In life sciences, the problem with discovering new insights isn’t the absence of helpful data, but analyzing the plethora of data already gathered. Data is commonly stored in silos, making it difficult to find patterns and build on what’s collected from each silo. Insights rely on broader data sets, gathering strength from multiple origins weaved together. A modern data warehouse pools all relevant data and invites collaboration.
Thanks to the cloud, we have powerful tools to do just that, effectively and securely, while advancing data interoperability. Making the change from on-premise to cloud storage, though, requires planning and a different way of working. The results for patients and research organizations can literally be life-changing. By removing dividers between data sources, and decreasing the time and cost it takes to make those connections, discoveries and ideas can flourish.
Storing data in a cloud warehouse creates a single source of truth for analytics. Learn how migrating to GCP enables life sciences organizations to utilize AI, incorporate data from wearable technology, handle large data sets, and more.
Moving to the cloud from a traditional on-prem data warehouse is not without its challenges. Learn how to address security concerns, control costs, and address the unique situation each organization may be in.
Cloud migration can be overwhelming. We identify key best practices to consider for building a cloud infrastructure, including cloud identity, permissions, networking, data architecture design, and more.