原文地址：Are You a Good Driver? How Designers Use Data to Get to the Truth

原文作者：Matt Cooper-Wright

翻译：东东方

译文：

              你是一个好司机吗？设计师怎样用数据获取真相

传感器、摄像头和开放的数据集意味着我们比以往更了解人们的行为。这是重塑设计和创新的过程。

当你询问伦敦有车一族他们的驾驶速度时，十之八九他们会告诉你“好”或者“非常好”。而所记录的驾驶行为，却呈现了一副不同的画面。这一点也不奇怪，城市里的“好”或“非常好”的司机通常比人们自己认为的要少很多。

这里发生了什么？为什么人么总是一致地高估自己的驾驶能力？作为一个设计师，我想知道：我是否应该设计一款产品服务于他们在开车时的所想，和他们到底如何开车？

揭示全貌

作为以人为本的设计师，我们常常使用数据寻求启发。在过去，这些定性数据都是通过人类学收集——比如家访，参与网上论坛，面谈等等。

但是人们所说，所思，所感，所做的一切也许会变得不同和矛盾。这并不是说人们不诚实——事实上恰恰相反，他们给出了尽可能真实的答案。

人类学设计研究并没有传统将定量和定性方法结合在一起。现在，科技让它成为了可能。廉价的感测器，小型电脑和开源数据集让我们更靠近这个世界的客观真实，而不是人们主观的看法。

这些数据向我们展示了人们的真实行为，

以及它为何不同于人们的所言。

作为一个设计师，我发现将定量与定性研究数据结合在一起的关键是理解全貌：这有时被称之为“混合”，也有了越来越多的设计研究标准。

言行不一是人的本性。一个以人为本的方法意味着为此设计（我的同事阿里安娜-麦克莱恩，一名在旧金山IDEO的设计师，在她的一篇文章里对数据和设计进行了深入探讨。）。

留意偏差

我们发现车主的感知与现实存有差距，所以我们设计了一款新型数据驱动的服务为城市司机减轻压力。作为我们研究的一部分，我们记录了15个伦敦人在三个月当中的驾驶行为，如方向盘角度，或踩下刹车的力度。我们以多种形式得到的数据，成为我们整个设计过程的一个组成部分。

这些与人们意图与行为相关的新数据点会以三种方式影响每一个阶段的设计——从在研究阶段的灵感，依据真实改善设计，细心研究行为数据，通过证明工作原型的价值进行纠正，并建立一个业务案例。

1.发现新的机遇

数据是一个灵感的新来源。而伟大的设计则来源于灵感。

在设计过程的开始，公共数据集关注了以往未曾察觉的新的机会领域。

个人、组织和政府逐渐意识到开放数据的作用，而更多的接口和可视化工具的出现让它们更有意义。举个例子，就像我们的新服务，最近的数据显示，英国汽车平均行驶里程下降，这显示了人们在汽车行驶过程中行为习惯的变化。伦敦交通的智能移动公开其传输数据——而不是自己开发比其它公司更为快速敏捷的APP。而其它公司，比如CityMapper，旨在为人们开发更好的工具。

作为设计师，我们应该利用新的工具和学会怎样直接运用数据，而不是依赖数据专家代表我们来处理数据。

如果你可以直接查看原始数据，你也许会灵感突现，

而数据专家只会将它们作为离群值丢弃。

当我们开始追踪我们城市的司机时，我们很快意识到我们需要建立可视化工具来让整个设计团队看到数据。从最简单的柱状图和饼状图显示驾驶过程中的刹车和减速开始，接着所行驶路线的天气情况和交通状况。对于整个设计团队而言，能够访问和理解数据非常重要，而不同的团队成员对数据的见解也有所不同。

发掘隐藏模式

聚集行为数据能够解锁隐藏模式和有新的见解。比如说，我们城市的司机组成的数据显示，周三和周四在伦敦开车最为危险。

如果你定期在城市里开车，你也许已经察觉到这样的情况。事实上，在一次采访中，我们中的一个司机说：“我不知道为什么，但人们在周三开车很古怪。”他感觉到我们能用数据证明。

