The case for personal data-driven decision making

Jennie Duggan*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


Data-driven decision making (D3M) has shown great promise in professional pursuits such as business and government. Here, policymakers collect and analyze data to make their operations more efficient and equitable. Progress in bringing the benefits of D3M to everyday life has been slow. For example, a student asks, "If I pursue an undergraduate degree at this university, what are my expected lifetime earnings?". Presently there is no principled way to search for this, be-cause an accurate answer depends on the student and school. Such queries are personalized, winnowing down large datasets for specific circumstances, rather than applying welldefined predicates. They predict decision outcomes by extrapolating from relevant examples. This vision paper introduces a new approach to D3M that is designed to empower the individual to make informed choices. Here, we highlight research opportunities for the data management community arising from this proposal.

Original languageEnglish (US)
Pages (from-to)943-946
Number of pages4
JournalProceedings of the VLDB Endowment
Issue number11
StatePublished - 2014
EventProceedings of the 40th International Conference on Very Large Data Bases, VLDB 2014 - Hangzhou, China
Duration: Sep 1 2014Sep 5 2014

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)


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