DATA ISSUES data collection, validating, other
The UN has the National Quality Assurance Frameworks http://unstats.un.org/unsd/dnss/qualityNQAF/nqaf.aspx
which says "While there are several general definitions of quality,
one of the most commonly used and succinct definitions is fitness
for use or fitness for purpose. ... the concept of quality of
statistical information is multi-dimensional and that there is no
one single measure of data quality. Examples of the common quality
dimensions or components include: relevance; accuracy; reliability;
timeliness; punctuality; accessibility; clarity, interpretability;
coherence; comparability; credibility; integrity; methodological
soundness; and serviceability." This UN site also lists
national and international quality references.
The UN has the UN Statistical Division http://unstats.un.org/unsd/default.htm
This division includes links to other UN units, committees, etc.
Conferences on Data Quality for International
Committee for the Coordination of Statistical Activities
Data.un.org has a glossary http://data.un.org/Glossary.aspx
which defines quality: Quality is a multi-faceted concept. The
dimensions of quality that are considered most important depend on
user perspectives, needs and priorities, which vary across groups of
users. ... A generic list would include the following dimensions:
Relevance, Accuracy, Timeliness, Punctuality, Accessibility, Clarity
/ interpretability, Comparability, Coherence, Integrity,
Credibility, Methodological soundness
Statistics Canada has this http://www.statcan.gc.ca/eng/about/policy/info-user
Policy on Informing Users of Data Quality and Methodology (approved
March 31, 2000), which lists the six characteristics of data fit for
use: relevance, accuracy, timeliness, accessibility,
interpretability and coherence.
Hong Chen, David Hailey, Ning Wang, and Ping Yu. A Review of Data
Quality Assessment Methods for Public Health Information
Int J Environ Res Public Health. 2014 May; 11(5): 5170–5207.
Among the findings are that "Completeness, accuracy, and timeliness
were the three most-used attributes among a total of 49 attributes
of data quality."
IQ International http://iaidq.org/
According to the website, "the professional association for
those interested in improving business effectiveness through quality
data and information." Can see some of their conference
presentations. For example
It Is Possible to Agree on an Industry-wide List of Dimensions of
Data Quality. Dan Myers IQCP. Manager of Enterprise Data Management.
Farmers Insurance. http://iaidq.org/idq2013/presentations/
A discussion of some of the usual terms (accuracy, completeness,
etc) and what they mean.
The six pillars of Data Quality http://database.ca/Blog.aspx?blogid=9
November 11, 2013, by Ramin Haghighat, Senior Database Architect.
The six are accuracy, completeness, conformity, consistency,
duplication and validity.
Melissa Data http://www.melissadata.com/enews/articles/1007/2.htm
6 Key Data Quality Dimensions: completeness, conformity,
consistency, accuracy, duplication, integrity.
Office of Information and Regulatory Affairs https://www.whitehouse.gov/omb/oira
has the Information Quality Guidelines (from 2002) https://www.whitehouse.gov/omb/info_quality_iqg_oct2002/
This includes Procedures for Ensuring and Maximizing the
Quality, Objectivity, Utility, and Integrity of Information
Prior to Dissemination, which says "In Government-wide
Guidelines, "quality" is defined as an encompassing term
comprising utility, objectivity, and integrity. ...
"objectivity" is a measure of whether disseminated information
is accurate, reliable, and unbiased and whether that information
is presented in an accurate, clear, complete and unbiased
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last updated 4/17/16
last verified 4/16/16