AI-Powered Stewardship Is the Future of Healthcare Data—and Somnath Banerjee Is Leading the Charge

Healthcare AI is often geared towards clinical decision tools, chatbots, and generative diagnostics, but none of these systems can work without reliable, well-managed data. Behind every AI breakthrough is the more difficult and less glamorous problem of data governance.

Senior enterprise architect Somnath Banerjee has spent the last several years addressing that challenge at one of the largest health insurers in the country. 

Over nearly two decades in healthcare data engineering, Banerjee recognized a persistent bottleneck: manual data stewardship could no longer keep up with the volume, complexity, and speed of modern systems. Poor data management doesn’t just slow operations; it actively undermines patient safety and regulatory compliance.

To solve this long-standing issue, Banerjee built a framework that automates data validation, error detection, and data reconciliation across millions of patient records. He was able to significantly reduce manual oversight with an automated and partly autonomous system designed to manage data quality at scale.

Here’s a closer look at how AI and automation can enhance the quality, compliance, and efficiency of data governance programs in healthcare.

The Hidden Costs of Human-Led Data Governance

For decades, healthcare organizations have relied on manual analysis to validate data, resolve discrepancies, and correct records. But as patient data volumes grow and systems become more interconnected, those manual processes have hit their limit.

It’s not just a question of speed. Estimates suggest that 20% of patient records in U.S. hospital systems are duplicated, leading to billing errors, clinical confusion, and compliance problems.

Human-led data stewardship introduces inconsistency, delays, and risk, especially when records are spread across multiple sources and updated in real time. And while regulations on data quality have tightened in recent years, many organizations still rely on processes that may not fully meet these standards.

“Data stewardship has long been a labor-intensive process,” Banerjee explains. “Our AI-driven data stewardship framework transforms how organizations master data, while ensuring real-time accuracy and compliance.”

To do this, he began embedding machine learning into the data governance process. His framework automatically flags anomalies, identifies duplicate records, and applies validation rules across systems. 

Reduced reliance on manual reviews can transform data stewardship from a bottleneck into a built-in function of the system.

Inside the Framework: Automating Data Quality at Scale

Banerjee’s framework is built to interact seamlessly within existing enterprise master data management (MDM) systems. 

It uses machine learning algorithms to detect inconsistencies and duplicate records with high precision, identifying mismatches and errors that would be difficult and time-consuming to catch manually. From there, automated validation rules replace manual cross-checking by applying established business logic to incoming data. Information is verified against predefined standards, without needing constant human review.

Through predictive analytics, the system can flag potential data issues before they impact operations, while real-time reconciliation enables patient records to be updated and verified dynamically across multiple platforms to ensure that data remains consistent and current.

In this environment, data stewardship is no longer a static and reactive process. Instead, management of the data evolves dynamically, ensuring continuous improvement in accuracy and usability of every bit of patient information. That means patient data is continuously checked and updated, reducing errors before they reach clinical or billing teams.

“Implementing machine learning algorithms and automated validation checks drastically reduces human intervention in error correction and matching validation,” says Banerjee. “We’re paving the way for intelligent, self-learning data systems that continue to evolve, improving accuracy at scale.”

How Better Data Enables Safer Care and Stronger Compliance

The healthcare organization using Banerjee’s system has already seen faster onboarding of new data sources and fewer errors in downstream reporting. 

More importantly, accurate and up-to-date data improves decision-making, clean records support more precise risk modeling and population health management, and better compliance with regulations like HIPAA and HITECH comes as a built-in benefit.

But for Banerjee, this is only the beginning: “Machine learning has already transformed how healthcare data is processed. In the future, self-learning MDM systems will be able to evolve in real time, eliminating manual stewardship and enhancing data accuracy, insights, and predictive analytics for a more effective and accurate healthcare delivery.”

This vision is what drives his advocacy for industry-wide data governance standards. His long-term plan is to work with leaders across healthcare to promote shared best practices for data security, compliance, and interoperability, thereby creating an ecosystem where data is consistent, clean, accessible, and actionable at every level.

Somnath Banerjee: A Career Built on Healthcare Data Innovation

Somnath Banerjee has been developing his expertise as a skilled architect of healthcare data technology for twenty years. 

Early in his career, he realized the importance of data in streamlining operations, improving decision-making, and solving complex challenges, which led him into the path of enterprise data management for large-scale systems.

In his current senior engineering role at a Fortune 50 health insurance company, he leads major initiatives in master data management and automation. Alongside his technical work, he is an active member of the healthcare technology community, holding senior membership in the IEEE and contributing to the Forbes Technology Council. He also mentors startups through programs like Startupbootcamp and Gener8tor, supporting the next generation of innovators.

Banerjee’s work has been recognized with the Stevie American Business Award, the Global Tech Award, and the Global Recognition Award, and he has also published research on healthcare master data management, further establishing his role as a trusted authority in the field.

Laying the Foundation for Smarter, Safer Healthcare

Somnath Banerjee’s AI-driven stewardship framework does more than improve operational efficiency; it reshapes the very foundation on which healthcare data is governed and trusted. By embedding intelligence into the core of data workflows, he has created a model that is scalable, compliant, and adaptable to real-time demands.

“My MDM solutions have set a benchmark, and next I aim to champion industry-wide best practices in healthcare data governance and interoperability,” he concludes.

That vision points toward a healthcare system where clean, up-to-date, and reliable data drives better patient outcomes, reduces costs, and supports more precise decision-making. Through self-correcting data systems, healthcare organizations will gain the tools they need to meet evolving challenges and improve the lives of their patients.