Why Healthcare’s Data Strategy Hinges on Governance and Cloud-Ready EMRs

Healthcare organizations face rising data complexity and increasing regulatory oversight. As electronic medical records (EMRs) continue to migrate to the cloud, the data supporting them needs to be highly accurate, accessible, and governed. This isn’t just a modernization exercise—it’s about regulatory compliance, operational performance, and better patient care.

Let’s focus on five key strategies for effective healthcare data management. These practices help organizations strengthen their data foundations and meet compliance expectations.

1. Data Governance is the Frontline of Compliance

Comprehensive data governance is not optional—it’s core to HIPAA compliance, GDPR readiness, and internal policy enforcement. Without clearly defined data ownership, data stewardship, and data quality standards, healthcare providers risk missteps in audits or patient safety and privacy. 47% of respondents 1to recent BARC research rank data quality as their top governance priority.

Along with quality, companies need to prioritize the following to build strong governance foundations.

· Assign ownership and stewardship across teams and departments, it takes a village.

· Build repeatable workflows that enforce data integrity rules, automate where possible.

· Deliver easy access data lineage for auditability and accountability and trust.

· Continuous monitoring and oversight of data policies and standards.

2. Clean, Governed Data Drives Better Outcomes

Moving to cloud-based EMRs without cleaning or governing your data is like renovating a house without addressing the foundation. Inconsistent data formats, duplicate records, and missing fields degrade care and complicate downstream analytics.

By embedding data quality process and transformation pipelines into data workflows, healthcare organizations avoid costly cleanup later. Governed data ensures patient

records are accurate and trustworthy—whether you're scheduling patient interactions or performing operational analytics.

All enterprise data plays a role with EMR outputs and needs to be incorporated into your governance practices. Highly structured data is often a primary source for these systems, but as artificial intelligence (AI) grows into EMR systems governance professionals need to be prepared to handle semi and unstructured data as well. Think Image, video, sound and documents data. The same BARC research reference above indicates that 32% of global respondents have already deployed AI projects.

3. Data Integration is Not Just a Technical Task It’s Foundational

Healthcare systems are challenged by disparate and often fragmented data. Patient history lives in EMRs, lab results in separate systems, and insurance claims in yet another. Modern data integration platforms unify these silos in real time or near-real time—making the correct data available where and when it’s needed.

This isn't just about connectivity it’s about orchestration and agility. It’s about matching patient IDs across systems, resolving inconsistencies, and delivering a single source of truth. With integration comes clarity. Well organized data provides observability and transparency to each step in complex data driven processes and successful governance.

4. Prepare Data to Scale with the Cloud

As EMR platforms accelerate their migration to cloud delivery, on-premises data processes and brittle pipelines can often fall short. Cloud migration demands scalable, secure data preparation that can handle variable workloads and distributed teams.

Automated transformation, metadata-driven design, and cloud-native orchestration all help healthcare IT teams keep pace.

5. Data Lineage Builds Confidence and Reduces Risk

Tracking where your data came from—and how it’s changed—isn’t just helpful. It’s essential. Data lineage supports regulatory reporting, internal audits, and root-cause analysis when errors surface. With lineage in place, teams resolve issues faster and avoid repeating mistakes. Perhaps the most valuable part of lineage and observability is how it impacts trust. A clear view of source data in combination with analytic outputs immediately answers trust and accuracy concerns in a positive manner.

Final Thoughts:

Healthcare’s move to the cloud can’t succeed without a parallel investment in data governance, integration, and data quality. Companies seeking to execute on these initiatives should reduce risk by engaging expert partners and vendors who can fast track the process based on market and healthcare expertise in Data Management, Analytics and AI to lower risk and avoid common mistakes. Optimized, consistent, governed data isn’t just an IT requirement, it needs to be the primary component of data driven healthcare innovation. Optimized, consistent, governed data isn’t just an IT requirement, it needs to be the primary component of data driven healthcare innovation.

About the Author:

Shawn Rogers is the CEO of BARC US and brings over 28 years of experience to the role. He is an internationally respected industry analyst, speaker, author and instructor on data, business intelligence, analytics, AI/ML and cloud technologies. His former executive strategy roles with Dell, Statistica, Quest software and TIBCO give him a unique perspective on the software industry.

About BARC:

BARC is a leading analyst firm for data & analytics and enterprise software with a reputation for unbiased and trusted advice. Our expert analysts deliver a wide range of research, events and advisory services for the data & analytics community. Our innovative research evaluates software and vendors rigorously and highlights market trends, delivering insights that enable our customers to innovate with data, analytics and AI. BARC’s 25 years of experience with data strategy & culture, data architecture, organization and software selection help clients transform into truly data-driven organizations. www.barc.com