A Framework for Data Quality: Case Studies

Mirel, Lisa B; Singpurwalla, Darius; Hoppe, Travis; Schmitt, Rolf; Weber, Julie; Liliedahl, Erika; United States, Federal Committee on Statistical Methodology. Data Quality Framework Implementation Subcommittee

doi:10.21949/1529869

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A Framework for Data Quality: Case Studies

2023-10-01
By Mirel, Lisa B ; Singpurwalla, Darius ; Hoppe, Travis ; ...

[PDF-787.19 KB]

English

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Mirel, Lisa B ; Singpurwalla, Darius ; Hoppe, Travis ; Schmitt, Rolf ; Weber, Julie ; ... More +

Mirel, Lisa B ; Singpurwalla, Darius ; Hoppe, Travis ; Schmitt, Rolf ; Weber, Julie ; Liliedahl, Erika Less -
Corporate Creators:

United States, Federal Committee on Statistical Methodology. Data Quality Framework Implementation Subcommittee
Subject/TRT Terms:

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Case Studies Data Analysis Data Collection Data Quality Machine Learning
DOI:

https://doi.org/10.21949/1529869
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

United States. Federal Committee on Statistical Methodology
Abstract:

The Federal Committee on Statistical Methodology (FCSM) released A Framework for Data Quality in September 2020 to provide a common foundation upon which federal agencies can make informed decisions about the quality of data products and their management throughout their life cycle. The framework introduces data program managers and analysts—including statisticians, chief data officers, and evaluation officers—to a broad range of quality considerations and provides a common, systematic language for communicating data quality issues and methods for resolving or accepting those issues. The framework includes quality issues beyond bias and accuracy, the traditional focus of statisticians, and encompasses data from sources including surveys, administrative records, monitors, and blended data.
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PDF
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FCSM
Main Document Checksum:

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urn:sha256:5ae28236a38adad46e7fed572636fde424c078dcd6a37fa67969b2c839adc242
Download URL:

https://rosap.ntl.bts.gov/view/dot/72804/dot_72804_DS1.pdf
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A Framework for Data Quality: Case Studies

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