Tricia Wang: The human insights missing from big data
トリシア・ワン: ビッグデータから見落とされる人間的な洞察
With astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers. Full bio
Double-click the English transcript below to play the video.
poets and politicians,
on life's most important questions,
大きな決断をしなければならない時 —
「結婚すべきか?」
「この航海を始めるべきか?」
advance into this territory?"
「自軍はこの地域に進出すべきか?」
and you would get on your knees,
she would come out of it,
what's going to happen next.
知るためでした
to make the right decision.
下したいと願うからです
knowing that we can make a decision
決断できると分れば
新しい「オラクル」があります
or "deep learning" or "neural net."
「ニューラルネット」呼び方は様々です
we ask of our oracle now,
例えば
to ship these phones
スマホを発送するための
with a genetic disorder?"
we can predict for this product?"
「この製品の 売り上げ予想は?」などです
and she hates the rain.
雨が大嫌いです
to untrain her.
あらゆることをしました
an oracle, called Dark Sky,
「予測(オラクル)」に頼らねばなりません
in the next 10 minutes.
天気予想を調べるのです
our oracle is a $122 billion industry.
私たちの予測は1220億ドルの産業です
aren't even profitable,
73%以上は 赤字で
coming up to me saying,
こう言います
better decisions.
with more breakthrough ideas."
アイデアも出てこない」と
(IT民族誌学者)だからです
利用するパターンについて
of how people use technology,
データなのです
not helping us make better decisions,
より良い決定の助けにならないのはなぜか?
who have all these resources
システムに投資するための
企業にとっては特にそうです
簡単にならないのか?
直接見てきました
a research position with Nokia.
ノキアで調査研究の仕事を始めました
cell phone companies in the world,
携帯電話会社であり
like China, Mexico and India --
新興市場を支配し —
a lot of research
低所得層の人が
調査研究をしました
as a street vendor
点心を売る
in internet cafés,
中国の若者と出歩いたりして
so I could understand
どう利用しているか
games and mobile phones
携帯をどう使うかを
from the rural areas to the cities.
that I was gathering,
質的証拠の全てを通して
among low-income Chinese people.
大きな変化が起ころうとしていることでした
by advertisements for luxury products
囲まれていましたが
who wouldn't want one? --
誰もが欲しいと思いますよね?
the actually enticed them the most
into this high-tech life.
約束する広告でした
in urban slums like this one,
暮らしていた時でさえ
over half of their monthly income
of iPhones and other brands.
安い模倣品でした
with migrants and working with them
that they were doing,
ほぼ全てやった後で
all these data points together --
まとめ始めて—
like me selling dumplings,
バラバラに見えるものから
支払額の推移のような
on their cell phone bills.
this much more holistic picture
何が起ころうとしているかの
would want a smartphone,
スマホを欲しいと考えていて
to get their hands on one.
ほぼ何でもするということです
looking like iPhones.
アンドロイド携帯も登場しました
and realistic people said,
多くがこう言っていました
these heavy things
持ち歩きたいと思うか?
and they break every time you drop them?"
一度落としたら故障するのに」
about my insights,
to share them with Nokia.
その予見をノキアに知らせました
なかったからです
millions of data points,
データポイントを持っている
of anyone wanting to buy a smartphone,
思う人の指標は見えないし
as diverse as it is, is too weak
バラバラのデータでは弱すぎる
「御社のいう通りです
assuming that people don't know
調査していれば
to get any data back
スマホの購入を希望する人についての
a smartphone in two years.
have been designed
既存のビジネスモデルを
at these emergent human dynamics
これから出てくるもので
of missing something.
見落としたことの代償です
throwing out data all the time
廃棄するのを見て来ました
出たものではないとか
ないというものでした
it's our responsibility.
私たちの責任です
very specific environments,
基づいたものであり
or delivery logistics or genetic code,
遺伝子コードなど
that are more or less contained.
定量化した場合です
are as neatly contained.
きちんと閉じている訳ではありません
and systems are more dynamic,
定量化を行う場合
that involve human beings,
人間が関与している場合は
予想不可能になり
that we don't know how to model so well.
うまくモデル化する術がありません
about human behavior,
are constantly changing.
enters the picture.
on big data alone
that we'll miss something,
that we already know everything.
幻想が生まれるのです
to see this paradox
非常に困難なのは
that I call the quantification bias,
状況があるからです
of valuing the measurable
測定不可能なものよりも重視するという
私たちの仕事において ありがちな経験です
colleagues who are like this,
このような同僚の傍で働いているか
company may be like this,
なのかもしれません
so fixated on that number,
outside of it,
right in front of their face.
wrong with quantifying;
from looking at an Excel spreadsheet,
スプレッドシートを見ても
Everything is under control."
全てうまくいっている」
to kind of keep that in check,
何かを備えていなければ
as a numerical value.
into silver-bullet thinking,
非常に簡単なことです
for any organization,
危険な瞬間です
the future we need to predict --
予測しなければならない未来は
that's bearing down on us
the wrong decisions.
of ancient Greece
行く末を私たちに示す
that shows us the path forward.
where the most famous oracle sat,
アポロの神殿は
over two earthquake faults.
建造されているのです
these petrochemical fumes
right above these faults,
この断層の真上に座して
of ethylene gas, these fissures.
