Start Date
10-12-2017 12:00 AM
Description
The importance of data in decision-making and for competitive advantage is well-known. Along with traditional “core” data, there are other types of data from which value may be derived. Among them is “data exhaust,” which, initially is normally collected as a byproduct of some event (transaction, event, search, disclosure, etc.), but ultimately can be used for another purpose to create value. Data exhaust often is user provided data, where users provide data that is not core or not required, but can be used to gather insights about their behavior and other issues. This paper analyzes the existing literature on data exhaust and proposes a data exhaust life cycle and a framework for analysis of data exhaust. The website, www.Stolen911, is used as a case analysis to illustrate the concepts associated with data exhaust, as well as its potential and limitations. Managerial implications are derived and future research areas identified.
Recommended Citation
OLeary, Daniel and Storey, Veda C., "Data Exhaust: Life Cycle, Framework and a Case Study of Stolen911.com" (2017). ICIS 2017 Proceedings. 2.
https://aisel.aisnet.org/icis2017/PracticeOriented/Presentations/2
Data Exhaust: Life Cycle, Framework and a Case Study of Stolen911.com
The importance of data in decision-making and for competitive advantage is well-known. Along with traditional “core” data, there are other types of data from which value may be derived. Among them is “data exhaust,” which, initially is normally collected as a byproduct of some event (transaction, event, search, disclosure, etc.), but ultimately can be used for another purpose to create value. Data exhaust often is user provided data, where users provide data that is not core or not required, but can be used to gather insights about their behavior and other issues. This paper analyzes the existing literature on data exhaust and proposes a data exhaust life cycle and a framework for analysis of data exhaust. The website, www.Stolen911, is used as a case analysis to illustrate the concepts associated with data exhaust, as well as its potential and limitations. Managerial implications are derived and future research areas identified.