SlideShare uma empresa Scribd logo
1 de 32
Baixar para ler offline
T H E C O M I N G W E B O F D ATA
O P E N D A TA I S T H E N E X T U T I L I T Y
The House of Representatives shall be composed of Members chosen
every second Year by the People of the several States, and the
Electors in each State shall have the Qualifications requisite for
Electors of the most numerous Branch of the State Legislature.
No Person shall be a Representative who shall not have attained to the
Age of twenty five Years, and been seven Years a Citizen of the United
States, and who shall not, when elected, be an Inhabitant of that State
in which he shall be chosen.
Representatives and direct Taxes shall be apportioned among the
several States which may be included within this Union, according to
their respective Numbers, which shall be determined by adding to the
whole Number of free Persons, including those bound to Service for a
Term of Years, and excluding Indians not taxed, three fifths of all other
Persons. The actual Enumeration shall be made within three Years
after the first Meeting of the Congress of the United States, and within
every subsequent Term of ten Years, in such Manner as they shall by
Law direct. The Number of Representatives shall not exceed one for
every thirty Thousand, but each State shall have at Least one
Article 1 Section 2
Representatives and direct Taxes shall be apportioned among the
several States which may be included within this Union, according to
their respective Numbers, which shall be determined by adding to the
whole Number of free Persons, including those bound to Service for a
Term of Years, and excluding Indians not taxed, three fifths of all other
Persons. The actual Enumeration shall be made within three Years
after the first Meeting of the Congress of the United States, and within
every subsequent Term of ten Years, in such Manner as they shall by
Law direct.
US Census
N U M Y E A R D AT E TA K E N P O P U L AT I O N
1 1 7 9 0 A U G U S T 2 , 1 7 9 0 3 , 9 2 9 , 3 2 6
2 1 8 0 0 A U G U S T 4 , 1 8 0 0 5 , 3 0 8 , 4 8 3
3 1 8 1 0 A U G U S T 6 , 1 8 1 0 7 , 2 3 9 , 8 8 1
4 1 8 2 0 A U G U S T 7 , 1 8 2 0 9 , 6 3 8 , 4 5 3
5 1 8 3 0 J U N E 1 , 1 8 3 0 1 2 , 8 6 6 , 0 2 0
6 1 8 4 0 J U N E 1 , 1 8 4 0 1 7 , 0 6 9 , 4 5 3
7 1 8 5 0 J U N E 1 , 1 8 5 0 2 3 , 1 9 1 , 8 7 6
8 1 8 6 0 J U N E 1 , 1 8 6 0 3 1 , 4 4 3 , 3 2 1
9 1 8 7 0 J U N E 1 , 1 8 7 0 3 9 , 8 1 8 , 4 4 9
1 0 1 8 8 0 J U N E 1 , 1 8 8 0 5 0 , 1 8 9 , 2 0 9
1 1 1 8 9 0 J U N E 2 , 1 8 9 0 6 2 , 9 4 7 , 7 1 4
The 1880 Census took SEVEN years to complete
The 1890 Census took 2.5 years to complete
Computing-Tabulating-Recording Company
“We’re on the cusp of another data-
driven revolution.”
NYC’s OPEN DATA LAW
Mar 7, 2012
O P E N D ATA
Making Open and Machine
Readable the New Default
for Government Information
- May 9, 2013
A P R I L 1 0 , 2 0 1 4
T W O O P E N - D ATA D R I V E N G O A L S 

O F T H E W O R L D B A N K
• End extreme poverty by 2030
• Promote shared prosperity for the poorest 40% in
developing countries
WHAT HAPPENS WHEN BILLIONS OF PEOPLE 

