Value-Based File Retention: File Attributes as File Value and Information Waste Indicators

Fons Wijnhoven*, Chintan Amrit, Pim Dietz

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
10 Downloads (Pure)

Abstract

Several file retention policy methods propose that a file retention policy should be based on file value. Though such a retention policy might increase the value of accessible files, the method to arrive at such a policy is underresearched. This article discusses how one can arrive at a method for developing file retention policies based on the use values of files. The method’s applicability is initially assessed through a case study at Capgemini, Netherlands. In the case study, we hypothesize that one can develop a file retention policy by testing causal relations between file attributes (as used by file retention methods) and the use value of files. Unfortunately, most file attributes used by file retention methods have a weak correlation with file value, resulting in the conclusion that these methods do not well select out high- and low-value files. This would imply the ineffectiveness of the used attributes in our study or errors in our conceptualization of file value. We continue with the last possibility and develop indicators for file utility (with low utility being waste). With this approach we were able to detect waste files, in a sample of files, with an accuracy of 80%. We therefore not only suggest further research in information waste detection as part of a file retention policy, but also to further explore other file attributes that could better predict file value and file utility.
Original languageEnglish
Article number15
JournalACM journal of data and information quality
Volume4
Issue number4
DOIs
Publication statusPublished - 2014

Keywords

  • Methodology
  • Case study
  • Quantitative
  • Data mining

Fingerprint

Dive into the research topics of 'Value-Based File Retention: File Attributes as File Value and Information Waste Indicators'. Together they form a unique fingerprint.

Cite this