BIG DATA : Big Opportunity or Big Threat for Official Statistics?* Jose Ramon G. Albert, Ph.D. Secretary General, NSCB jrg.albert@nscb.gov.

Transcription

1 BIG DATA : Big Opportunity or Big Threat for Official Statistics?* Jose Ramon G. Albert, Ph.D. Secretary General, NSCB jrg.albert@nscb.gov.ph 1 *Views expressed do not reflect those at NSCB

2 Outline of the Presentation I. Introduction: Importance of Official Statistics in Public Policy II. Big Data is Here!!! III. Big Data: Big News or Big Mess? IV. Some Final Words on Big Data and Statistics (and Possible Ways Forward) 2

3 I. Introduction Official Statistics Importance for managing economies more effectively Inputs to monitor national development plans, roadmaps, targets, international commitments (MDGs, post MDG agenda) 3 You can t manage well what you don t measure Credibility : integrity, independence and professionalism UN Fundamental Principles on Official Statistics Critics find official statistics not sufficient: call for data revolution

4 I. Introduction Data Sources on Official Statistics BIG in volume surveys and censuses, administrative reporting systems but not big in frequency (i.e. velocity) Despite ICT tools, still not fast enough (due to costs, human resources, processes for reporting, including attention to precision and accuracy). Tried and Tested Methods for Collecting Data 4

5 II. BIG DATA is here!!! Electronic devices (mobile phones, smart phones, tablets, laptops), social media, google, sensors, tracking devices (GPS) 2.5 quintillion (2.5 x 1018) bytes of data created per day in 2012 More and more internet subscribers!!! (In PH, 36% in 2012 from 2% in 2000) More and more mobile subscribers!!! (102 per 100 persons in 2012 in PH) Timor-Leste Myanmar Cambodia Cambodia Indonesia Lao P.D.R. Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Percentage of Individuals Viet Nam using the Internet Mobile-cellular telephone subscriptions per 100 inhabitants Cambodia Indonesia Lao P.D.R. Malaysia Myanmar Philippines Singapore Thailand Timor-Leste Viet Nam

6 II. BIG DATA is here!!! Age of gadgets, social media and sensors Increasing Public Need for Knowing in (Real) Time Data is the new oil! - Andreas Weigend, Stanford (ex Amazon) Health Surveillance: Google Flu Trends (J. Ginsburg et al, Nature, 2009) 6

7 II. BIG DATA is here!!! Google Dengue Trends 7

8 II. BIG DATA is here!!! Beyond Health: Google Predicting the Present Predicting the Present with Google Trends (Choi & Varian, April 2009) 8

9 II. BIG DATA is here!!! Monitoring I Inflation and Traffic: UN Global Pulse reports of successes in Pulse Laboratory in Jakarta relating about rice on Twitter with actual price of rice (Letouze, 2012) 9

10 10 II. BIG DATA is here!!! DATA REVOLUTION in PH NSO using Google Maps to help re-design master sample of household surveys; NSCB extensively using web for dissemination (online articles, facebook, twitter, livestream) DOST s Project Nationwide Operational Assessment of Hazards (NOAH) helps government minimize climate disaster risks 676 deaths in CDO due to Sendong in death in CDO due to Pablo in 2012

11 II. BIG DATA is here!!! Importance of Information in Planning and Programming, especially Mitigating Risks from New Threats to Development (such as Impact of Climate Change on Climate Disasters) 11

12 III. Big Data: Big News or Big Mess? Official Statistics 1. Structured and planned product 2. Methodological and clear concepts Big Data 1. Largely unstructured unfiltered data exhaust, i.e., by-product of digital products (transactions, web, social media) 2. Poor analytics 3. Regulated 3. Unregulated 4. Macro-level but typically based on high volume primary data 4. Micro-level huge volume with high velocity (or frequency) and variety 5. High cost 5. Generally little, or no cost Centralized; point in time 6. Distributed; real-time

