Solving pharmaceutical data research & regulation problems with analytics
Researchers are hamstrung by data fragmentation. Learn how data lakes & AI enable interactive, predictive pharmaceutical data research.
Empowering the Data-driven Enterprise
Researchers are hamstrung by data fragmentation. Learn how data lakes & AI enable interactive, predictive pharmaceutical data research.
How AI risk management can flip the paradigm of traditional risk management methods, achieiving enhanced outcomes at a far lower cost.
Big data fabric is an emerging platform concept that aims to accelerate business insights by automating ingestion, integration & more from data silos.
How a Chief Happiness Officer can take a data-driven approach to workplace wellness, improving employee productivity and ultimately the bottom line.
With so much data fragmented across disparate systems, the global healthcare industry is in dire need of a revolutionary blockchain for healthcare system.
Data does not mean what it once did. A decade ago, most was structured data from internal sources such as financial systems and ERP. Today, big data encompasses social media data, machine-generated logs, IoT, real-time, and much more.
The data lake is now well past its infancy: 1/4 of all organizations have a data lake in production. However, with maturity comes data lake misconceptions.
Recently, Frost & Sullivan awarded Solix with its prestigious 2018 Global Product Leadership Award. We are honored to receive this award, validating our major efforts over the last few years to create a Common Data Platform for data-driven enterprises that affordably address data management, governance, and analytics needs. “The General Data Protection Regulation (GDPR) is the most comprehensive privacy legislation […]
Things will inevitably change as your business grows, so it’s important to look for opportunities that can optimize your budget.
Organizations must have a comprehensive understanding of their entire enterprise data landscape to meete GDPR compliance requirements.