Executable Data Contracts for Reliable AI Pipelines
DOI:
https://doi.org/10.15680/dj7azh45Keywords:
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.
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