Back in 2023, estimates indicated that hospitals generated an average of 50 petabytes of data yearly, accounting for 30% of the world’s data, growing at a 36% annual pace. Fast forward to 2025, as per the yearly growth rate, hospitals today will be creating around 92.5 petabytes of data annually.
That inflated number demonstrates the sheer volume of data that has nearly doubled in just 2 years alone and, given the same annual growth rate, may reach over 400 petabytes by 2030. While healthcare institutions may regard it as an asset that powers better healthcare and fuels growth, it can quickly turn into a liability if it lacks data governance.
What is Data Governance in Healthcare?
Data today is sprawling across healthcare data centers, networks, systems, on-premises, and cloud infrastructures. It’s the backbone of providing high-quality healthcare services to patients who trust healthcare institutions with their sensitive and confidential data.
Data governance in healthcare is a structured process that outlines how healthcare organizations collect, manage, process, store, and share medical patient data transparently while complying with industry best practices as well as ethical and regulatory standards.
It ensures that healthcare data is accurate (data quality and integrity), secure and private, accessible by only authorized individuals, and utilized responsibly (data minimization, purpose limitation, and deletion) across the healthcare institution and by third-party vendors. It also enables healthcare institutions to make better decisions and provide improved healthcare, meet regulatory compliance, and avoid noncompliance penalties.
Data governance in healthcare is core to securing Protected Health Information (PHI), Electronic Health Records (EHRs), and enhancing the healthcare organization’s overall data security posture to combat evolving threats, data breaches, and inadvertent data exposure. Without data governance, healthcare institutions risk patient data and trust.
How Does Data Governance Help Healthcare Organizations
According to the World Health Organization (WHO) Health Data Governance Summit, health data needs to be collected, stored, shared transparently, and analyzed, based on the foundations of strong data governance.
An effective data governance posture requires a multi-layered, structured framework that consists of policies, processes, and practices geared towards managing growing volumes of sensitive health data. Here are some of the ways data governance helps healthcare organizations:
a. Improves Data Quality and Accuracy
Data governance creates clear rules for how health data is gathered, processed, stored, shared, and updated. It affirms that patient data is consistent across data systems and isn’t incomplete or cluttered with inconsistencies. For example, patient data is the same for the lab team, emergency services, billing, etc.
This avoids data duplication or incomplete patient records, eliminating human error and strengthening communication between teams and systems. Hence, healthcare facilities may provide patients with accurate and enhanced diagnostics, improved insights, etc.
b. Strengthens Data Security and Compliance
Healthcare institutions handle massive volumes of patient data that is subject to regulatory safeguards. Regulations such as HIPAA, GDPR, and HITECH impose stringent requirements, necessitating a robust data governance framework.
Data governance establishes dedicated policies for access controls, data usage, sharing, and retention. It assigns accountability to ensure data remains secure and compliant throughout the data lifecycle.
c. Enhances Decision-Making and Analytics
Data is core to better decision-making and reporting. High-quality data backed by data governance improves daily operations and the ability to manage health systems effectively, as data can be trusted for fueling growth. It enables all stakeholders to make informed decisions and leverage structured data that resides outside silos and shadow data stores.
Trusted data can be shared internally and externally. Multiple teams can collaborate effectively to provide dedicated healthcare treatment, specific medicine and rehabilitation services.
e. Builds Patient Trust and Transparency
Data governance bolsters patient trust and confidence in the healthcare institution and the industry in general. It empowers patients to safely disclose and share their history, current prescriptions and any ongoing treatment and receive healthcare without hesitation. This strengthens the healthcare institution’s reputation for being transparent and accountable.
Challenges of Not Having a Data Governance Framework
There’s no structure or uniformity without governance. Absence of data governance threatens the very fabric that makes healthcare institutions private and exposes not only patients but also attracts regulatory scrutiny and noncompliance penalties.
According to the World Health Organization (WHO) Health Data Governance Summit, the COVID–19 pandemic has exposed long-standing data governance challenges, such as intellectual property rights, data sharing, reuse, and storage. Globally, persistent data gaps and fragmented approaches to governing health data in different contexts are a major roadblock to using data as a Global Public Good.
