Acquisition Requirements and Root Cause Analysis: A Data-Centric Perspective with Data Governance, Data Analytics, and Data Quality
Abstract
Military logistics are responsible for sourcing and providing nearly every consumable item used by military forces worldwide. The process is highly complex; any misplaced decisions have serious cost and security consequences. Central to the entire process is the fidelity of the information used to make these acquisition decisions. We propose a holistic enterprise approach to improving data capabilities for acquisition management building upon a cumulative body of knowledge from information quality research and practice, as facilitated today by the role of Chief Data Officer (CDO). The research issues will be addressed and research findings written for both senior acquisition leaders and the larger academic researchers. Robust optimization deals with the sensitivity of output decisions in relation to input data differences. These differences can result from errors, uncertainties, reliability and credibility from multiple sources of data. Combining all these sophisticated models into a unified analytic framework, we will apply an innovative system-approach to estimate the cost of poor data quality: assessing how errors and uncertainties in data affect strategic and tactical decision making. Strategic decision making can include decisions about acquisitions and resource allocation, while tactical decision making concerns schedule, maintenance, and logistics. We will seek models that allow users to determine appropriate data quality improvement and data standardization investments. This would encompass many inter-related research components: 1. Creation of a data platform with data technologies to handle a variety of data in high volume and velocity. 2. Innovative data quality and data integration solutions, as well as state-of-the-art big data tools for improved data capabilities. 3. Data analytics capabilities ranging from simple business analytics to machine learning algorithms, to cost-based analytics to improve acquisition management. 4. Improved data governance capabilities in the data platform through research results and industry practices emerging from information quality advances and the emerging CDO roles and responsibilities. 5. Application of this holistic approach in an organizational setting to demonstrate the efficacy and to identify issues critical for future acquisitions research. The research issues will be addressed and research findings written for both senior acquisition leaders and the larger academic researchers. Robust optimization deals with the sensitivity of output decisions in relation to input data differences. These differences can result from errors, uncertainties, reliability and credibility from multiple sources of data. Combining all these sophisticated models into a unified analytic framework, we will apply an innovative system- approach to estimate the cost of poor data quality: assessing how errors and uncertainties in data affect strategic and tactical decision making. Strategic decision making can include decisions about acquisitions and resource allocation, while tactical decision making concerns schedule, maintenance, and logistics. We will seek models that allow users to determine appropriate data quality improvement and data standardization investments.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Nov 22, 2019
- Source ID
- HQ00341810011
Entities
People
- Richard T Wang
Organizations
- Office of the Secretary of Defense
- University of Arkansas at Little Rock