Description:

Generating actionable information from data requires several predicates, chief among them is the quality of the data.  Bad data yields bad information.  A robust model is hence required not only ensures reliable data allowed in, but more importantly must also score high level of fidelity of the information that it produced from the data.  A credible transference model must therefore be driven by scientific tools, strict objective analysis, and meant to enable unrestricted outcomes.  The quality of the model is measured by the consistency of outcome information generated from the transference of the same source data.  In other words, the exact same data, regardless of its quality and validity, must yield the exact same information.  The quality and validity of the extracted information are strongly correlated to the quality of source data.  This correlation can be linear, simple nonlinear, or complex nonlinear.  Data may drive information about important indicators of changes in environment, behavior of a phenomenon, and success or lack thereof of tactical and strategic planning.  The value of such information, however, is only meaningful if it flows into a decision-making process, economy-driven governance, and organizational leadership.  In this session, we solicit presentations and papers that address data to information (D2I) transfer, the generic and agency-centric transfer models.  Papers that address the potential risks associated with D2I transference models are particularly welcomes. 

We also invite papers that address the critical part of how resulting information is assimilated into the agency's decision-making process.  Of particular interest are paper that would address quantifiable measures that would be expressed by specific metrics and indicators of change. 

We also invite papers that address End User role in validating the resulting Information Products and whether they meet their needs or would inform future developments of new data streams. 

The desired outcome of this session is develop a better understanding of how data transference to information is a critical element that flows into decision-making processes and governance.  We hope to gain lessons on how diverse elements in this transference process would work together toward data and information-based decision-making in defining agency's ability to meet the data and information needs of End Users, to guide the Decision-Making process.