Optimizing Data Quality in Real-Time: A Self-Healing Pipeline Approach
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V3I2P107Keywords:
Data Quality, Real-Time Data Processing, Self-Healing Pipelines, Data Pipeline Optimization, Automated Data Validation, Streaming Data, Data Governance, Fault-Tolerant Systems, Data Monitoring, Data Reliability, Error Detection and Correction, Data Engineering, Adaptive Data PipelinesAbstract
The reliability of AI-driven decision-making systems depends not only on the robustness of their architectures but also on the consistent quality of the data they process. In real-time analytics environments, ensuring high data quality is often in tension with meeting stringent latency requirements. This paper introduces a theoretical framework for optimizing data quality in self-healing data pipelines by employing a quantitative decision model that balances latency constraints with adaptive data validation. The approach is grounded in formalizing the optimization problem through explicit constraints on processing time, detection rates, and acceptable error margins. At the core of this model is an adaptive validation function capable of dynamically tuning its verification intensity based on observed data distributions and system performance metrics. Rather than relying on fixed, rule-based checks that may either underperform during data drift or overburden the system under high load, the proposed method continuously calibrates itself to achieve optimal trade-offs. Using simulated data generated from synthetic probability distributions, we evaluate the model’s behavior under varying levels of noise, drift, and system stress.
Our findings indicate that adaptive validation strategies consistently outperform static validation rules in non-stationary environments, enabling pipelines to maintain high data fidelity without compromising throughput. The theoretical results also identify threshold conditions under which adaptive checks provide the greatest benefit, offering a decision-making guide for system architects. By embedding this optimization model within a self-healing pipeline, we enhance not only its ability to detect and repair anomalies but also to proactively sustain the quality of streaming data in mission-critical applications. This work contributes to the growing body of theory that positions data quality assurance as an integral, quantitative component of resilient AI infrastructure, with broad applicability across finance, healthcare, cybersecurity, and other latency-sensitive sectors. Self-healing data pipelines, real-time data quality, adaptive validation, latency optimization, quantitative decision model, streaming analytics, anomaly detection, data drift, non-stationary environments, AI infrastructure
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