Healthcare organizations have been swimming (or sinking) in oceans of data for a long time now.
Every patient interaction, lab result, insurance claim, and medical device generates information that, when harnessed effectively, can improve outcomes for patients and transform operations.
For many health systems, payers, and life sciences organizations, however, the complexity of building and scaling healthcare data engineering capabilities in-house can quickly become overwhelming.
Internal data teams have a lot on their plates, from struggling to integrate fragmented systems, to working with unstructured and semi-structured data, to delivering on an ever-growing list of AI use cases—all the while juggling tight regulatory frameworks and even tighter margins.
What starts as a well-intentioned effort to modernize can quickly glide off the rails and become a costly, resource-draining liability.
Healthcare Data Comes with Unique Challenges
While every healthcare and life science enterprise brings its own unique bottlenecks and objectives to modernization, there are also some common roadblocks we tend to see in these spaces. Those include:
Regulatory complexity: Healthcare operates under some of the most stringent regulatory environments in the world. These regulations add layers of compliance requirements that must be engineered directly into data pipelines. Internal teams often lack the bandwidth or specialized expertise to ensure that governance, lineage, and security are built into every step of ingestion and transformation.
Data silos and interoperability gaps: Clinical, operational, supply chain, and financial data often sit in siloed systems or bespoke legacy platforms. Engineering pipelines that can unify data across EHRs, payer claims, imaging systems, and third-party applications is a heavy lift, requiring not only technical expertise but also deep domain knowledge.
Unstructured and semi-structured data: A colossal share (think north of 80%!) of generated data still lives in unstructured formats. This information, such as clinician notes, imaging files, and device data, has historically necessitated considerable human intervention to turn into a strategic asset. Emerging machine learning and AI solutions are transforming that process, but doing so in a vacuum presents a host of other challenges.
Scalability and performance bottlenecks: Building pipelines that can ingest and transform massive volumes of data from multiple sources in real time is not the same as running a traditional data warehouse. Health systems often underestimate the infrastructure and skill set required to achieve reliable refreshes, performant queries, and elastic scaling across geographies.
Resource constraints: Internal data teams are stretched thin, balancing day-to-day operational support with the nigh-constant need to innovate. Building an enterprise-scale healthcare data platform in-house while struggling to keep the lights on can result in burnout, turnover, or disruptions to daily operations that negatively impact clinicians, administrators, and patients alike.
Why Finding the Right Partner Makes the Difference
For healthcare organizations, the stakes are too high for trial and error. Poorly engineered data pipelines lead to more than just inefficiencies. They run an active risk of compliance violations, inaccurate reporting, and diminished patient care.
Instead of reinventing the wheel internally, more and more organizations in the space are turning to specialized data partners who live and breathe these challenges every day.
With the right partner, healthcare data engineering becomes a catalyst that allows providers, payers, and researchers to focus on what they do best: expanding the art of the possible, delivering exceptional patient care, and scaling for what comes next.
Working with a trusted data engineering partner can also give organizations access to:
- Proven frameworks and migration roadmaps that accelerate integration across EHRs, labs, and other systems
- Accelerators and playbooks that dramatically reduce time-to-insight while ensuring accuracy and compliance
- Deep expertise in building compliant architectures that scale across departments, span multiple clouds, and provide disruption-free support to systems spanning multiple geographies
Unburden Your Talent, Unleash Your Data
Healthcare and life science organizations already have the data they need to lead in the future of their industry. But that data is only as transformative as their ability to put it to work.
The right data engineering partner makes this possible, shifting the burden of large scale migration or modernization initiatives away from internal teams and enabling them to focus on delivering insights that improve outcomes, reduce costs, and transform patient experiences.
At Hakkoda, an IBM Company, we’ve helped some of the largest provider systems and future-ready payers in the U.S. modernize their data platforms with scalable, compliant, and future-ready architectures.
By bringing together deep domain expertise, Snowflake, multicloud, and hybrid data engineering, and AI accelerators built specifically to solve the industry’s most daunting challenges, we help organizations unlock new capabilities and plant the seeds of future innovations.
Ready to unlock the full potential of your healthcare data? Let’s talk.