ARE LIFTED FROM POVERTY?
WHAT ECONOMIC EFFECTS WILL IT BRING ABOUT?
130
2,720 7,9102005
2012
2015
EXABYTE	
  =	
  1,000,000,000,000,000,000
Global	
  Data	
  Volume
Global	
  Data	
  Volume
2005
2012
2015
EXABYTE	
  =	
  1,000,000,000,000,000,000
130
2,720 7,910
Global	
  Data	
  Volume
20
90,000
Global	
  Data	
  Volume
2005
2012
2015
130
2,720 7,910
90,000
2020
90	
  ZETTABYTES
The State is the
biggest generator,
collector and user of
Data.
If	
  an	
  11	
  oz	
  tea	
  cup	
  =	
  1	
  gigabyte
The	
  Great	
  Wall	
  of	
  China	
  =	
  1	
  ZeDabyte
RAW DATA
EVERYWHERE
Opendata:UnlockinginnovationandperformancewithliquidinformationMcKinseyGlobalInstitute
McKinsey Global Institute
McKinsey Center for Government
Open data: Unlocking
innovation and performance
with liquid information
October 2013
Open data can help unlock $3.2 trillion to $5.4 trillion in economic value
per year across seven “domains”
Exhibit E3
SOURCE: McKinsey Global Institute analysis
NOTE: Numbers may not sum due to rounding.
$ billion
3,220–5,390Total
Consumer finance
Five domains
210–280
Health care1 300–450
Consumer products
2,710–4,660
Oil and gas 240–510
Electricity 340–580
520–1,470
Transportation 720–920
Education 890–1,180
Values represent examples of
open data potential, not
comprehensive sizing of
potential value across sectors
1 Includes US values only.
P U B L I C S E C T O R D ATA H A S F U E L E D
O T H E R I N D U S T R I E S
• Public Weather Data - weather industry
• Human Genome - biotech industry
• GPS - location industry
What will you build with Open Data 

subsuming everything else?
Y O U ’ R E B L A Z I N G T H E T R A I L
• Cultural Shift for Public Sector IT - to Open by Default
• Its happening by law (compliance)
• Changing how Government works is Hard!
!
Why not build something outside Government,

as an Entrepreneur to fix it?
March 12, 2014
25th Anniversary of World Wide Web
Sir Tim Berners-Lee, Inventor of the WWW
TBL’s “Vague but exciting…” proposal
31
BACKGROUND
London Olympics 2012 Opening Ceremony	

Tim Berners-Lee
In 25 years, we’ve built a
Web of Documents,
AND CHANGED THE WORLD!


What will the next 25 years bring
when we build the
Web of Data?

Mais conteúdo relacionado

Destaque

Destaque (10)

NYCBigApps 2013 Expo/Hackathon Talk
NYCBigApps 2013 Expo/Hackathon TalkNYCBigApps 2013 Expo/Hackathon Talk
NYCBigApps 2013 Expo/Hackathon Talk
 
NYCFacets: Metadata, Extrametadata and Crowdknowing
NYCFacets: Metadata, Extrametadata and CrowdknowingNYCFacets: Metadata, Extrametadata and Crowdknowing
NYCFacets: Metadata, Extrametadata and Crowdknowing
 
NYC Remapped
NYC RemappedNYC Remapped
NYC Remapped
 
Effortless Hr Offering Presentation
Effortless Hr Offering PresentationEffortless Hr Offering Presentation
Effortless Hr Offering Presentation
 
Microsoft word
Microsoft wordMicrosoft word
Microsoft word
 
NYC Data Web (static version) - A Semantic, Open Public Data Exchange for NYC
NYC Data Web (static version) - A Semantic, Open Public Data Exchange for NYCNYC Data Web (static version) - A Semantic, Open Public Data Exchange for NYC
NYC Data Web (static version) - A Semantic, Open Public Data Exchange for NYC
 
Practica word
Practica wordPractica word
Practica word
 
Ejercicios practicos de excel ii
Ejercicios practicos de excel iiEjercicios practicos de excel ii
Ejercicios practicos de excel ii
 
Raw data in, Insights out - CKANcon 2015
Raw data in, Insights out - CKANcon 2015Raw data in, Insights out - CKANcon 2015
Raw data in, Insights out - CKANcon 2015
 
Open source in government
Open source in governmentOpen source in government
Open source in government
 

Semelhante a The Coming Web of Data

THE RISE & FALL OF THE GDPSTUDY QUESTIONS - MGMT 4231. Wh.docx
THE RISE & FALL OF THE GDPSTUDY QUESTIONS   -   MGMT 4231.  Wh.docxTHE RISE & FALL OF THE GDPSTUDY QUESTIONS   -   MGMT 4231.  Wh.docx
THE RISE & FALL OF THE GDPSTUDY QUESTIONS - MGMT 4231. Wh.docx
ssusera34210
 
