A Matter of Trust: Data Quality and Information Integrity

Abstract

Macfarlane and AbouZahr describe how data producers can assure users about the quality and integrity of information. They provide guidance about preventing, detecting, addressing and documenting errors and omissions that may compromise data quality whatever the source. They suggest how users can interrogate a dataset to determine whether quality assurance techniques have been implemented, from the design stage, to the processes of gathering, compiling, cleaning and analysing data, and to the eventual transformation of data into statistics and information for policy and programme use. The authors explore how data can be shared, combined, linked and triangulated to multiply information. Finally, they draw attention to the need to build and maintain trust in statistical information as central to decision-making and a core public good.