The Army logistics community relies on data to make decisions regarding the logistics of their operations. Our armed forces need to have access to dependable logistics data in order to be properly supported. These logistics decisions greatly impact the Army’s budget, as well as their readiness and effectiveness in combat. Significant care must be taken to ensure that changes in the Army’s data systems do not create unintended consequences for its users. Army logistics decisions are made by a variety of people in different organizations. It is essential that they all share the same view of accurate, timely data that is well defined, of high quality and provided in a timely manner. Establishing data stewardship over logistics data is essential to ensuring the quality of that data.
Data Blueprint designed a data stewardship process that provides the governance necessary to prioritize resources and efforts to meet the Army’s data objectives and to minimize the risk of unintended consequences. The G-4 Council of Colonels (CoC) provides an integrated view of the logistics business information needs and makes decisions accordingly. The G-46 manages the process of gaining and implementing decisions from the CoC and monitors the implementation of these decisions. Data Blueprint’s ultimate goal was to make the appropriate data available to decision makers within the enterprise architecture defined by the Department of Defense and the Army CIO/G-6.
Data Blueprint’s process for data governance and change management allowed the Army G-4 to:
Establish stewardship over Army logistics data
Establish the role of the Army G-4 in managing logistics data definitions, setting data quality standards, and identifying authoritative data sources
Establish priorities of effort for making relevant Army logistics data reliable, visible, accessible, understandable, trusted and interoperable to all authorized users
Provide guidance to program managers on the integration, development, quality, and retention of Army logistics data
Establish a standard process and format for business case analyses that resulted in the prioritization of efforts to define data element quality and supporting authoritative data systems (ADS)