3519, 9(47 AMLabor and Capital in the Global Economy Democ.docx
3519, 9(47 AMLabor and Capital in the Global Economy  Democ.docx3519, 9(47 AMLabor and Capital in the Global Economy  Democ.docx
3519, 9(47 AMLabor and Capital in the Global Economy Democ.docx
lorainedeserre
 

Semelhante a The Coming Web of Data (20)

Un population and vital statistics report
Un population and vital statistics reportUn population and vital statistics report
Un population and vital statistics report
 
Population and Vital Statistics Report
Population and Vital Statistics ReportPopulation and Vital Statistics Report
Population and Vital Statistics Report
 
Turbulent Times: Our economic prospects in an uncertain world
Turbulent Times: Our economic prospects in an uncertain worldTurbulent Times: Our economic prospects in an uncertain world
Turbulent Times: Our economic prospects in an uncertain world
 
Ielts writing task1 samples hocielts
Ielts writing task1 samples hocieltsIelts writing task1 samples hocielts
Ielts writing task1 samples hocielts
 
Mercer Capital's Value Focus: Laboratory Services | Mid-Year 2016
Mercer Capital's Value Focus: Laboratory Services | Mid-Year 2016Mercer Capital's Value Focus: Laboratory Services | Mid-Year 2016
Mercer Capital's Value Focus: Laboratory Services | Mid-Year 2016
 
Silicon Valley Housing and Transportation Crisis
Silicon Valley Housing and Transportation CrisisSilicon Valley Housing and Transportation Crisis
Silicon Valley Housing and Transportation Crisis
 
A Modern Housing Crisis
A Modern Housing CrisisA Modern Housing Crisis
A Modern Housing Crisis
 
January2010marketupdateseminar 100120082607 Phpapp01
January2010marketupdateseminar 100120082607 Phpapp01January2010marketupdateseminar 100120082607 Phpapp01
January2010marketupdateseminar 100120082607 Phpapp01
 
January 2010 Market Update Seminar
January 2010 Market Update SeminarJanuary 2010 Market Update Seminar
January 2010 Market Update Seminar
 
THE RISE & FALL OF THE GDPSTUDY QUESTIONS - MGMT 4231. Wh.docx
THE RISE & FALL OF THE GDPSTUDY QUESTIONS   -   MGMT 4231.  Wh.docxTHE RISE & FALL OF THE GDPSTUDY QUESTIONS   -   MGMT 4231.  Wh.docx
THE RISE & FALL OF THE GDPSTUDY QUESTIONS - MGMT 4231. Wh.docx
 
The jobs boom: How has our employment surge changed Britain?
The jobs boom: How has our employment surge changed Britain?The jobs boom: How has our employment surge changed Britain?
The jobs boom: How has our employment surge changed Britain?
 
Using Apache Spark and Differential Privacy for Protecting the Privacy of the...
Using Apache Spark and Differential Privacy for Protecting the Privacy of the...Using Apache Spark and Differential Privacy for Protecting the Privacy of the...
Using Apache Spark and Differential Privacy for Protecting the Privacy of the...
 
3519, 9(47 AMLabor and Capital in the Global Economy Democ.docx
3519, 9(47 AMLabor and Capital in the Global Economy  Democ.docx3519, 9(47 AMLabor and Capital in the Global Economy  Democ.docx
3519, 9(47 AMLabor and Capital in the Global Economy Democ.docx
 
Arbor US Economic Overview 2018 Q1
Arbor US Economic Overview 2018 Q1Arbor US Economic Overview 2018 Q1
Arbor US Economic Overview 2018 Q1
 
US Government
US Government US Government
US Government
 
Spatial Patterns of Urban Innovation and Productivity
Spatial Patterns of Urban Innovation and ProductivitySpatial Patterns of Urban Innovation and Productivity
Spatial Patterns of Urban Innovation and Productivity
 
Essay On Importance Of Physical Education
Essay On Importance Of Physical EducationEssay On Importance Of Physical Education
Essay On Importance Of Physical Education
 
Democracy In The UK Essay
Democracy In The UK EssayDemocracy In The UK Essay
Democracy In The UK Essay
 
Vieslekcija Latvijas Universitātē: Kā D. Trampa prezidentūra ietekmē Eiropu? ...
Vieslekcija Latvijas Universitātē: Kā D. Trampa prezidentūra ietekmē Eiropu? ...Vieslekcija Latvijas Universitātē: Kā D. Trampa prezidentūra ietekmē Eiropu? ...
Vieslekcija Latvijas Universitātē: Kā D. Trampa prezidentūra ietekmē Eiropu? ...
 
