Why speed beats quality: how corporate pressure sidelines UX for quick wins

Why speed beats quality: how corporate pressure sidelines UX for quick wins

A hero’s journey starts with a call to adventure, something the hero sets out to achieve. This call to adventure is often a problem that arises (and that cannot be avoided) or an invitation to do something exciting.

In the second half of the 1940s, a man named Joseph Campbell analyzed the journeys of the hero in ancient myths. He found that the hero followed a common pattern in all these myths. He found this so compelling that he wrote a book about it called “The Hero with a Thousand Faces.”

The book became a worldwide success, inspiring many like George Lucas and Christopher Vogler. Vogler had attended the same school as Lucas. While he worked at the Walt Disney Studios in the 1990s, he was developing a secret code for generating movie stories based on Campbell’s work. He wrote a 7-page memo and distributed it amongst his colleagues. It was picked up by Jeffrey Katzenberg, chairman of Walt Disney Studios, and used in the movie The Lion King.

What had started as a memo, ended in a book called “The Writer’s Journey: Mythic Structure for Writers.” In it, Vogler identifies the 12 parts and three acts that form the basis of almost any (good) story.

Below is a representation of Christopher Vogler’s model:

Now, imagine that the UX designer is the hero of the story. She started working as a UX designer in the early 2000s. The first task she did for an ad agency was to translate whatever they had on paper to the web. She taught herself the skills to program in HTML and to turn her static screen design into an interactive web design.

As code took on a severe shape, she had to choose to continue as a designer or become a developer. Although she knew programming was the safe bet, she chose the first.

Bridging the gap: balancing agile and UX design without sacrificing quality

Bridging the gap: balancing agile and UX design without sacrificing quality

The Agile/SCRUM and UX design combo

If you have ever been the (only) designer in a team that works with traditional scrum, you know that you got stuck in the sprints sooner or later.

As a designer, you must do research (or outsource it), which takes time to prepare and execute. You are required to make prototypes, which take time to build and test. While you are working on those things, your team cannot move forward. This causes a sticky situation where team members may put pressure on you to finish quickly, or worse, you get sidelined as the team ‘steams’ ahead without you. In both cases, you, the user and the product suffer.

The Agile Manifesto caused this situation. Its 12 principles describe it (https://agilemanifesto.org/principles.html). I won’t read through all of them, but I will show you principle number 4, which goes like this: “Business people and developers must work together daily throughout the project.” The product owner represents ‘Business people,’ and the developers are, well, the developers. No designer is mentioned in any of the twelve principles.

So, bad news, right? Not necessarily.

I have worked on numerous projects. Large and small. New design and redesign. Large teams and small teams. The reality is that each project is different. Each team is different. Every company has its own character. Some are outright feature factories, and others do top-notch user-centred work. The truth is that there is no ‘one size fits all’ when it comes to synchronising design and scrum. Agile was never made for that. But this is what you can do:

1. Work outside of the sprints.
You can work outside of the sprints in a so-called Spike. A spike is a time-boxed period where you can do whatever you need to do without slowing down the rest of the team. When you are done (the time is up), you can send the value you got from your research back to the team.

2. Work two sprints ahead of the rest.
It is easiest to get done. Assuming your sprints are two weeks long, that will give you four weeks to do some user research and put together a solid report with recommendations.

3. Base your information on desk research web analytics and best practices.
Dicey. A poor man’s solution. A catch-22 but still a lot better than gut feelings, which I have also seen happen at prestigious companies. Let the web analyst make a list of the top 250 most visited pages if the website is that large and select from the top 50 or so to understand what people are looking for on the website. Then, get a Hotjar account and observe people walking across the pages. Now you know what people are doing; you only have to figure out the ‘why’. But that’s what user research is for. That is the catch.

Photo: Guillaume de Germain

How a broken lamp by Emile Gallé revealed the power of craftsmanship and emotional value

How a broken lamp by Emile Gallé revealed the power of craftsmanship and emotional value

Some years ago, I came across what I thought was a vase by the famous French glassmaker Emile Gallé from the 1900s.

The ‘vase’ was far from perfect, but I knew that. A piece at the top had broken off, but I would have that fixed. I had no intention of selling it, so it didn’t matter to me.

After some research, I found out that my ‘vase’ was not a vase at all but a lamp—the base of a lamp, to be correct. There are only a handful of them left, mostly in museums. Barbara Streisand used to have one.

I also discovered that fixing a lamp with the shade missing requires quite a lot more effort than fixing a vase. The more research I did, the more I learned just how much effort was needed.

I discovered that the glass is made up of three different-coloured layers. They then used acid to eat parts of the glass away to get the cameo effect his glass is famous for. After that, the glass was carved to perfection by hand.

I also learned a lot about the man himself, and I grew fond of him. Emile Gallé had a human side, which was less common in the period before the Great War.