当这些隐藏模式出现，我们可以量化他们所出现几率的高低。我们城市的司机过高的自我评估，往往是最严重的表现。我们的数据揭示了一个重要的现象，部分司机掉进了过分自信的怪圈，于是我们调整了我们的设计以尽到为他们服务的职责。

宏观观察和微观观察

正如数据开始有了揭示宏观行为的趋势，同时它也揭示了微观行为。亚马逊在过去已经确定，在网站上100毫秒的延迟意味着销售额下降1%。

当用户的行为可以以毫秒观测时，对于设计师而言有一些有趣的问题：你设计质量的评测速度不过在眨眼之间，人们如何感受到？谷歌和亚马逊已经说明了改善现有产品的重要性，但是如果你在设计的时候就知道这些会怎样？

除了这些自信的司机，研究还发现另一些紧张不安的司机。微观行为显示，特别是夜晚开车时紧张是其它更广泛紧张情况的一个关键指标。简而言之，如果你在晚上开车时紧张，你可能在其它一些情况下也紧张。如果你的设计研究已经在人群中确定了两个截然不同的分组，你该如何设计以适应两者？

2.衡量人类行为的细微差别

用数据来对人们的言语，思想，感觉和行为进行三角剖分。

人类学已经建立了一些伟大的工具去揭示人们的思想和感受，互补的数据能帮助我们理解人们行为之间的细微差别。两者结合强而有力。

除了追踪司机如何使用我们的原型，同时通过在线定量调查，我们也从更多的500名城市司机中获得灵感。将附加的研究方法放在更为广阔的环境中。

连接研究点

将15个司机与另外500个司机结合一起给设计团队，还有用户相信我们这个小团体的司机代表着更大群城市的司机：从洞悉小范围的研究直接了解另外一些群体。

对于一个设计师而言，当有新的服务或产品时，你应该在它们还没有建立之前就知道用户的反应。

纵向学习

设计新事物的挑战在于提供一系列证据证明潜在的影响力。

在我们的行驶项目结束时，我们有75小时的人类学采访，2000万行驶数据点和从我们原型中的分析，还有650个司机的调查反馈。我们所有的证据都指向我们的设计方案。这是我最感兴趣的领域之一——记录行为的能力随着时间推移，去见证当你设计新的东西时人们如何改变。收集数据突然间让这成为了可能。

例如，我们城市的司机有一个app，能将我们所捕捉到的数据显示给他们。两周的时间，他们每天都打开app，这证明了我们的成功，也证明了我们的用户发现了他们觉得有价值的东西。

更好的是，记录数据可以马上告诉我们真正的行为，据此适应设计过程的快速迭代。

当我们用一周开发原型时，我们也在基于下周我们收集的数据对它们进行精炼。这些对于从事数码产品的人而言很熟悉，但对于设计师而言这是一个新的机会。

看到很少的用户使用你耗时费力开发的原型是令人十分沮丧的事情。但更糟的是你将产品投入生产并使用，而依旧少有用户参与其中。

3.建立商业案例

数据和用户行为一样可以证明一个想法的价值。

对于用户而言，一个好的想法远远不够：用户的愿景是需要看到该想法的商业可行性。如果你已经收集到纵向数据并用它塑造了原型，接下来的步骤就是将这些数据直接投入一个新兴的商业模式。

原始行为数据所生成的原型对交互设计师和商业设计师是十分有用的。

它可以在你还在设计时就为你的设计建立一个业务案例，同时量化新设计的潜在影响。我们很难对用户的反馈和回应置之不理。

比如说，最后在我们城市行驶项目中，商业设计师可以基于我们收集到的真正的驾驶行为设计可变的价格模型

以人为本的数据

最后，数据在设计过程中的新角色是更好的理解人们：这只是一个新的视角。很难想象回到之前的过程，我们没有为所设计对象的生活描绘一幅丰富的画像。

但是数据的使用不单只是新的机会和更好的观察。

监测行为会随着时间的变化赋予设计师力量，让他们的工作拥有更大的潜在影响力。

举个例子：在我们的项目中，我们测试的司机告诉我们他们感觉良好，但是数据告诉我们实际上并非如此。刚开始，他们对主观认为和客观事实上的偏差很诧异，并要求给出数据证明。看他们的驾驶回访是很轻松的时刻，但是每一个司机都下定决心成为一个更好的司机。

不久以后，追踪数据显示他们的行为得到了改善。我们知道依据数据的设计能帮助我们起到让人们更好驾驶的作用。

我们还在用工具和技术进行着实验，我很想看看别人如何用数据去重塑他们的过程。

原文：

Are You a Good Driver? How Designers Use Data to Get to the Truth

By Matt Cooper-Wright

Sensors, cameras and open data sets mean we’re learning more about people’s behaviour than ever before. That’s reshaping the design and innovation process.

