To establish a consistent approach to assess, manage and improve data quality across the data lifecycle, covering a wide spectrum of data types, and taking into account the blurred line between data and information in the context of emerging technologies, NATO has issued a detailed Data Quality Framework to the Alliance to establish a common understanding of data quality dimensions, metadata, and management to achieve fit-for-purpose and fit-for-use data.
An extensive framework responds to alliance-wide requirements
According to NATO-related official documentation, the Data Quality (DQ) Framework of the Alliance preconditions a unified strategy of Allies, the NATO Enterprise, and Partners to evaluate data quality and to control and refine it in the course of the data lifecycle. The Framework includes a wide scope of data types and is cognizant of the gray area between the terms data and information in light of emerging technologies.
It lays down common ground in terms of Data quality dimensions, Data quality metadata, and Data quality management, which, when combined, will provide fit-for-purpose and fit-for-use data. The Framework offers direction on practical instruments and methods in order to gauge, communicate, and enhance data quality across an organization.
Metadata standards and continuous improvement focus
Moreover, the Framework offers direction on the practical tools and techniques that can be used to measure, deliver, and enhance data quality across an organization. It highlights the importance of implementing accepted metadata standards to convey data quality, ongoing improvement, and technology enablers. The set of tools and techniques will be under revision as new policies, guidelines, and standards keep encouraging data quality work in the Alliance.
Broad stakeholder implementation approach
The Framework is directed to all those involved in the creation, sharing, administration, or usage of data among Allies, the NATO Enterprise, and Partner Nations, their authorities of implementation, their standardization bodies, decision makers, and the owners, custodians, and stewards of data. This holistic approach will make sure that the same data quality standards are always enforced by all levels of the Alliance structure.
AI and machine learning considerations
The document proposes elaborating on more implementation plans on Allies and on the NATO Enterprise, on Data Exploitation, on AI Priority Use-Cases, on the Alliance Data-Centric Reference Architecture, and on the topic of data quality as applied to Machine Learning (ML) and Artificial Intelligence (AI). Such a progressive method recognizes the importance of quality data in new technologies.
Strategic implementation recommendations
The Framework stresses the necessity of elaborating on the implementation plans with regard to Allies and the NATO Enterprise. Such plans should also be reviewed with detailed Data Exploitation and AI Priority Use-Cases to ensure that data quality criteria are aligned with the needs of the operations and strategic goals of the Alliance.
The second recommendation introduced in the document is that the Alliance Data-Centric Reference Architecture must be revised to make it possible to be compatible and integrated with the present systems without breaking the highest data quality requirements. Such an architectural review is crucial to preserving interoperability between members and partners of the Alliance.
Technology enablers and future adaptability
The Framework acknowledges that technology enablers are important to support and enhance data quality standards. The set of tools and techniques presented in the Framework will be revised regularly in accordance with the continuously evolving policies, instructions, and guidelines supporting the data quality work in the Alliance.
The Data Quality Framework of NATO is a major advance toward the standardization of data management activities within the Alliance, offering both broad advice on data quality assessment, data quality management, and data quality improvement in the context of modern military operations, artificial intelligence applications, and inter-allied cooperation in a more data-driven security setting.