Strategic Data Engineering Paves the Way for Healthcare AI Revolution
As artificial intelligence continues to gain momentum in healthcare, industry experts stress that its success is deeply rooted in one foundational element: data engineering. Venkat Mounish Gundla, Founder of Lydnow EdTech, underscores that the journey toward AI-enhanced healthcare begins not with algorithms, but with reliable, scalable, and integrated data systems.
The healthcare sector generates data at an extraordinary pace, with volumes increasing by over 60% annually. Hospitals typically manage information across 18 different systems, involving structured, semi-structured, and unstructured formats. Even the most advanced AI models risk failure without a robust data infrastructure. “Data engineering provides this critical foundation by standardizing, integrating, and delivering reliable data at the scale needed for modern medical innovation,” says Gundla.
High-Quality Data: The Fuel for Predictive AI Models
The performance of AI in healthcare is directly tied to data quality. Gundla explains that more than 80% of AI model failures in healthcare are linked to poor data quality—a reality that affirms the importance of investing in clean, relevant, and consistent data. A mere 5% improvement in data completeness and accuracy can lead to nearly a 10% increase in AI performance.
This impact is not theoretical. Gundla cites the example of sepsis prediction models that can now issue alerts hours before symptoms appear, thanks to well-engineered data pipelines. These models can dramatically improve patient outcomes and reduce mortality rates when fed high-quality, real-time data.
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Securing and Scaling for the Future
With global healthcare data expected to reach 2314 exabytes by 2025, the pressure is on institutions to build data systems that are efficient but also secure and compliant. Gundla advocates for the strategic use of cloud infrastructure, real-time data pipelines, and adherence to regulatory frameworks like HIPAA and GDPR to ensure patient privacy.
“Strategic data engineering is not just a technical upgrade—it is the cornerstone of healthcare innovation,” says Gundla. By treating data as a strategic asset, healthcare providers can unlock AI’s full potential and create patient-centric, predictive systems that transform care delivery.