US Economic Overview | Q3 2019
US Economic Overview | Q3 2019US Economic Overview | Q3 2019
US Economic Overview | Q3 2019
 

Último

📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
@Chandigarh #call #Girls 9053900678 @Call #Girls in @Punjab 9053900678
 
Lucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRL
Lucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRLLucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRL
Lucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRL
imonikaupta
 
Call Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort ServiceCall Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
9953056974 Low Rate Call Girls In Saket, Delhi NCR
 
valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...
valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...
valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...
Call Girls In Delhi Whatsup 9873940964 Enjoy Unlimited Pleasure
 

Último (20)

Al Barsha Night Partner +0567686026 Call Girls Dubai
Al Barsha Night Partner +0567686026 Call Girls  DubaiAl Barsha Night Partner +0567686026 Call Girls  Dubai
Al Barsha Night Partner +0567686026 Call Girls Dubai
 
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
📱Dehradun Call Girls Service 📱☎️ +91'905,3900,678 ☎️📱 Call Girls In Dehradun 📱
 
Real Escorts in Al Nahda +971524965298 Dubai Escorts Service
Real Escorts in Al Nahda +971524965298 Dubai Escorts ServiceReal Escorts in Al Nahda +971524965298 Dubai Escorts Service
Real Escorts in Al Nahda +971524965298 Dubai Escorts Service
 
2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs
2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs
2nd Solid Symposium: Solid Pods vs Personal Knowledge Graphs
 
(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7
(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7
(INDIRA) Call Girl Pune Call Now 8250077686 Pune Escorts 24x7
 
Call Now ☎ 8264348440 !! Call Girls in Green Park Escort Service Delhi N.C.R.
Call Now ☎ 8264348440 !! Call Girls in Green Park Escort Service Delhi N.C.R.Call Now ☎ 8264348440 !! Call Girls in Green Park Escort Service Delhi N.C.R.
Call Now ☎ 8264348440 !! Call Girls in Green Park Escort Service Delhi N.C.R.
 
VIP Model Call Girls Hadapsar ( Pune ) Call ON 9905417584 Starting High Prof...
VIP Model Call Girls Hadapsar ( Pune ) Call ON 9905417584 Starting  High Prof...VIP Model Call Girls Hadapsar ( Pune ) Call ON 9905417584 Starting  High Prof...
VIP Model Call Girls Hadapsar ( Pune ) Call ON 9905417584 Starting High Prof...
 
Busty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort Service
Busty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort ServiceBusty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort Service
Busty Desi⚡Call Girls in Vasundhara Ghaziabad >༒8448380779 Escort Service
 
Top Rated Pune Call Girls Daund ⟟ 6297143586 ⟟ Call Me For Genuine Sex Servi...
Top Rated  Pune Call Girls Daund ⟟ 6297143586 ⟟ Call Me For Genuine Sex Servi...Top Rated  Pune Call Girls Daund ⟟ 6297143586 ⟟ Call Me For Genuine Sex Servi...
Top Rated Pune Call Girls Daund ⟟ 6297143586 ⟟ Call Me For Genuine Sex Servi...
 
Lucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRL
Lucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRLLucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRL
Lucknow ❤CALL GIRL 88759*99948 ❤CALL GIRLS IN Lucknow ESCORT SERVICE❤CALL GIRL
 
Call Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort ServiceCall Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
Call Girls in Prashant Vihar, Delhi 💯 Call Us 🔝9953056974 🔝 Escort Service
 
Yerawada ] Independent Escorts in Pune - Book 8005736733 Call Girls Available...
Yerawada ] Independent Escorts in Pune - Book 8005736733 Call Girls Available...Yerawada ] Independent Escorts in Pune - Book 8005736733 Call Girls Available...
Yerawada ] Independent Escorts in Pune - Book 8005736733 Call Girls Available...
 
valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...
valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...
valsad Escorts Service ☎️ 6378878445 ( Sakshi Sinha ) High Profile Call Girls...
 