Key challenges of not having a robust data governance framework include:
a. Poor Data Quality and Inconsistent Records
Lack of data governance leads to inaccurate data records and inferior data quality, which ultimately results in poor patient healthcare. Unreliable data can’t be trusted, leading to inferior business decisions and strategic growth.
b. Increased Risk of Data Breaches and Non-Compliance
Malicious actors are continually on the lookout for vulnerabilities. A single gap unguarded by access controls can result in a massive data breach, which could trigger costly penalties, reputational damage, and loss of patient trust.
c. Creation of Data Silos and Lack of Interoperability
Healthcare institutions are made up of various departments and teams that rely on unified, trusted data. Lack of data governance results in teams storing data differently, which leads to the creation of data silos. This results in a lack of collaboration and poor analytics.
d. Lack of Clear Data Ownership and Accountability
Data needs to be handled by data stewards. Lack of data governance means no one is accountable for managing data, resulting in vulnerability to misuse and unauthorized access.
e. Increased Risk to Data Privacy and Security
Absence of data governance escalates data privacy and security risks, where sensitive health data is at risk of exposure. Lack of robust security policies, inferior data security posture, irregular audits, and assessments result in data loss and misuse.
How to Implement a Data Governance Framework in Healthcare
As more and more regulations tighten and enforce stringent requirements, implementing a robust data governance framework is no longer a choice but a critical business requirement.
Regulations such as the Health Insurance Portability and Accountability Act (HIPAA), Health Information Technology for Economic and Clinical Health (HITECH), General Data Protection Regulation (GDPR), California Privacy Rights Act (CPRA), among several other laws, require healthcare institutions to adopt strict data measures that ensure data privacy, security, and integrity.
Key steps in implementing a robust data governance framework include:
a. Establishing Clear Goals and Objectives
At the core of a robust data governance framework lies the intent of securing data that must be conveyed across the board. Stakeholders must realize the urgency and importance of securing data, improving patient data quality, enhancing interoperability, aligning practices with regulatory requirements, and more. This demands establishing clear policies and roles with accountability to ensure all stakeholders understand and follow a structured approach.
b. Establishing a Data Governance Team
There’s no oversight without a dedicated team whose core job is to monitor, develop, and improve data governance policies and practices. They’re like a watchdog that ensures health data is secured at all times, whether in motion or at rest.
c. Defining Roles and Assigning Responsibilities
Each member who comes in contact with health data must understand the importance of keeping it confidential. Data handlers such as data owners, data stewards, and data custodians must be responsible for data accuracy, quality, and security.
d. Ensuring Continuous Data Quality, Privacy and Compliance
Having a well-defined policy is one thing; ensuring all stakeholders adhere to the policy at all times is another. The governance team must ensure data remains secure throughout its lifecycle and complies with regulatory requirements that mandate data minimization, purpose limitation, retention, and disposal.
Legacy models and methods can’t scale with today’s hypervolume data sprawl. Healthcare institutions should onboard a robust automated data governance tool that streamlines compliance to secure sensitive patient data.
Automate and Simplify Data Governance with Securiti
Today’s hyper-connected data landscape demands a robust data governance automation tool that automates critical functions, minimizing error, risk, and ensuring swift regulatory compliance.
Securiti’s Data Governance empowers healthcare organizations to automate critical governance processes, democratizing unstructured and structured data utilization with contextual data+AI intelligence. With Securiti, healthcare organizations can:
- Discover and classify data and establish common grammar, classification, and labeling policies across data assets at scale
- Manage unstructured data to enable its safe use with generative AI
- Monitor sensitive data access and prevent unauthorized use
- Establish controls for safe adoption of AI technologies, including GenAI
- Enable users to easily find, understand, and trust the data they need
- Automatically track changes and transformations of data throughout its lifecycle
- Conduct data quality checks and validation across various data types
Request a demo to learn more.
