An Information Product Approach for Total Information Awareness
17 Pages Posted: 6 Feb 2003
Date Written: November 2002
Abstract
To fight terrorism successfully, the quality of data must be considered to avoid garbage-in-garbage-out. Research has shown that data quality (DQ) goes beyond accuracy to include dimensions such as believability, timeliness, and accessibility. In collecting, processing, and analyzing a much broader array of data than we do currently, therefore, a comprehensive approach must be developed to ensure that DQ is incorporated in determining the most probable current or future scenario for preemption, national security warning and decision making. Additional data such as who was the data source, when was the data made available, how, where, and why also need to be included to judge the quality of the information assembled from these data.
We propose such an approach for Total Information Awareness with Quality (TIAQ), which includes concepts, models, and tools. Central to our approach is to manage information as a product with four principles. We have applied the information product approach to research sites where opportunities arise. For example, the Air Force Material Command uses requirements definition and forecasting processes to perform a number of functions. However, the Air Force experienced several complex problems due to DQ problems; as a result, fuel pumps were unavailable. Each engine needs a fuel pump; when a pump is not available, a military aircraft is grounded. We traced the fuel-pump throughout the process of remanufacture, and identified root causes such as delays by pump contractors and ordering problems. To a certain extent, detecting foreign terrorists and decipher their plots are analogous to tracing fuel pumps. Our research provides an interdisciplinary approach to facilitating Total Information Awareness.
Keywords: Total Information Awareness (TIA), Total Information Awareness with Quality (TIAQ), Data Quality (DQ), Information Product Map (IPMap), Quality Entity Relationship (QER)
Suggested Citation: Suggested Citation
Do you have negative results from your research you’d like to share?
Recommended Papers
-
Exemplifying Business Opportunities for Improving Data Quality from Corporate Household Research
By Stuart Madnick, Richard Y. Wang, ...
-
Improving the Quality of Corporate Household Data: Current Practices and Research Directions
By Stuart Madnick, Richard Y. Wang, ...
-
By Stuart Madnick, Richard Y. Wang, ...
-
A Framework for Corporate Householding
By Stuart Madnick, Richard Y. Wang, ...
-
Corporate Household Knowledge Processing: Challenges, Concepts, and Solution Approaches
By Stuart Madnick and Richard Y. Wang
-
Oh, so that is What You Meant! The Interplay of Data Quality and Data Semantics
-
Inequality in Utility of Data and Its Implications for Data Management
By Adir Even, G. Shankaranarayanan, ...
-
One Size Does Not Fit All - A Contingency Approach to Data Governance
By Kristin Weber, Boris Otto, ...