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TED Talks Daily

The human insights missing from big data | Tricia Wang

TED Talks Daily

TED

Creativity, Ted Podcast, Ted Talks Daily, Business, Design, Inspiration, Society & Culture, Science, Technology, Education, Tech Demo, Ted Talks, Ted, Entertainment, Tedtalks

4.111.9K Ratings

🗓️ 19 July 2017

⏱️ 16 minutes

🧾️ Download transcript

Summary

Why do so many companies make bad decisions, even with access to unprecedented amounts of data? With stories from Nokia to Netflix to the oracles of ancient Greece, Tricia Wang demystifies big data and identifies its pitfalls, suggesting that we focus instead on "thick data" -- precious, unquantifiable insights from actual people -- to make the right business decisions and thrive in the unknown.

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Transcript

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0:00.0

This TED Talk features technology ethnographer Trisha Wang, recorded live at TEDx Cambridge 2016.

0:09.3

In ancient Greece, when anyone from slaves, soldiers, poets, and politicians needed to make a big

0:17.4

decision on life's most important questions. Like, should I get married, or should we

0:22.1

embark on this voyage, or should our army advance into this territory? They all consulted the

0:28.2

Oracle. So this is how it worked. You would bring her question and you would get on your knees,

0:33.9

and then she would go into this trance. It would take a couple of days, and then eventually she would come out of it,

0:39.3

giving you her predictions as your answer.

0:43.3

From the Oracle bones of ancient China to ancient Greece to Mayan calendars,

0:48.3

people have craved for prophecy in order to find out what's going to happen next.

0:58.0

And that's because we all want to make the right decision. We don't want to miss something.

1:00.0

The future is scary,

1:02.0

so it's much nicer knowing that we can make a decision

1:05.0

with some assurance of the outcome.

1:07.0

Well, we have a new Oracle, and its name is Big Data.

1:11.6

Or we call it Watson or deep learning or neural nets.

1:15.6

And these are the kind of questions we ask of our Oracle now.

1:19.6

Like, you know, what's the most efficient way to ship these phones from China to Sweden?

1:25.6

Or what are the odds of my child being born with a genetic disorder?

1:31.7

Or what are the sales volume who can predict for this product?

1:36.6

I have a dog, her name is Elle, and she hates the rain.

1:41.0

And I have tried everything to untrain her,

1:46.8

but because I have failed at this,

...

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