Unique ID: 2015097

Division: Office of Information and Regulatory Affairs
Issue Date: February 13th 2019
Last modified: February 22nd 2019

U.S. household food purchases and retail food sales

Using retail sales data for household food purchase information

Data purchased from IRI are used in food economics research, seven years of U.S. coverage, with location and date. Retail sales data total over 40 billion records.

Project Objective:

Scientific / research

Project Outcomes:

Journal articles, research reports, summary data.

Publications Comments:

Research reports and journal articles.

Statistical Area


Project Sources
Project Sources
Type Of Institution: National statistical office
Big Data Source: Scanner data, Health records
Region: North America
Country Area: United States
Id Country Regional: country
Data Providers: Intermediary Big Data provider
Other Partners: Academic institute
Accessing Data
Accessing Data
Data Access Rights: Only for this project
Intermediary Comments: Private U.S. company IRI sells the data under a custom agreement. University of Chicago's National Opinion Research Center (NORC) hosts the data and software and provides access to approved research collaborators.
Data Access Comments: Many research projects are under an umbrella agreement regarding appropriate access and disclosure.
Data Coverage
Data Coverage
Data Coverage: Only a portion of all data
Coverage Geo Pop: Whole country / high % of market
Cost Implication: Commercial
Cost Comments: Data purchased from private market insights vendor.
Coverage Period: 2008-2014
Data Quality
Data Quality
Quality Framework: Quality of source/input
Quality Aspects Evaluated: Privacy and Security, Accessibility, Relevance, Institutional/Business Environment, Validity, Accuracy, including selectivity, Coherence, including linkability to other sources
Validation Comments: Summary data are compared to results from other sources and projects, and detail data are compared to other source data.
Quality Framework Comments: The evaluation process includes both quantitative and qualitative components.
Data Quality Concerns Comments: Dozens of checks are run to identify data issues, including gaps, duplicates, outliers, and various anomalies.
Methods Used: Traditional statistical methods
Technologies: Column store database, Spreadsheet, Other, Hadoop Clusters
Technologies Comments: SAS, Stata
Income Level: High-income
Iso: US
Timeframe To Produce Indicator: NA
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