A Prototype for Content-Rich Decision-Making Support in NOAA using Data as an Asset

Tuesday, 15 December 2015
Poster Hall (Moscone South)
Matthew Austin, National Environmental Satellite, Data, and Information Service, Silver Spring, MD, United States and Ge Peng, NC State University, Asheville, NC, United States
Data Producers and Data Providers do not always collaborate to ensure that the data meets the needs of a broad range of user communities. User needs are not always considered in the beginning of the data production and delivery phases. Often data experts are required to explain or create custom output so that the data can be used by decision makers. Lack of documentation and quality information can result in poor user acceptance or data misinterpretation. This presentation will describe how new content integration tools have been created by NOAA’s National Environmental Satellite, Data, and Information Service (NESDIS) to improve quality throughout the data management lifecycle.

The prototype integrates contents into a decision-making support tool from NOAA’s Observing System Integrated Assessment (NOSIA) Value Tree, NOAA’s Data Catalog/Digital Object Identifier (DOI) projects (collection-level metadata) and Data/Stewardship Maturity Matrices (Data and Stewardship Quality Rating Information). The National Centers for Environmental Information’s (NCEI) Global Historical Climatology Network-Monthly (GHCN) dataset is used as a case study to formulate/develop the prototype tool and demonstrate its power with the content-centric approach in addition to completeness of metadata elements. This demonstrates the benefits of the prototype tool in both bottom roll-up and top roll-down fashion. The prototype tool delivers a standards based methodology that allows users to determine the quality and value of data that is fit for purpose. It encourages data producers and data providers/stewards to consider users’ needs prior to data creation and dissemination resulting in user driven data requirements increasing return on investment.