearable experiences. Information that matters to you in the moment, but requires no intervention. Wear actually does this extremely well through Google Now. Traffic, Time to Home, Reminders, Friend’s Birthdays, and Travel Information all work beautifully. […] After some real experience with Wear, I think what is more important is to consider what Apple Watch is missing: Google Services. Google Services are a big component of what can make wearing a tiny screen on your wrist meaningful and personal. I wouldn’t be surprised after the initial wave of apps through the app store if Google Now ends up being the killer app for Apple Watch.
  3. Solving 11 Likely Problems In Your Multithreaded Code (Joe Duffy) — a good breakdown of concurrency problems, including lower-level ones than high-level languages expose. But beware. If you try this [accessing variables with synchronisation] on a misaligned memory location, or a location that isn’t naturally sized, you can encounter a read or write tearing. Tearing occurs because reading or writing such locations actually involves multiple physical memory operations. Concurrent updates can happen in between these, potentially causing the resultant value to be some blend of the before and after values.
  4. Obama Sharply Criticizes China’s Plans for New Technology Rules (Reuters) — In an interview with Reuters, Obama said he was concerned about Beijing’s plans for a far-reaching counterterrorism law that would require technology firms to hand over encryption keys, the passcodes that help protect data, and install security “backdoors” in their systems to give Chinese authorities surveillance access. Goose sauce is NOT gander sauce! NOT! Mmm, delicious spook sauce.
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7 user research myths and mistakes

Finding the holes in qualitative and quantitative testing.

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I can’t tell you how often I hear things from engineers like, “Oh, we don’t have to do user testing. We’ve got metrics.” Of course, you can almost forgive them when the designers are busy saying things like, “Why would we A/B test this new design? We know it’s better!”

In the debate over whether to use qualitative or quantitative research methods, there is plenty of wrong to go around. So, let’s look at some of the myths surrounding qualitative and quantitative research, and the most common mistakes people make when trying to use them.
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Year Zero: Our life timelines begin

In the next decade, Year Zero will be how big data reaches everyone and will fundamentally change how we live.

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Editor’s note: this post originally appeared on the author’s blog, Solve for Interesting. This lightly edited version is reprinted here with permission.

In 10 years, every human connected to the Internet will have a timeline. It will contain everything we’ve done since we started recording, and it will be the primary tool with which we administer our lives. This will fundamentally change how we live, love, work, and play. And we’ll look back at the time before our feed started — before Year Zero — as a huge, unknowable black hole.

This timeline — beginning for newborns at Year Zero — will be so intrinsic to life that it will quickly be taken for granted. Those without a timeline will be at a huge disadvantage. Those with a good one will have the tricks of a modern mentalist: perfect recall, suggestions for how to curry favor, ease maintaining friendships and influencing strangers, unthinkably higher Dunbar numbers — now, every interaction has a history.

This isn’t just about lifelogging health data, like your Fitbit or Jawbone. It isn’t about financial data, like Mint. It isn’t just your social graph or photo feed. It isn’t about commuting data like Waze or Maps. It’s about all of these, together, along with the tools and user interfaces and agents to make sense of it.

Every decade or so, something from military or enterprise technology finds its way, bent and twisted, into the mass market. The client-server computer gave us the PC; wide-area networks gave us the consumer web; pagers and cell phones gave us mobile devices. In the next decade, Year Zero will be how big data reaches everyone. Read more…

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What experts do: Curse of the intermediate

A framework for what separates those whose skills continue to build and those who stall out no matter how much they try.

We all know the Curse of Expertise — that thing that makes most experts awful at imagining what it’s like to be a novice. The Curse of Expertise makes tech editors weep and readers seethe. “Where’s the empathy?!” we say, as if the expert had a conscious choice. But they mostly don’t. The Curse of Expertise is not a problem for which MOAR EMPATHY is the solution. Experts don’t lack empathy; they lack the security clearance to the part of their brain where their cognitive biases live. Subconscious cognitive biases. And those biases don’t just make us (experts) fail at predicting the struggle of novices, they can also make us less likely to see novel solutions to well-worn problems.

But given a choice to suddenly be an expert or a novice, we’d pick Curse of the Sucks-To-Be-Me Expert over Curse of the I-Suck-At-This Novice. There’s a third curse, though. The mastery curve is, of course, not binary, but a continuum from first-time to Jiro-Dreams-Of-Sushi. And there in the middle? The Curse of the Intermediate. The Curse of the Intermediate is the worst because it’s the place where hopes and dreams of expertise go to die. The place where even the most patient practicer eventually believes they just don’t have what it takes. Read more…

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Four short links: 2 March 2015

Four short links: 2 March 2015

Onboarding UX, Productivity Vision, Bad ML, and Lifelong Learning

  1. User Onboarding Teardowns — the UX of new users. (via Andy Baio)
  2. Microsoft’s Productivity Vision — always-on thinged-up Internet everywhere, with predictions and magic by the dozen.
  3. Machine Learning Done WrongWhen dealing with small amounts of data, it’s reasonable to try as many algorithms as possible and to pick the best one since the cost of experimentation is low. But as we hit “big data,” it pays off to analyze the data upfront and then design the modeling pipeline (pre-processing, modeling, optimization algorithm, evaluation, productionization) accordingly.
  4. Ten Simple Rules for Lifelong Learning According to Richard Hamming (PLoScompBio) — Exponential growth of the amount of knowledge is a central feature of the modern era. As Hamming points out, since the time of Isaac Newton (1642/3-1726/7), the total amount of knowledge (including but not limited to technical fields) has doubled about every 17 years. At the same time, the half-life of technical knowledge has been estimated to be about 15 years. If the total amount of knowledge available today is x, then in 15 years the total amount of knowledge can be expected to be nearly 2x, while the amount of knowledge that has become obsolete will be about 0.5x. This means that the total amount of knowledge thought to be valid has increased from x to nearly 1.5x. Taken together, this means that if your daughter or son was born when you were 34 years old, the amount of knowledge she or he will be faced with on entering university at age 17 will be more than twice the amount you faced when you started college.
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