大量のエチレンガスを吸い込んでいました
babble and hallucinate
大声でわめきながら 幻覚を見て
any useful advice out of her
有益な助言を
surrounding the oracle?
on your left-hand side
with the oracle.
and get on their knees,
would get to work,
仕事に就く時です
follow-up questions,
this prophecy? Who are you?
あなたは何者か?
with this information?"
などなど
this more ethnographic,
この答えを追加の民俗学的で
解釈しました
are huffing ethylene gas,
エチレン酔いだなどとは言いません
invalid predictions.
言うつもりもありません
that the oracle needed her temple guides,
寺院の介添え人を必要とするのと同じく
介添え人が必要だということです
and user researchers
ものを収集できる ―
必要です
that cannot be quantified.
貴重なデータです
that I collected for Nokia
データの類であり
of a very small sample size,
手に入るデータですが
深い意味を持っています
the human narrative.
what's missing in our models.
見落としを見つける助けとなります
in human questions,
人間の問いに基づいたものとなし
big and thick data
統合することで
insights at scale
of machine intelligence,
rescue the context loss
ビッグデータを使えるようにした時に
of human intelligence.
役立ちます
that's when things get really fun,
本当に面白くなります
just working with data
データを扱う以上のことが
that hasn't been collected.
できるようになるのです
to transform their business.
全く新しい道を開拓しました
recommendation algorithm,
推薦アルゴリズムで知られていて
for anyone who could improve it.
百万ドルの賞金を出しました
the improvements were only incremental.
漸増の過程であると気づきました
Grant McCracken,
グラント・マクラッケンを雇って
まとめさせました
that they hadn't seen initially
最初は定量的なデータの中には
to binge-watch.
を発見したのです
feel guilty about it.
"Oh. This is a new insight."
みたいな感じでした
this big data insight
スケールアップさせ
and validated it,
very simple but impactful.
インパクトのある決定をしました
the same show from different genres
提案するのをやめる
from similar users,
提案するのもやめて その代わり
どんどん見せて行こうというのです
for you to binge-watch.
言ったのです
viewer experience,
for whole weekends at a time,
見逃し配信があると
友人も人々も一斉に姿を消しました
like "Master of None."
they not only improved their business,
Netflixは自社の業績改善をしただけでなく
変貌させたのです
to double in the next few years.
数年以内に倍増が予想されています
watching more videos
動画の視聴数やスマホ販売数が
insights into the algorithm
ということは 人によっては
police departments are using big data
and sentencing recommendations
行なっています
of thousands of civilians in Pakistan
もたらした可能性があります
or to employment,
by the quantification bias.
is that we've come a long way
予測をするのに
to make predictions.
ずいぶん遠くまで来たことです
so let's just use them better.
より良く使いましょう
with the thick data.
シックデータと統合しましょう
with the oracles,
介添え人を呼んで来ましょう
in companies or nonprofits
行政であれ ソフトウェアであれ
we're collectively committed
私たちが力を合わせて全力で
より良い計算結果、より良い意思決定を
missing that something.
見落とさずに済むでしょう
ABOUT THE SPEAKER
Tricia Wang - Technology ethnographerWith astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers.
Why you should listen
For Tricia Wang, human behavior generates some of the most perplexing questions of our times. She has taught global organizations how to identify new customers and markets hidden behind their data, amplified IDEO's design thinking practice as an expert-in-residence, researched the social evolution of the Chinese internet, and written about the "elastic self," an emergent form of interaction in a virtual world. Wang is the co-founder of Sudden Compass, a consulting firm that helps companies unlock new growth opportunities by putting customer obsession into practice.
Wang's work has been featured in The Atlantic, Al Jazeera, and The Guardian. Fast Company spotlighted her work in China: "What Twitter Can Learn From Weibo: Field Notes From Global Tech Ethnographer Tricia Wang." In her latest op-ed on Slate, she discusses how attempts to stop terrorists on social media can harm our privacy and anonymity. Her Medium post, "Why Big Data Needs Thick Data," is a frequently cited industry piece on the importance of an integrated data approach. One of her favorite essays documents her day in the life of working as a street vendor in China.
Known for her lively presentations that are grounded in her research and observations about human behavior and data, Wang has spoken at organizations such as Proctor & Gamble, Nike, Wrigley, 21st Century Fox and Tumblr. Her most recent talk at Enterprise UX delved into why corporate innovation usually doesn’t work and what to do about it. She delivered the opening keynote at The Conference to a crowd of marketers and creatives, delving into the wild history of linear perspective and its influence on how we think and form organizations.
Wang holds affiliate positions at Data & Society, Harvard University's Berkman Klein Center for Internet Studies and New York University's Interactive Telecommunication Program. She oversees Ethnography Matters, a site that publishes articles about applied ethnography and technology. She co-started a Slack community for anyone who uses ethnographic methods in industry.
Wang began her career as a documentary filmmaker at NASA, an HIV/AIDS activist, and an educator specializing in culturally responsive pedagogy. She is also proud to have co-founded the first national hip-hop education initiative, which turned into the Hip Hop Education Center at New York University, and to have built after-school technology and arts programs for low-income youth at New York City public schools and the Queens Museum of Arts. Her life philosophy is that you have to go to the edge to discover what’s really happening. She's the proud companion of her internet famous dog, #ellethedog.
Tricia Wang | Speaker | TED.com