How to deploy data science applications with enterprise governance
BARC's blueprint for eliminating deployment bottlenecks—integrating open-source tools with enterprise security to deliver high-value applications faster.
Data science teams build sophisticated models, but struggle to deliver them as business applications. Deployment takes months. Models sit waiting for engineering resources. Business stakeholders make decisions without the insights they requested.
This BARC Spotlight examines why nearly half of organizations lack formalized data science governance—and how leaders are eliminating deployment bottlenecks by integrating open-source tools with enterprise security and collaboration.
• How to accelerate deployment from months to weeks while maintaining compliance and security
• Why purpose-built data science applications deliver higher ROI than traditional BI dashboards
• Practical frameworks for embedding governance without restricting data scientists' ability to innovate with R and Python
• Real examples from NASA, financial services, and regulated industries that cut deployment cycles while strengthening governance
Chief Data Officers, VP of Data Science, IT leaders, and Enterprise Architects balancing the need to scale data science impact with governance, security, and operational risk management.
This BARC Spotlight examines why nearly half of organizations lack formalized data science governance—and how leaders are eliminating deployment bottlenecks by integrating open-source tools with enterprise security and collaboration.
What you'll learn
• How to accelerate deployment from months to weeks while maintaining compliance and security
• Why purpose-built data science applications deliver higher ROI than traditional BI dashboards
• Practical frameworks for embedding governance without restricting data scientists' ability to innovate with R and Python
• Real examples from NASA, financial services, and regulated industries that cut deployment cycles while strengthening governance
Who this report is for
Chief Data Officers, VP of Data Science, IT leaders, and Enterprise Architects balancing the need to scale data science impact with governance, security, and operational risk management.