The effort that went into making the lamp is enormous. And the story of the man who made it gives the object a soul. I have warmed up to him, and he also has my respect.

The effort I must put into fixing the object is still unclear, but I will do it. I am emotionally invested.

The rise of data-driven UX: how designers and analysts are reshaping decision-making

The rise of data-driven UX: how designers and analysts are reshaping decision-making

Am I the only one noticing this?

UX researchers and designers, who were once separate from data analysts, are now working together. What great news! When organisations ask for a UX designer now, one of the key requirements is that they be data-driven.

Studies show that data-driven organisations make better strategic decisions, experience higher operational efficiency, improved customer satisfaction, and stronger profit and revenue levels. Recent research even reveals that data-driven organisations are twenty-three times more likely to acquire customers, six times more likely to retain them, and nineteen times more likely to be profitable.

This highlights an important truth: at the intersection of two or more seemingly unrelated disciplines, far more valuable insights emerge than from within a single field alone.

I’ve always been a data-driven designer, though not officially. No one had ever requested that I take usage data into account when conducting research—until now.

What I see happening is a shift away from the reliance on surveys, as we incorporate analytics into our mix of quantitative methods and desk research.

The triangulation of information in user research could look like this:

triangulation

Triangulation isn’t a miracle method; it has its critics. Sure, it increases the likelihood of making informed decisions, but there’s a catch. Surveys have long been criticised for being inaccurate or biased—whether because respondents don’t answer truthfully or because the analysis is flawed. We only need to look at election polls: when are they ever spot-on? And what’s true for surveys also applies to usage data. While there aren’t respondents tailoring their answers to what they think we want to hear, the data is still analysed by humans. Even with machine learning, the process is indirectly biased (take a look at the debate on “math washing”).

Data is math, and math is binary—it proves something right or wrong. There’s no disputing a correct mathematical result. And that’s where things can go wrong.

 Machine learning is math, and its outcomes leave no room for debate or doubt. It presents hard evidence.

This places a lot of power in the hands of data analysts.

Two brothers on the fifth floor

Two brothers on the fifth floor

Two brothers work in the office next to me.

They deal in high-quality coffees.

You wouldn’t find two more different people than these two brothers, though they both share a love for music and play several instruments.

One day, they invited me over for a chat, and the conversation drifted to music. I admitted, somewhat embarrassed, that I didn’t play any instruments and had no real knowledge about music. “I just know what I like,” I said, hoping the topic would end there. But it didn’t.

One of the brothers has an academic mindset. He went to university, enjoys deep discussions with his small circle of friends, and approaches most things like an algorithm that can be solved.

The other brother didn’t go to university. He chose a different path, working in restaurants, hotels, and bars. He’s a people person, always in touch with others, and is entirely self-taught.

The academic brother, full of passion, started gesturing in the air, trying to explain the difference between an A minor and an A major. It was clear he was frustrated by my inability to follow.

The other brother, who had been observing, simply turned to his piano, hit a key, and said, “This is an A minor.” Then he pressed another key and added, “And this is an A major.”

“Aha,” I said.

Photo: Simon Noh

why hard work and efficiency are the real keys to success in a fast-paced world

why hard work and efficiency are the real keys to success in a fast-paced world

Doing hard work is never easy. Stupid thing to say really but there it is.

Doing hard work is never easy. A simple statement, but true nonetheless.

The thing about ease is that, while it sounds appealing, it comes with many downsides.

We like to make things easy for ourselves. Work should be easy. Love should be easy. Food should be easy. Becoming a millionaire should be easy. Saving the planet should be easy.

But they aren’t. Nothing good ever is.

When you don’t put in the effort, you don’t feel the reward—there’s no dopamine rush.

Winning at tennis against a 5-year-old isn’t exactly satisfying. But winning against Bjorn Borg, even at 63, would be something worth bragging about. That would boost your self-confidence.

This applies to things that really matter—things that change the world, life goals you want to achieve. But it doesn’t necessarily apply to the routine tasks of day-to-day life, like stopping at a drive-thru to grab a bite. Fast food companies have made it easy to get in, buy something, and leave in five minutes or less, all for a low cost. If you decide to stay, they’ll entertain you and your kids.

It works.

It’s not just because your kids think it tastes good (though that helps) that these companies are so successful. They make everything easy: a few simple menu items, all easy to understand, all affordable. Once you’re hooked as a child, you’re hooked for life.

Fast food chains thrive because they follow the rules of efficiency: tasty, quick, cheap, and easy. Ikea does this and is successful. Online stores do this and are successful. Automation during the first and second industrial revolutions did this and was wildly successful.

The only way something can be easy and worthwhile is if you make the process efficient.

But here’s the catch: making something efficient is hard work.

On the global stage, there’s no place to hide; most of us are already in the spotlight.

Photo: Nihal Demirci