A Framework for Data Quality

Dworak-Fisher, Keenan; Mirel, Lisa B; Parker, Jennifer D; Popham, John; Prell, Mark; Schmitt, Rolf R.; Seastrom, Marilyn M; Young, Linda J; United States. Federal Committee on Statistical Methodology

doi:10.21949/1529866

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A Framework for Data Quality

2020-09-01
By Dworak-Fisher, Keenan ; Mirel, Lisa B ; Parker, Jennifer D ; ...

[PDF-1.06 MB]

English

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Creators:

Dworak-Fisher, Keenan ; Mirel, Lisa B ; Parker, Jennifer D ; Popham, John ; Prell, Mark ; ... More +

Dworak-Fisher, Keenan ; Mirel, Lisa B ; Parker, Jennifer D ; Popham, John ; Prell, Mark ; Schmitt, Rolf R. ; Seastrom, Marilyn M ; Young, Linda J Less -
Corporate Creators:

United States. Federal Committee on Statistical Methodology
Subject/TRT Terms:

[+]

Data Analysis Data Collection Data Quality Machine Learning
Publication/ Report Number:

FCSM-20-04
DOI:

https://doi.org/10.21949/1529866
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

United States. Federal Committee on Statistical Methodology
Abstract:

Efective understanding of data quality is essential for public officials, private businesses, and the public to make data-driven decisions. New sources of data, uses of existing data, analysis methods, and increasing reliance on integrating data from multiple sources bring new opportunities and challenges to federal agencies that provide information to support public and private decisions. Although new data sources and methods show great promise, the quality of these data must be evaluated. Inferior quality data can result in misleading information and poor decisions.
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PDF
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FCSM
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urn:sha256:aa75fa223354661db30abbdf8cdb3852505b6dcdab8f07ef829ecec2b2d7aecf
Download URL:

https://rosap.ntl.bts.gov/view/dot/72798/dot_72798_DS1.pdf
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A Framework for Data Quality

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