Ask car-owning Londoners to rate their driving, and nine out of ten will tell you they’re either ‘good’, or ‘very good’. Record people’s driving behaviour, and a different picture emerges. It will come as no surprise to anyone who’s driven in a city recently that ‘good’ and ‘very good’ drivers are much less common than people’s self-reporting would suggest.

So what’s happening here? Why do people consistently overestimate their driving ability? As a designer, I want to know: should I design products and services for how theythinkthey drive, or how theyactuallydrive?

Revealing the Full Picture

As human-centred designers we’ve always used data to get inspired. In the past it’s been qualitative data gathered through ethnography — home visits, participating on online forums, and interviews, for example.

But what people say, think, feel and do can all be different and contradictory. It’s not that people are being dishonest — in fact quite the opposite, they’re giving as honest an answer as they can.

Ethnographic design research hasn’t traditionally brought quantitative and qualitative methods together. Now, technology is making it possible. Cheap sensors, smaller computers and open source datasets are making it possible to access an objective picture of the world and compare it with people’s subjective views.

This behavioural data shows us what peoplereallydo,

and how it differs from what they say.

As a designer i’ve found that combining quantitative and qualitative research data is the key to understanding the full picture: it’s sometimes called ‘hybrid’, and is increasingly the standard for design research (you can read morehere).

It’s human nature to say one thing, and do another. A people-centred approach means designing for that. (My colleague Arianna McClain, a designer at IDEO in San Francisco, explores this in depth inher article about data and design.)

Mind the gap

We spotted this gap between car owners’ perception and reality while designing a new type of data-driven service to reduce stress for city drivers. As part of our research, we recorded the driving behaviour of 15 Londoners for three months, things like steering wheel angle, or how heavily people braked. What we found is data, in many forms, became an integral part of our entire design process.

Here are three ways these new data points around human intention and behaviour can impact design at every stage: from inspiring at the research phase, refining design with real, nuanced behavioural data, right through to proving the value of a working prototype, and building a business case for it.

1. Discovering new opportunities

Data is a new source of inspiration. Inspiration fuels great design.

At the beginning of a design process publicly-accessible datasets draw attention to new opportunity areas that were previously unobservable.

Individuals, organisations andgovernmentsare gradually realising how useful open data can be, while more interfaces and visualisation tools are bubbling up to make sense of them. For example, in relation to our new service, recent figures showed average mileage for cars in the UK isfalling, which shows changing behaviour around car travel. Transport for London took the smart move of making its transport datapublicly available— rather than developing apps themselves they have made it much easier for more agile companies, likeCityMapper, to build better tools for people.

As designers we should be both making use of the new tools and learning how to manipulate data directly, rather than relying on data scientists to process the data on our behalf.

If you’re able to look at raw data directly you might spot inspiration

that a data scientist would discard as an outlier.

When we started tracking our city drivers, we quickly realised we’d need to build visualisation tools to let the whole design team see the data. This began with simple bar- and pie-charts showing braking and acceleration over a journey, and moved toward geographically-mapped routes with overlayed weather and traffic conditions. It was very important that the whole team could access the data and understand it, as different team members spotted different insights in the data.

Unearth hidden patterns

Aggregating behavioural data can unlock hidden patterns and new insights. Grouping together the data from our city drivers, for example, showed that Wednesdays and Thursdays are the most dangerous days to drive in London, for example.

If you drive in cities on a regular basis this might be something you’ve noticed. In fact, during an interview, one of our drivers told us: “I don’t know why, but people just drive weirdly on Wednesdays.” He had sensed what we were able to prove with data.

