Tricia Wang: The human insights missing from big data
טרישה וואנג: תובנות אנושיות החסרות בביג דאטה
With astronaut eyes and ethnographer curiosity, Tricia Wang helps corporations grow by discovering the unknown about their customers. Full bio
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poets and politicians,
משורר או פוליטיקאי,
on life's most important questions,
בשאלות הכי חשובות בחיים,
advance into this territory?"
לתוך הטריטוריה הזאת?"
and you would get on your knees,
כורעים על הברכיים,
she would come out of it,
what's going to happen next.
to make the right decision.
את ההחלטה הנכונה.
knowing that we can make a decision
שאנחנו יכולים לקבל החלטה
or "deep learning" or "neural net."
"למידה לעומק" או "רשת עצבית".
we ask of our oracle now,
שואלים את האורקל שלנו כעת,
to ship these phones
לשלוח את הטלפונים האלה
with a genetic disorder?"
we can predict for this product?"
לחזות עבור המוצר הזה?"
and she hates the rain.
והיא שונאת את הגשם.
to untrain her.
an oracle, called Dark Sky,
שנקרא "שמיים אפלים",
in the next 10 minutes.
ב 10 דקות הקרובות.
our oracle is a $122 billion industry.
של 122 מיליארד דולר.
aren't even profitable,
ביג דאטה אינם רווחיים אפילו,
coming up to me saying,
better decisions.
with more breakthrough ideas."
of how people use technology,
בהם בטכנולוגיה,
not helping us make better decisions,
לקבל החלטות טובות יותר,
who have all these resources
a research position with Nokia.
cell phone companies in the world,
הגדולות ביותר בעולם,
like China, Mexico and India --
כמו סין, מקסיקו והודו --
a lot of research
משתמשים בטכנולוגיה.
as a street vendor
כמוכרת בדוכן רחוב,
in internet cafés,
so I could understand
games and mobile phones
from the rural areas to the cities.
that I was gathering,
among low-income Chinese people.
הסינים בעלי ההכנסה הנמוכה.
by advertisements for luxury products
who wouldn't want one? --
רוצה אחת כזאת?
the actually enticed them the most
into this high-tech life.
in urban slums like this one,
העוני העירוניות כמו זו,
over half of their monthly income
ממחצית משכורתם החודשית
of iPhones and other brands.
ומותגים אחרים.
with migrants and working with them
that they were doing,
all these data points together --
הנתונים האלה יחד --
like me selling dumplings,
כמו מכירת כופתאות על ידי,
on their cell phone bills.
this much more holistic picture
would want a smartphone,
to get their hands on one.
looking like iPhones.
להיראות כמו אייפונים.
and realistic people said,
these heavy things
and they break every time you drop them?"
ונשברים בכל פעם שמפילים אותם?"
about my insights,
to share them with Nokia.
millions of data points,
of anyone wanting to buy a smartphone,
שמישהו ירצה לקנות סמארטפון,
as diverse as it is, is too weak
על אף שהוא מגוון הוא חלש מדי,
assuming that people don't know
to get any data back
שום נתונים חזרה
a smartphone in two years.
בשנתיים הקרובות.
have been designed
at these emergent human dynamics
of missing something.
throwing out data all the time
it's our responsibility.
בביג דאטה; זאת האחריות שלנו.
very specific environments,
or delivery logistics or genetic code,
או קוד גנטי,
that are more or less contained.
פחות או יותר מוכלות.
are as neatly contained.
בצורה כל כך מסודרת.
and systems are more dynamic,
that involve human beings,
that we don't know how to model so well.
היטב כיצד ליצור דגם.
about human behavior,
are constantly changing.
enters the picture.
on big data alone
that we'll miss something,
that we already know everything.
שאנחנו כבר יודעים הכל.
to see this paradox
that I call the quantification bias,
שאני מכנה אותו ההטיה לכימות,
of valuing the measurable
colleagues who are like this,
company may be like this,
להיות כזאת,
so fixated on that number,
outside of it,
right in front of their face.
wrong with quantifying;
from looking at an Excel spreadsheet,
מהתבוננות בגיליון אלקטרוני של אקסל,
Everything is under control."
הכל תחת שליטה".
to kind of keep that in check,
as a numerical value.
into silver-bullet thinking,
for any organization,
the future we need to predict --
that's bearing down on us
the wrong decisions.
of ancient Greece
that shows us the path forward.
שמראה לנו את הדרך קדימה.
where the most famous oracle sat,
רוב האורקלים המפורסמים,
over two earthquake faults.
these petrochemical fumes
right above these faults,
of ethylene gas, these fissures.
של גז אתילן מסדקים אלה.
babble and hallucinate
any useful advice out of her
כלשהי ממנה
surrounding the oracle?
שמקיפים את האורקל?
on your left-hand side
with the oracle.
and get on their knees,
would get to work,
follow-up questions,
this prophecy? Who are you?
את הנבואה הזאת? מי אתה?
with this information?"
this more ethnographic,
היותר אתנוגרפי,
are huffing ethylene gas,
ביג דאטה שואפות גז אתילן,
invalid predictions.
that the oracle needed her temple guides,
נזקקה לכוהני המקדש שלה,
and user researchers
וחוקרי משתמשים
שאני קוראת לו "נתוני עובי."
that cannot be quantified.
שלא ניתנים לכימות.
that I collected for Nokia
of a very small sample size,
the human narrative.
what's missing in our models.
מה חסר במודלים שלנו.
in human questions,
העסקיות שלנו בנושאי שאלות אנושיות,
big and thick data
insights at scale
of machine intelligence,
rescue the context loss
להציל את אובדן ההקשר
of human intelligence.
that's when things get really fun,
הכיף ממש מתחיל,
just working with data
that hasn't been collected.
to transform their business.
לשנות את העסק שלהם.
recommendation algorithm,
ההמלצות המצוין שלהם,
for anyone who could improve it.
לכל מי שיוכל לשפר אותו.
the improvements were only incremental.
היו מזעריים בלבד.
Grant McCracken,
that they hadn't seen initially
to binge-watch.
בכמה פרקים של סדרות ברצף.
feel guilty about it.
"Oh. This is a new insight."
this big data insight
and validated it,
very simple but impactful.
פשוט מאוד, אך בעל השפעה.
the same show from different genres
אותה הסדרה בז'אנרים שונים
from similar users,
for you to binge-watch.
viewer experience,
for whole weekends at a time,
במשך סופי שבוע שלמים מדי פעם,
like "Master of None."
"מאסטר של אף אחד".
they not only improved their business,
הם לא רק שיפרו את העסק שלהם,
to double in the next few years.
יכפילו את ערכן בשנים הקרובות.
watching more videos
insights into the algorithm
נתוני-עובי לתוך האלגוריתם
police departments are using big data
משטרה משתמשים בביג דאטה
and sentencing recommendations
of thousands of civilians in Pakistan
במכשירים סלולריים
or to employment,
by the quantification bias.
is that we've come a long way
to make predictions.
so let's just use them better.
בהם יותר טוב.
with the thick data.
with the oracles,
יחד עם האורקלים,
in companies or nonprofits
we're collectively committed
missing that something.
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