“Mitigating Threats To Data Quality Throughout the Curation Lifecycle

Abstract

Data quality criteria implied by the candidate frameworks are neither easily harmonized, nor readily quantified. Thus, a generalized systematic approach to evaluating data quality seems unlikely to emerge soon. Fortunately, developing an effective approach to digital curation that respects data quality does not require a comprehensive definition of data quality. Instead, we can appropriately address “data quality” in curation by limiting our consideration to a narrower applied questions: Which aspects of data quality are (potentially) affected by (each stage of) digital curation activity? And how do we keep invariant data quality properties at each curation stage? A number of approaches suggest seem particularly likely to bear fruit: Incorporate portfolio diversification in selection and appraisal. Support validation of preservation quality attributes such as authenticity, integrity, organization, and chain of custody throughout long-term preservation and use — from ingest through delivery and creation of derivative works. Apply semantic fingerprints for quality evaluation during ingest, format migration and delivery. These approaches have the advantage of being independent of the content subject area, of the domain of measure, and of the particular semantics content of objects and collections — so they are broadly applicable. By mitigating these broad-spectrum threats to quality, we can improve the overall quality of curated collections, and their expected value to target communities.