13 III. Big Data: Big News or Big Mess? A. Privacy Issues Much of Big Data being generated includes personal information. Precise, geo-location-based information pushes boundary of confidentiality/privacy. 13 Big Brother is watching you! - George Orwell, 1984 Amazon, Visa, Mastercard watching our shopping preferences Google watching our browsing habits Twitter watching what s on our minds Facebook watching various info, including our social relationships Mobile providers watching whom we talk to, what we say to them, and even who is nearby

14 III. Big Data: Big News or Big Mess? A. Privacy Issues Examples of Breach of Confidentiality/Privacy In 1943, US Census Bureau gave US govt block addresses (although not street names and numbers) of Japanese-Americans that led to having them imprisoned because of the US-Japan war Netherlands civil records used by Nazis to round up Jews Census data were used by BPS Statistics Indonesia to assist government in coming up with list of poor households 14

15 III. Big Data: Big News or Big Mess? A. Privacy Issues 15 Notice and Consent of Users Can users give informed consent to an unknown use? When Google Flu Trends was developed, did Google have contact all its users for approval to use old search queries for this project? Should users be asked to agree to any possible future use of their data? Other ways to protect privacy, but imperfect: Opting out (but this can leave a trace) Anonimization (but re-identification still possible)

16 III. Big Data: Big News or Big Mess? B. Big Bias 16 Gains in velocity (and cost) over sacrificing precision and accuracy, i.e. Big Data may not be completely accurate, but is thought of as good enough. But how good is good enough? Recent work suggests some over-estimation of Google Virus Trends of flu levels (11% in the US public this flu season, almost double the CDC s estimate of about 6%).

17 III. Big Data: Big News or Big Mess? B. Big Bias 17 A study of Twitter and Foursquare data before, during and in aftermath of Hurricane Sandy (Grinberg, et al., 2013) revealed: grocery shopping peaks the night before the storm) nightlife picked up the day after). Greatest number of tweets about Hurricane Sandy came from Manhattan. (This creates the illusion that Manhattan was the most hit in the US. It wasn t!)

18 III. Big Data: Big News or Big Mess? 18 B. Big Bias "[Big Data] is sometimes seen as a cure-all, as computers were in the 1970s. Chris Anderson wrote in 2008 that the sheer volume of data would obviate the need for theory, and even the scientific method. [T]hese views are badly mistaken. The numbers have no way of speaking for themselves. We speak for them.. If the quantity of information is increasing by 2.5 quintillion bytes per day, the amount of useful information almost certainly isn't. Most of it is just noise, and the noise is increasing faster than the signal. Nate Silver, The Silver and the Noise

19 III. Big Data: Big News or Big Mess? C. Predictive Analytics Gone Wild 19 Perilously Predicting Future Crime and Punishing Future Criminals ala movie Minority Report Parole boards in US using predictions from data analysis for parole decisions City of Memphis, Tennessee uses Blue CRUSH (Crime Reduction Utilizing Statistical History) to concentrate police resources in a specific area at a specific time. (Crimes fell by a quarter from CRUSH inception in 2006, but due to CRUSH???) US Dept of Homeland Security uses FAST (Fture Attribute Screening Technology) to identify potential terrorists (Reportedly 70% accurate??? )

20 20 IV. Final Words 1. Big Data is Here to Stay 2. The End of Official Statistics? Hardly! BUT 3. Possible Ways Forward Need for Legal Protocols and Institutional Arrangements for Access/Ownership Public-Private Partnerships / Investments on Data Addressing Privacy Issues with Big Data Investments for Capacity Building in the PSS and Partners to Harness Big Data Official Statistics community identifying signals within noise ; certifying quality; deciphering truth from falsehood PSS making use of non-traditional sources

21 Big Data and Official Statistics: Partners in Enabling Public to Know in (Real) Time /NSCBInfo 21