Dubai=Desi Dubai Call Girls O525547819 Outdoor Call Girls Dubai
Dubai=Desi Dubai Call Girls O525547819 Outdoor Call Girls DubaiDubai=Desi Dubai Call Girls O525547819 Outdoor Call Girls Dubai
Dubai=Desi Dubai Call Girls O525547819 Outdoor Call Girls Dubai
 
Russian Call Girls Pune (Adult Only) 8005736733 Escort Service 24x7 Cash Pay...
Russian Call Girls Pune  (Adult Only) 8005736733 Escort Service 24x7 Cash Pay...Russian Call Girls Pune  (Adult Only) 8005736733 Escort Service 24x7 Cash Pay...
Russian Call Girls Pune (Adult Only) 8005736733 Escort Service 24x7 Cash Pay...
 
Shikrapur - Call Girls in Pune Neha 8005736733 | 100% Gennuine High Class Ind...
Shikrapur - Call Girls in Pune Neha 8005736733 | 100% Gennuine High Class Ind...Shikrapur - Call Girls in Pune Neha 8005736733 | 100% Gennuine High Class Ind...
Shikrapur - Call Girls in Pune Neha 8005736733 | 100% Gennuine High Class Ind...
 
(+971568250507 ))# Young Call Girls in Ajman By Pakistani Call Girls in ...
(+971568250507  ))#  Young Call Girls  in Ajman  By Pakistani Call Girls  in ...(+971568250507  ))#  Young Call Girls  in Ajman  By Pakistani Call Girls  in ...
(+971568250507 ))# Young Call Girls in Ajman By Pakistani Call Girls in ...
 
VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...
VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...
VVIP Pune Call Girls Sinhagad WhatSapp Number 8005736733 With Elite Staff And...
 
Wagholi & High Class Call Girls Pune Neha 8005736733 | 100% Gennuine High Cla...
Wagholi & High Class Call Girls Pune Neha 8005736733 | 100% Gennuine High Cla...Wagholi & High Class Call Girls Pune Neha 8005736733 | 100% Gennuine High Cla...
Wagholi & High Class Call Girls Pune Neha 8005736733 | 100% Gennuine High Cla...
 
WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)
WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)
WhatsApp 📞 8448380779 ✅Call Girls In Mamura Sector 66 ( Noida)
 

