The healthcare and life sciences (HCLS) sector is experiencing significant changes in how data is managed and utilized. Organizations generate vast amounts of information that could improve patient care, accelerate innovation and enhance operational efficiency. However, much of this data — up to 97% — remains unused, highlighting challenges in collecting, managing and analyzing it effectively.

In a recent webinar, experts from Fivetran and Slalom discussed the current state of data in healthcare and life sciences (HCLS), trends shaping the industry and how organizations can address these challenges with modern data strategies. 

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The challenges of managing data in HCLS

HCLS organizations face several hurdles as they seek to better use their data:

• Data silos: Data is often stored across disparate systems, making integration complex and time-consuming.
• Regulatory compliance: Global regulations, including HIPAA and GDPR, create significant barriers to moving and utilizing data securely.
• Cybersecurity risks: Breaches emphasize the need for strong security measures to protect private information. Just look at healthcare provider Kaiser, who had to notify millions of customers of a data breach when sensitive personal information was transmitted to third-party vendors. 
• AI adoption: Many organizations see the potential of AI but are held back by poor data quality and compliance issues, limiting scalability and effectiveness. In fact, according to Vanson Bourne research, underperforming AI programs/models built using low-quality or inaccurate data cost companies up to 6% of annual revenue on average. 

Despite these challenges, the potential for improvement is considerable. Advances in areas like AI-driven drug discovery, personalized patient care and predictive analytics demonstrate how better data use can drive progress. However, unlocking these capabilities requires a strong data foundation.

Building a strong data foundation 

For HCLS organizations to fully unlock the potential of data, they need to address foundational challenges. Simply put, the effectiveness of AI is directly proportional to the amount of data that is available to train and interrogate. To get the most out of that data, it requires:

• Streamlining data movement: The ability to move data securely and efficiently between systems is critical to enabling insights and driving decision-making, as is reducing as much cost as possible. 
• Ensuring security and compliance: HCLS organizations need solutions that not only meet but exceed regulatory requirements for data handling.

Large enterprises will have many legacy ETL vendors that previously were best of breed, but now these contribute largely to debt. Contemporary cloud-based ELT vendors, like Fivetran, enable faster access to data in analytics systems, providing AI-ready data within hours. Fivetran ensures full security, requires minimal management and eliminates maintenance for schema drift and API changes.

All these combine to provide HCLS companies a path to speed up innovation, overcoming the challenges of ETL and custom, in-house developed pipelines. Yet there is another barrier —  the challenge of processing highly regulated, sensitive data on the public web, even if it is fully end-to-end encrypted.

Fivetran’s Hybrid Deployment: A secure and scalable solution

To meet strict security standards and make the most of their data, HCLS organizations are adopting Fivetran's Hybrid Deployment. Designed for businesses in highly regulated industries, Hybrid Deployment processes sensitive data entirely within an organization’s virtual private cloud (VPC) or on-premises infrastructure, ensuring compliance without compromising usability.

How it works

Hybrid Deployment combines the simplicity of Fivetran’s managed platform with the security of customer-controlled environments. Sensitive data never leaves the organization’s infrastructure, addressing key concerns about privacy and compliance.

Key features and benefits:

• Enterprise-grade security: Data stays within the customer’s network, ensuring compliance with industry regulations like HIPAA and GDPR.
• Ease of implementation: The solution is easy to set up and manage through Fivetran’s intuitive interface, reducing maintenance efforts.
• Scalability and flexibility: With over 650 pre-built connectors, Fivetran supports a wide range of data sources and destinations.

This capability allows organizations to focus on deriving value from their data while leaving the complexity of integration and security to Fivetran’s platform.

Real-world example

Large pharmaceutical companies are already finding value with Fivetran’s Hybrid Deployment. With the ability to move sensitive, proprietary product, experiment and test data to warehouses like Databricks and Snowflake, one such company — who is among the 40 largest pharmaceutical companies — is already accelerating new developments. They are centralizing data that was previously left untouched due to security requirements into a central data power hub, allowing them to analyze current results and identify new paths forward. 

Fivetran’s Hybrid Deployment is also helping a telehealth company centralize their customer and physician communication data, allowing them to better streamline services and provide customers with the best healthcare experience possible — all while ensuring the data is safe, secure and handled well within regulatory standards.

Final thoughts

The healthcare and life science industry’s ability to meet evolving demands depends on secure, scalable and efficient data integration. Tools like Fivetran help organizations manage and utilize their data more effectively by bringing together all their data — including the most sensitive patient data — into a secure and compliant environment. 

By investing in secure integration methods and embracing modern approaches to data management, HCLS organizations can position themselves to improve outcomes for patients, providers and stakeholders.

To learn more about how Fivetran’s Hybrid Deployment can support your organization’s needs, watch the full webinar.

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