Elkhidir, ERotimi, JOBPatel, TMoshood, TDWilkinson, S2025-08-252025-08-252025-08-07Buildings, ISSN: 2075-5309 (Print); 2075-5309 (Online), MDPI AG, 15(15), 2797-2797. doi: 10.3390/buildings151527972075-53092075-5309http://hdl.handle.net/10292/19720The construction industry is a cornerstone of New Zealand (NZ)’s economic growth, yet strategic infrastructure planning is constrained by fragmented and inconsistent pipeline data. Despite the increasing availability of construction pipeline datasets in NZ, their limited clarity, interoperability, and standardisation impede effective forecasting, policy development, and investment alignment. These challenges are compounded by disparate data structures, inconsistent reporting formats, and semantic discrepancies across sources, undermining cross-agency coordination and long-term infrastructure governance. To address this issue, the study begins by assessing the quality of four prominent pipeline datasets using Wang and Strong’s multidimensional data quality framework. This evaluation provides a necessary foundation for identifying the structural and semantic barriers that limit data integration and informed decision-making. The analysis examines four dimensions of data quality: accessibility, intrinsic quality, contextual relevance, and representational clarity. The findings reveal considerable inconsistencies in data fields, classification systems, and levels of detail across the datasets. Building on these insights, this study also develops a conceptual minimum dataset (MDS) framework comprising three core thematic categories: project identification, project characteristics, and project budget and timing. The proposed conceptual MDS includes unified data definitions, standardised reporting formats, and semantic alignment to enhance cross-platform usability and data confidence. This framework applies to the New Zealand context and is designed for replication in other jurisdictions, supporting the global push toward open, high-quality infrastructure data. The study contributes to the construction informatics and infrastructure planning by offering a practical solution to a critical data governance issue and introducing a transferable methodology for developing minimum data standards in the built environment to enable more informed, coordinated, and evidence-based decision-making.© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).4005 Civil Engineering40 Engineering33 Built Environment and Design3302 Building1201 Architecture1202 Building1203 Design Practice and Management3301 Architecture3302 Building4005 Civil engineeringconstruction projects datapipeline projects dataconstruction data qualityprojects dataconstruction data standardisationJournal ArticleOpenAccess10.3390/buildings15152797