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Big Data: Uses and Limitations
Nathaniel Schenker
Associate Director for Research and Methodology
National Center for Health Statistics
Centers for Disease Control and Prevention
Presentation for discussion at the meeting of the
NCHS Board of Scientific Counselors
September 19, 2013
2CONTENTS
Definitions of Big Data (or lack thereof)
Advantages and disadvantages of Big Data
Skills needed with Big Data
Current and potential uses of Big Data (not including administrative data) in the Federal Statistical SystemRobert Groves's COPAFS presentation
Some recent work at NCHS on blending data
Lessons learned from work at NCHS on blending dataCukier and Mayer-Schoenberger (2013)
Some Questions for Discussion
3Definitions of Big Data (or lack thereof)
Wikipedia: "Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on hand database management tools or traditional data processing applications."Horrigan (2013): "I view Big Data as nonsampled data, characterized by the creation of databases from electronic
sources whose primary purpose is something other than statistical inference." Rodriguez (2012): "For years, statisticians have been working with large volumes of data in fields as diverse as astronomy, bioinformatics, and data mining. Big Data is different because it is generated on a massive scale by countless online interactions among people, transactions between people and systems, and sensor-enabled machinery." 4Arbesman (2013, "Five myths about big data")
o Myth 1: "'Big data' has a clear definition." 5Advantages and disadvantages of Big Data
+ Big + Timely + Predictive (sometimes) + Cheap (?) - Unknown population representation - Issues of data quality - Typically not very multivariate (at the person level) - Privacy and confidentiality issues - Difficult to assess accuracy and uncertainty 6Skills needed with Big Data
(Rodriguez 2012)Management and processing of distributed data
New tools for data analysis and visualization
oE.g., unstructured text data
7 Current and potential uses of Big Data (not including administrative data) in the Federal Statistical SystemCurrent
oBureau of Labor Statistics (Horrigan 2013)
Web scraping to obtain prices for various goods and services Use of retail scanner data in research on distributions of items within expenditure classes 8Potential
o NCHS:EHRs; pilot tests in National Health Care Surveys
(http://www.cdc.gov/nchs/dhcs.htm) oBureau of Labor Statistics (Horrigan 2013)
Replacement of traditional data collection from
establishments by corporate data from parent company oBureau of Economic Analysis (Lohr 2013)
Use of data from Intuit on small businesses for national income accounting oCensus Bureau (Capps and Wright 2013)
Auxiliary data for stratification, improving survey estimates, compensating for nonresponse, small-area estimation, ...Helping to check estimates
More timely, preliminary estimates (to be revised using survey data) 9Robert Groves's COPAFS presentation
(COPAFS 2013) Two extreme approaches one could take with respect to Big Data1. Replace existing measures with big data indicators
o Tools becoming available, but there are issues of:Quality; e.g., coverage error
Inability to examine subgroups due to lack of
multivariate nature2. Assume that the present system will endure and win out
over Big Data oPerhaps optimal now, but what happens when big data become so prevalent and are used so widely in business that they cannot be ignored?
Groves's view: Traditional survey data (although challenged) are not going away, andBig Data are too powerful to ignore
o Only choice: pursue path of blending Big Data and survey data 10Some recent work at NCHS on blending data
Combining information from complementary surveys (the National Health Interview Survey and the National Nursing Home Survey) to extend coverage (Schenker, Gentleman,Rose, Hing, and Shimizu 2002)
o Big Data analogue: Combining Big Data with data from a smaller survey to adjust for non-coverage in Big Data Combining information from the Behavioral Risk Factor Surveillance System and the National Health Interview Survey via Bayesian modeling for small-area estimation (http://sae.cancer.gov) o Big Data analogue: Combining Big Data with data from a smaller survey to adjust for nonresponse and non -coverage in Big Data via modeling 11 Bridging the transition from single-race reporting to multiple- race reporting in the census using information from theNational Health Inte
rview Survey oBig data analogue: Adjusting for a change in reporting systems for Big Data using information from a smaller
survey with data collected under both reporting systems Enhancing the scientific value of surveys by linking their data with administrative and other data o Big Data analogue: Linking survey data with Big Data Probably more feasible at area level than at person level New project, joint with the Census Bureau: Using information from the American Community Survey to create predictors in small -area estimation for outcomes measured in NCHS surveys o Big Data analogue: Using local summaries of Big Data as predictors in small-area estimation 12