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

翻譯：東東方

譯文：

? ? ? ? ? ? ? 你是一個好司機嗎？設計師怎樣用數據獲取真相

傳感器、攝像頭和開放的數據集意味著我們比以往更了解人們的行為。這是重塑設計和創新的過程。

當你詢問倫敦有車一族他們的駕駛速度時，十之八九他們會告訴你“好”或者“非常好”。而所記錄的駕駛行為，卻呈現了一副不同的畫面。這一點也不奇怪，城市里的“好”或“非常好”的司機通常比人們自己認為的要少很多。

這里發生了什么？為什么人么總是一致地高估自己的駕駛能力？作為一個設計師，我想知道：我是否應該設計一款產品服務于他們在開車時的所想，和他們到底如何開車？

揭示全貌

作為以人為本的設計師，我們常常使用數據尋求啟發。在過去，這些定性數據都是通過人類學收集——比如家訪，參與網上論壇，面談等等。

但是人們所說，所思，所感，所做的一切也許會變得不同和矛盾。這并不是說人們不誠實——事實上恰恰相反，他們給出了盡可能真實的答案。

人類學設計研究并沒有傳統將定量和定性方法結合在一起。現在，科技讓它成為了可能。廉價的感測器，小型電腦和開源數據集讓我們更靠近這個世界的客觀真實，而不是人們主觀的看法。

這些數據向我們展示了人們的真實行為，

以及它為何不同于人們的所言。

作為一個設計師，我發現將定量與定性研究數據結合在一起的關鍵是理解全貌：這有時被稱之為“混合”，也有了越來越多的設計研究標準。

言行不一是人的本性。一個以人為本的方法意味著為此設計（我的同事阿里安娜-麥克萊恩，一名在舊金山IDEO的設計師，在她的一篇文章里對數據和設計進行了深入探討。）。

留意偏差

我們發現車主的感知與現實存有差距，所以我們設計了一款新型數據驅動的服務為城市司機減輕壓力。作為我們研究的一部分，我們記錄了15個倫敦人在三個月當中的駕駛行為，如方向盤角度，或踩下剎車的力度。我們以多種形式得到的數據，成為我們整個設計過程的一個組成部分。

這些與人們意圖與行為相關的新數據點會以三種方式影響每一個階段的設計——從在研究階段的靈感，依據真實改善設計，細心研究行為數據，通過證明工作原型的價值進行糾正，并建立一個業務案例。

1.發現新的機遇

數據是一個靈感的新來源。而偉大的設計則來源于靈感。

在設計過程的開始，公共數據集關注了以往未曾察覺的新的機會領域。

個人、組織和政府逐漸意識到開放數據的作用，而更多的接口和可視化工具的出現讓它們更有意義。舉個例子，就像我們的新服務，最近的數據顯示，英國汽車平均行駛里程下降，這顯示了人們在汽車行駛過程中行為習慣的變化。倫敦交通的智能移動公開其傳輸數據——而不是自己開發比其它公司更為快速敏捷的APP。而其它公司，比如CityMapper，旨在為人們開發更好的工具。

作為設計師，我們應該利用新的工具和學會怎樣直接運用數據，而不是依賴數據專家代表我們來處理數據。

如果你可以直接查看原始數據，你也許會靈感突現，

而數據專家只會將它們作為離群值丟棄。

當我們開始追蹤我們城市的司機時，我們很快意識到我們需要建立可視化工具來讓整個設計團隊看到數據。從最簡單的柱狀圖和餅狀圖顯示駕駛過程中的剎車和減速開始，接著所行駛路線的天氣情況和交通狀況。對于整個設計團隊而言，能夠訪問和理解數據非常重要，而不同的團隊成員對數據的見解也有所不同。

發掘隱藏模式

聚集行為數據能夠解鎖隱藏模式和有新的見解。比如說，我們城市的司機組成的數據顯示，周三和周四在倫敦開車最為危險。

如果你定期在城市里開車，你也許已經察覺到這樣的情況。事實上，在一次采訪中，我們中的一個司機說：“我不知道為什么，但人們在周三開車很古怪?！彼杏X到我們能用數據證明。

當這些隱藏模式出現，我們可以量化他們所出現幾率的高低。我們城市的司機過高的自我評估，往往是最嚴重的表現。我們的數據揭示了一個重要的現象，部分司機掉進了過分自信的怪圈，于是我們調整了我們的設計以盡到為他們服務的職責。