The Coming Web of Data

  • 1. T H E C O M I N G W E B O F D ATA O P E N D A TA I S T H E N E X T U T I L I T Y
  • 2.
  • 3.
  • 4. The House of Representatives shall be composed of Members chosen every second Year by the People of the several States, and the Electors in each State shall have the Qualifications requisite for Electors of the most numerous Branch of the State Legislature. No Person shall be a Representative who shall not have attained to the Age of twenty five Years, and been seven Years a Citizen of the United States, and who shall not, when elected, be an Inhabitant of that State in which he shall be chosen. Representatives and direct Taxes shall be apportioned among the several States which may be included within this Union, according to their respective Numbers, which shall be determined by adding to the whole Number of free Persons, including those bound to Service for a Term of Years, and excluding Indians not taxed, three fifths of all other Persons. The actual Enumeration shall be made within three Years after the first Meeting of the Congress of the United States, and within every subsequent Term of ten Years, in such Manner as they shall by Law direct. The Number of Representatives shall not exceed one for every thirty Thousand, but each State shall have at Least one Article 1 Section 2
  • 5. Representatives and direct Taxes shall be apportioned among the several States which may be included within this Union, according to their respective Numbers, which shall be determined by adding to the whole Number of free Persons, including those bound to Service for a Term of Years, and excluding Indians not taxed, three fifths of all other Persons. The actual Enumeration shall be made within three Years after the first Meeting of the Congress of the United States, and within every subsequent Term of ten Years, in such Manner as they shall by Law direct.
  • 7. N U M Y E A R D AT E TA K E N P O P U L AT I O N 1 1 7 9 0 A U G U S T 2 , 1 7 9 0 3 , 9 2 9 , 3 2 6 2 1 8 0 0 A U G U S T 4 , 1 8 0 0 5 , 3 0 8 , 4 8 3 3 1 8 1 0 A U G U S T 6 , 1 8 1 0 7 , 2 3 9 , 8 8 1 4 1 8 2 0 A U G U S T 7 , 1 8 2 0 9 , 6 3 8 , 4 5 3 5 1 8 3 0 J U N E 1 , 1 8 3 0 1 2 , 8 6 6 , 0 2 0 6 1 8 4 0 J U N E 1 , 1 8 4 0 1 7 , 0 6 9 , 4 5 3 7 1 8 5 0 J U N E 1 , 1 8 5 0 2 3 , 1 9 1 , 8 7 6 8 1 8 6 0 J U N E 1 , 1 8 6 0 3 1 , 4 4 3 , 3 2 1 9 1 8 7 0 J U N E 1 , 1 8 7 0 3 9 , 8 1 8 , 4 4 9 1 0 1 8 8 0 J U N E 1 , 1 8 8 0 5 0 , 1 8 9 , 2 0 9 1 1 1 8 9 0 J U N E 2 , 1 8 9 0 6 2 , 9 4 7 , 7 1 4
  • 8. The 1880 Census took SEVEN years to complete
  • 9.
  • 10. The 1890 Census took 2.5 years to complete
  • 12.
  • 13. “We’re on the cusp of another data- driven revolution.”
  • 14. NYC’s OPEN DATA LAW Mar 7, 2012
  • 15. O P E N D ATA Making Open and Machine Readable the New Default for Government Information - May 9, 2013
  • 16. A P R I L 1 0 , 2 0 1 4
  • 17. T W O O P E N - D ATA D R I V E N G O A L S 
 O F T H E W O R L D B A N K • End extreme poverty by 2030 • Promote shared prosperity for the poorest 40% in developing countries WHAT HAPPENS WHEN BILLIONS OF PEOPLE 
 ARE LIFTED FROM POVERTY? WHAT ECONOMIC EFFECTS WILL IT BRING ABOUT?
  • 18. 130 2,720 7,9102005 2012 2015 EXABYTE  =  1,000,000,000,000,000,000 Global  Data  Volume
  • 19. Global  Data  Volume 2005 2012 2015 EXABYTE  =  1,000,000,000,000,000,000 130 2,720 7,910
  • 21. Global  Data  Volume 2005 2012 2015 130 2,720 7,910 90,000 2020 90  ZETTABYTES The State is the biggest generator, collector and user of Data.
  • 22. If  an  11  oz  tea  cup  =  1  gigabyte
  • 23. The  Great  Wall  of  China  =  1  ZeDabyte
  • 25. Opendata:UnlockinginnovationandperformancewithliquidinformationMcKinseyGlobalInstitute McKinsey Global Institute McKinsey Center for Government Open data: Unlocking innovation and performance with liquid information October 2013
  • 26. Open data can help unlock $3.2 trillion to $5.4 trillion in economic value per year across seven “domains” Exhibit E3 SOURCE: McKinsey Global Institute analysis NOTE: Numbers may not sum due to rounding. $ billion 3,220–5,390Total Consumer finance Five domains 210–280 Health care1 300–450 Consumer products 2,710–4,660 Oil and gas 240–510 Electricity 340–580 520–1,470 Transportation 720–920 Education 890–1,180 Values represent examples of open data potential, not comprehensive sizing of potential value across sectors 1 Includes US values only.
  • 27. P U B L I C S E C T O R D ATA H A S F U E L E D O T H E R I N D U S T R I E S • Public Weather Data - weather industry • Human Genome - biotech industry • GPS - location industry What will you build with Open Data 
 subsuming everything else?
  • 28. Y O U ’ R E B L A Z I N G T H E T R A I L • Cultural Shift for Public Sector IT - to Open by Default • Its happening by law (compliance) • Changing how Government works is Hard! ! Why not build something outside Government,
 as an Entrepreneur to fix it?
  • 29. March 12, 2014 25th Anniversary of World Wide Web Sir Tim Berners-Lee, Inventor of the WWW
  • 30. TBL’s “Vague but exciting…” proposal
  • 31. 31 BACKGROUND London Olympics 2012 Opening Ceremony Tim Berners-Lee
  • 32. In 25 years, we’ve built a Web of Documents, AND CHANGED THE WORLD! 
 What will the next 25 years bring when we build the Web of Data?