DATA ISSUES  data collection, validating, other

The UN has the National Quality Assurance Frameworks  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   This division includes links to other UN units, committees, etc. Including:

Conferences on Data Quality for International Organizations   
Committee for the Coordination of Statistical Activities has a glossary   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   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 Systems   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   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.   A discussion of some of the usual terms (accuracy, completeness, etc) and what they mean.

The six pillars of Data Quality    November 11, 2013, by Ramin Haghighat, Senior Database Architect. The six are accuracy, completeness, conformity, consistency, duplication and validity.

Melissa Data   6 Key Data Quality Dimensions: completeness, conformity, consistency, accuracy, duplication, integrity.

Office of Information and Regulatory Affairs   has the Information Quality Guidelines (from 2002)   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 manner.

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last updated 4/17/16
last verified 4/16/16