As these hidden patterns emerge, we can quantify the size of the opportunity they represent. Our city drivers who rated their confidence highest were often among the most badly behaved. Our data showed a significant enough portion of drivers fell into this over-confident group for us to adjust our design of the service to account for them.

Macro and micro observation

Just as data starts to reveal macro behavioural trends, it also reveals micro behaviours. Amazon in the past hasidentifiedthat latency of 100 milliseconds on their website translated to a drop in sales of 1%.

There are interesting questions for designers when user behaviour can be measured in milliseconds: how does it feel when the quality of your

design can be assessed at speeds faster than the blink of an eye? Google

and Amazon have shown theimportanceof this when refining an existing product, but what if you knew about this while designing something?

Alongside our over-confident drivers, research revealed another

group of nervous drivers. The micro behaviours that related to specific nervousness around driving at night were a key indicator for broader nervousness. Simply put, if you’re nervous driving at night you’re probably nervous in a range of other situations. If your design research has identified two very different groups in a population, how should you design to

suit both?

2. Measuring the nuance of

human behaviour

Triangulating thesay, think, feel and do,with data.

Where ethnography has developed a great suite of tools to uncover what people really think and feel, complementary data can help us understand the nuance

of human behaviour. The combination of the two is potent.

Beyond simply tracking how our drivers used our prototype, we also took inspiration from online quantitative surveys we’d run with a bigger group

of 500 city drivers. The additional research methods put them in a broader context.

Joining the research dots

Correlating the detailed behaviour of the 15 with the 500 gave the design team and the client confidence that our small subset of drivers were representative of a much bigger group of city drivers: insight from one stream of research directly informed another.

For a designer, knowing your future customer’s reaction to

a new service or product before it’s built is something new.

Longitudinal learnings

The challenge when designing something new is to build a body of evidence to show the potential for impact.

By the end of our driving project we had 75 hours of ethnographic interviews, 20 million driving data points and analytics from our prototypes, and the survey responses of 650 drivers. All of our evidence pointed towards our design solution. This is one of the areas I’m most interested in — the ability to record behaviour over time, to see how people changewhileyou’re designing something new. Collecting data suddenly makes that possible.

Our city drivers were given a prototype app showing them the data we’d captured, for example. Seeing them open the app every day for a fortnight was proof for us and our client we’d found something they really valued.

Better still, recording data can show us real behaviour immediately,

and therefore fit into the rapid iteration of the design process.

As we developed prototypes for our drivers one week, we refined them based on the data we’d collected the following week. This will be familiar to anyone working on digital products, but it’s a new opportunity for designers.

It can be disheartening to see low user engagement in a prototype you’ve spent time developing. But it would be far worse to see low engagement in a product launched into the real world.

3. Building the business case

Data can prove an idea’s value, as well as user behaviour

For clients a good idea is not enough: desirability from users needs to be matched with commercial viability. If you’ve collected longitudinal evidence and used it to shape design, the next logical step is to take this data directly into an emerging business model.

The original behavioural data a prototype generates is useful

to the interaction designer and business designer alike.

It can build a business case for the thing you’re designing while you’re still designing, and quantify the potential impact of a new design. It’s hard to argue against real user’s reactions and responses.

Toward the end of our city driving project, for example, business designers were able to design a variable pricing model based on the real driving behaviour we’d gathered.

Human-centred data

Ultimately, data’s new role in the design process is better understanding people: it’s just a new perspective.It’s hard to imagine going back to a process that doesn’t draw such a rich picture of the life of the people I’m designing for.

But data’s use goes beyond new opportunities and better observation.

Measuring behaviour over time gives designers new power to

improve their work’s potential for impact.

Take one example: towards the end of our project, we retested our concepts with the drivers who’d said they were good, but the data showed were in fact badly behaved. They were initially shocked at the gap between their subjective view, and the objective reality, and demanded to see the data evidence. Seeing their driving played back to them was a moment of levity, but each resolved to become a better driver.

Soon afterwards, the tracking data showed their behaviourdidimprove.

We knew then that designing with data had helped us get to the heart of encouraging better driving.

We’re continuing to experiment with tools and techniques, I’d love to hear how others are using data to reshape their process.