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Healthcare Data Infrastructure Services
Offering Healthcare Data Infrastructure processes, technologies, and systems that enable healthcare organizations to collect, store, manage, and analyze data to extract insights.
Our Solutions
Healthcare Data Infrastructure Services
Veritas set the strategy, which outlines how healthcare organizations can manage, utilize, and derive value from their data assets. This Implementation involves executing the plan, including establishing data governance, architecture, and analytics processes. A successful data strategy enables data-driven ethical decision-making, improves business processes, and helps organizations achieve their goals.
Veritas creates data architectures and centralizes management on-premises or in the cloud to make data-driven decision-making accessible to all its customers.
ETL for Healthcare
ETL stands for Extract, Transform, Load and is a critical process in data analytics and data warehousing. Veritas implement and execute the strategies of extracting raw data from various sources, transforming it to a suitable format, and then loading it into a centralized repository to prepare it for analysis, reporting, and business intelligence, billing etc. ensuring it’s clean, consistent, and usable. We specialize in Data Infrastructure for Healthcare.
Cloud Based Data Infrastructure (AWS)
Cloud data infrastructure and implementation involve using cloud computing services to store, manage, and process data, offering scalability, cost-effectiveness, and flexibility. AWS provide us with the physical hardware (servers, storage), the software abstraction layer, and the networking infrastructure that allows our users to access virtualized resources on demand.
On Premise Data Infrastructure (Linux)
On-premises data infrastructure refers to an organization’s IT systems, hardware, and software being hosted and managed within its own physical facilities, rather than a cloud provider’s data center. Implementation involves setting up and maintaining all necessary infrastructure, including servers, networking equipment, and storage, directly on-site.
ML for Healthcare
Machine Learning model testing and validation are crucial processes for ensuring a model’s performance and generalization ability. It evaluates a trained model’s performance to assess its ability to make accurate predictions. Model validation, is used during the development phase to fine-tune and optimize the model by evaluating its performance on a validation dataset, which is different from the training
At Veritas, conversations are where it all begins. We analyze your goals and challenges, dive deep to uncover insights, and through extensive interviews and creative storyboards, we help you identify top-value solutions, and setting the stage for transformative growth.