Executable Data Contracts for Reliable AI Pipelines
Keywords:
Executable Data Contracts, Data Quality, AI Pipelines, Smart ContractsAbstract
An executable Data Contract (EDC) is an emerging paradigm of data architecture in assuring data quality, compliance and interoperability of contemporary data ecologies. In contrast to more traditional, static contracts that exist only as documents defining schema and validation rules, EDCs contain that logic as part of a runnable, executable program that can fit directly into data pipelines and systems of record. This paper measures the operational, compliance and performance costs of deploying EDCs on a heterogeneous data landscape integrating cloud-native warehouses, API-centric integration and regulated use-cases, like finance and healthcare. We determined how efficiencies in validation, reduction of error, compliance with regulation, and cost minimization are looked at using a mixed-methods approach that comprises of both empirical measurement and simulation-based stress tests as well as interviews with stakeholders.
The results will bring findings that adoption of EDC will reduce data-related defects 62 to 74 percent, pipeline set up approval time 35 to 42 percent, and compliance scores to 15 percent. Nevertheless, its implementation does not occur without some of the obstacles, such as the complexity of primary development, investment required in integrating them with the legacy systems, and the alignment of governances across business units. The study then comes to a conclusion that EDCs have most potency when used together with automated CI/CD validation pipelines, schema version control and compliance aware orchestration layers.
The maturity scheme provided is a phased plan of using EDCs that companies can follow in order to balance performance enhancement against manageability and governance. These findings can serve as an empirical basis on which an adequate effort to roll out the data governance policies to a real time environment can be based with minimal friction between the engineering and compliance groups.
Downloads
References
Bhoite, H. (2025, May 4). AI-Driven generation of data contracts in modern data engineering systems. arXiv.org. https://arxiv.org/abs/2507.21056
Foidl, H., Felderer, M., & Ramler, R. (2022). Data Smells: categories, causes and consequences, and detection of suspicious data in AI-based systems. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2203.10384
D’Amour, A., Heller, K. A., Moldovan, D., Adlam, B., Alipanahi, B., Beutel, A., Chen, C., Deaton, J., Eisenstein, J., Hoffman, M. D., Hormozdiari, F., Houlsby, N., Hou, S., Jerfel, G., Karthikesalingam, A., Lucic, M., Ma, Y., McLean, C. Y., Mincu, D., . . . Sculley, D. (2020). Underspecification presents challenges for credibility in modern machine learning. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2011.03395
Kolluri, N. S. (2024). Automating Data Pipelines with AI for Scalable, Real-Time Process Optimization in the Cloud. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(6), 2070–2079. https://doi.org/10.32628/cseit242612405
Namli, T., Sınacı, A. A., Gönül, S., Herguido, C. R., Garcia-Canadilla, P., Muñoz, A. M., Esteve, A. V., & Ertürkmen, G. B. L. (2024). A scalable and transparent data pipeline for AI-enabled health data ecosystems. Frontiers in Medicine, 11. https://doi.org/10.3389/fmed.2024.1393123
Harishchandra Patel Impedance Control in HDI and Substrate-Like PCBs for AI Hardware Applications. (2024). Journal of Electrical Systems, 20(11s), 5109-5115.
Aejas, B., Belhi, A., & Bouras, A. (2025). Using AI to ensure reliable supply chains: legal relation extraction for sustainable and transparent contract automation. Sustainability, 17(9), 4215. https://doi.org/10.3390/su17094215
Socius Labs, University of Cyprus, University of Amsterdam, London School of Economics and Political Science, Conspiracy Watch, & Bedrock AI. (2025). PETLP: A Privacy-by-Design Pipeline for Social Media Data in AI Research. https://arxiv.org/html/2508.09232v1
Berre, A. J., Tsalgatidou, A., Francalanci, C., Ivanov, T., Pariente-Lobo, T., Ruiz-Saiz, R., Novalija, I., & Grobelnik, M. (2022). Big Data and AI Pipeline Framework: Technology Analysis from a Benchmarking Perspective. In Springer eBooks (pp. 63–88). https://doi.org/10.1007/978-3-030-78307-5_4
Foidl, H., Golendukhina, V., Ramler, R., & Felderer, M. (2023). Data pipeline quality: Influencing factors, root causes of data-related issues, and processing problem areas for developers. Journal of Systems and Software, 207, 111855. https://doi.org/10.1016/j.jss.2023.111855
Tolmach, P., Li, Y., Lin, S., Liu, Y., & Li, Z. (2020). A survey of smart Contract formal specification and verification. arXiv (Cornell University). https://doi.org/10.48550/arxiv.2008.02712
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.