مقاله انگلیسی رایگان در مورد تأثیر تجربه و زمان بر استفاده از اطلاعات کیفیت داده‌ها – Informs 2003

Abstract

Data Quality Information (DQI) is metadata that can be included with data to provide the user with information regarding the quality of that data. As users are increasingly removed from any personal experience with data, knowledge that would be beneficial in judging the appropriateness of the data for the decision to be made has been lost. Data tags could provide this missing information. However, it would be expensive in general to generate and maintain such information. Doing so would be worthwhile only if DQI is used and affects the decision made.

This work focuses on how the experience of the decision maker and the available processing time influence the use of DQI in decision making. It also explores other potential issues regarding use of DQI, such as task complexity and demographic characteristics. Our results indicate increasing use of DQI when experience levels progress through the stages from novice to professional. The overall conclusion is that DQI should be made available to managers without domain-specific experience. From this it would follow that DQI should be incorporated into data warehouses used on an ad hoc basis by managers.

Introduction

It has long been recognized that the effectiveness of decision making is influenced by many factors. Among these are the time available before the decision must be rendered, the experience of the decision maker, and the quality of the data needed for the decision. Although ideally the data used should be of high quality, in practice this often is not the case, for reasons that range from the cost of obtaining quality data to the inherent difficulty or even impossibility of doing so for certain data types. Nevertheless, experienced decision makers, especially ones who have worked in a particular milieu for a sufficient period of time, develop a feel for the nuances and eccentricities of the data used and intuitively compensate for them. As organizations increasingly move to stored repositories such as data warehouses, this intuitive feel is not preserved for many who extract data from such sources to support their particular needs.