宏觀觀察和微觀觀察

正如數據開始有了揭示宏觀行為的趨勢，同時它也揭示了微觀行為。亞馬遜在過去已經確定，在網站上100毫秒的延遲意味著銷售額下降1%。

當用戶的行為可以以毫秒觀測時，對于設計師而言有一些有趣的問題：你設計質量的評測速度不過在眨眼之間，人們如何感受到？谷歌和亞馬遜已經說明了改善現有產品的重要性，但是如果你在設計的時候就知道這些會怎樣？

除了這些自信的司機，研究還發現另一些緊張不安的司機。微觀行為顯示，特別是夜晚開車時緊張是其它更廣泛緊張情況的一個關鍵指標。簡而言之，如果你在晚上開車時緊張，你可能在其它一些情況下也緊張。如果你的設計研究已經在人群中確定了兩個截然不同的分組，你該如何設計以適應兩者？

2.衡量人類行為的細微差別

用數據來對人們的言語，思想，感覺和行為進行三角剖分。

人類學已經建立了一些偉大的工具去揭示人們的思想和感受，互補的數據能幫助我們理解人們行為之間的細微差別。兩者結合強而有力。

除了追蹤司機如何使用我們的原型，同時通過在線定量調查，我們也從更多的500名城市司機中獲得靈感。將附加的研究方法放在更為廣闊的環境中。

連接研究點

將15個司機與另外500個司機結合一起給設計團隊，還有用戶相信我們這個小團體的司機代表著更大群城市的司機：從洞悉小范圍的研究直接了解另外一些群體。

對于一個設計師而言，當有新的服務或產品時，你應該在它們還沒有建立之前就知道用戶的反應。

縱向學習

設計新事物的挑戰在于提供一系列證據證明潛在的影響力。

在我們的行駛項目結束時，我們有75小時的人類學采訪，2000萬行駛數據點和從我們原型中的分析，還有650個司機的調查反饋。我們所有的證據都指向我們的設計方案。這是我最感興趣的領域之一——記錄行為的能力隨著時間推移，去見證當你設計新的東西時人們如何改變。收集數據突然間讓這成為了可能。

例如，我們城市的司機有一個app，能將我們所捕捉到的數據顯示給他們。兩周的時間，他們每天都打開app，這證明了我們的成功，也證明了我們的用戶發現了他們覺得有價值的東西。

更好的是，記錄數據可以馬上告訴我們真正的行為，據此適應設計過程的快速迭代。

當我們用一周開發原型時，我們也在基于下周我們收集的數據對它們進行精煉。這些對于從事數碼產品的人而言很熟悉，但對于設計師而言這是一個新的機會。

看到很少的用戶使用你耗時費力開發的原型是令人十分沮喪的事情。但更糟的是你將產品投入生產并使用，而依舊少有用戶參與其中。

3.建立商業案例

數據和用戶行為一樣可以證明一個想法的價值。

對于用戶而言，一個好的想法遠遠不夠：用戶的愿景是需要看到該想法的商業可行性。如果你已經收集到縱向數據并用它塑造了原型，接下來的步驟就是將這些數據直接投入一個新興的商業模式。

原始行為數據所生成的原型對交互設計師和商業設計師是十分有用的。

它可以在你還在設計時就為你的設計建立一個業務案例，同時量化新設計的潛在影響。我們很難對用戶的反饋和回應置之不理。

比如說，最后在我們城市行駛項目中，商業設計師可以基于我們收集到的真正的駕駛行為設計可變的價格模型

以人為本的數據

最后，數據在設計過程中的新角色是更好的理解人們：這只是一個新的視角。很難想象回到之前的過程，我們沒有為所設計對象的生活描繪一幅豐富的畫像。

但是數據的使用不單只是新的機會和更好的觀察。

監測行為會隨著時間的變化賦予設計師力量，讓他們的工作擁有更大的潛在影響力。

舉個例子：在我們的項目中，我們測試的司機告訴我們他們感覺良好，但是數據告訴我們實際上并非如此。剛開始，他們對主觀認為和客觀事實上的偏差很詫異，并要求給出數據證明?？此麄兊鸟{駛回訪是很輕松的時刻，但是每一個司機都下定決心成為一個更好的司機。

不久以后，追蹤數據顯示他們的行為得到了改善。我們知道依據數據的設計能幫助我們起到讓人們更好駕駛的作用。

我們還在用工具和技術進行著實驗，我很想看看別人如何用數據去重塑他們的過程。

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.