Accelerating medical research with data science as a service
Data science as a service (DSaaS) is a cloud-based delivery model. It makes data science infrastructure, data engineering, and data analytics available to data scientists so that they can process and analyze massive quantities of diverse data.
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Lisa Hines
A few months ago, I published a blog—titled Honey and Vinegar—in which I shared my thoughts on how to control shadow IT by simply enabling business-managed IT. In this blog, I’ll explore how data science as a service can be a business-managed IT offering.
What is data science as a service, and why is it important to academic medical centers?
Data science as a service (DSaaS) is a cloud-based delivery model. It makes data science infrastructure, data engineering, and data analytics available to data scientists so that they can process and analyze massive quantities of diverse data. These cloud-based service models are sometimes offered as industry-specific solutions, provided as a managed service offering in a private cloud, or provided in an on-premises, shared-services data science model.
Academic medical centers are unique in their ability to combine care delivery, education, and research to advance patient care. Data science research initiatives are often grant funded, and the grantee controls how the funding is allocated, including investments in the data science infrastructure. Although this is great for scientific independence and can expedite the project lifecycle, it isn’t great for organizational efficiency, data security, or data governance.
DSaaS can reduce the amount of time your data scientists spend on building platforms and data pipelines, and it can accelerate data engineering tasks and keep IT leaders happy at the same time. An enterprise infrastructure optimized for AI workloads can speed AI model development from data preparation to prototype creation to training and inference.
Investing in an on-premises AI shared-services environment is one way of avoiding model debt and shadow IT. Another option is to test-drive AI development with a private, cloud-hosted platform. Academic medical research centers can partner with NetApp, the industry leader in data management, to fast-track data science by providing guidance and choice as you determine which DSaaS offering is best for your organization.
As the data authority on hybrid cloud, NetApp® delivers AI solutions that remove bottlenecks at the edge, core, and cloud to enable more efficient data collection, faster AI workloads, and smoother cloud integration. To learn more, visit our AI Experts website.
Lisa Hines
Lisa Hines is a healthcare CIO on NetApp’s Global Healthcare and Life Sciences Team. Her 25 years of real-world experience in the industry allows her to deliver strategic insights and strategic planning for customers and partners.
Throughout her career, she has led numerous early adopter projects in the healthcare provider space and has participated in statewide collaborative efforts to improve access to high-quality health care, while effectively managing the cost of providing care.
She is an active HIMSS member and has held numerous board positions in her local chapter. In her spare time, Lisa enjoys lake life, kayaking, and standup paddleboarding.