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Six Provocations of BIG DATA. Joe Howell • MCDM Cohort 12 Digital Democracy• August 11, 2012. IN 2000 THE WORLD GENERATED TWO EXABYTES OF NEW INFORMATION. Sources: “How Much Information?” Peter Lyman and Hal Varian, UC Berkeley, 2011 IDC Digital Universe Study. 2011.

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Six provocations of big data
Six Provocations ofBIG DATA

  • Joe Howell • MCDM Cohort 12

  • Digital Democracy• August 11, 2012


IN 2000 THE WORLD GENERATED

TWO EXABYTES

OF NEW INFORMATION

Sources: “How Much Information?” Peter Lyman and Hal Varian, UC Berkeley, 2011 IDC Digital Universe Study.


2011

IN 2000 THE WORLD GENERATED

TWO EXABYTES

OF NEW INFORMATION

5

EVERY DAY

Sources: “How Much Information?” Peter Lyman and Hal Varian, UC Berkeley, 2011 IDC Digital Universe Study.


  • big•data \ datasets so large theybreak traditionalIT infrastructures.


What s driving the data deluge
What’s Driving The Data Deluge?

Mobile Sensors

Social Media

Video Surveillance

Video Rendering

OilExploration

Smart Grids

Medical Imaging

Gene Sequencing

COST TO SEQUENCE

ONE GENOME

HAS FALLEN FROM $100M IN 2001

TO $10K IN 2011

OIL RIGS GENERATE

25000DATA POINTS PER SECOND

READING SMART METERSEVERY 15 MINUTES IS

3000X MORE

DATA INTENSIVE

FACEBOOK UPLOADS

300 MILLION

PHOTOS EACH DAY



Automating research changes the definition of knowledge
Automating Research Changes the Definition of Knowledge.

  • Big Data creates a radical shift in how we think about research.

  • Big Data provides ‘destabilizing amounts of knowledge & information that lack the regulating force of philosophy.

  • Big Data is about exactly right now, with no historical context that is predictive.

  • Google and other harvesters of Big Data might change the meaning of learning.


Claims to objectivity and accuracy are misleading
Claims to Objectivity and Accuracy are Misleading.

  • Big Data offers the humanistic disciplines a new way to claim the status of quantitative science.

  • Makes more social spaces quantifiable.

  • Big Data is subject to errors and researchers still have biases in interpretation.

  • Spectacular errors can emerge when researchers try to build social science findings into technological systems.



Not all data are equivalent
Not All Data Are Equivalent

1

  • Articulated Networks

2

Behavioral Networks

3

  • Personal Networks


Just because it s accessible doesn t make it ethical
Just Because It’s Accessible Doesn’t Make it Ethical.

  • What is the status of “public” data on social media sites?

  • Researchers cannot justify their actions as ethical just because data is accessible.

  • Are people who mine and analyze Big Data held to the same standard as academic researchers?



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