Category: Data Strategy & Modernisation

Establishing total SAS Viya open-source security requires a dual-layered strategy that governs both runtime code execution and incoming package software supply chains. The integration of Python and R into the SAS Viya platform represents a major step forward for enterprise analytics. By allowing data science teams to blend the distributed, in-memory power of Cloud Analytic […]

The modern enterprise analytics landscape is changing rapidly as teams move away from the rigid debate of proprietary platforms versus open-source languages. For data-driven leaders operating within regulated fields like banking, insurance, or life sciences, the core challenge has shifted completely. It is no longer about choosing a single language but rather establishing a balanced […]

Data teams across Australia and New Zealand are scaling up machine learning. However, technology leaders face a clear challenge. How do you balance data science flexibility with corporate risk compliance? Developers want the agility of open-source programming to build models. Because of this, establishing a secure open source governance layer is an absolute prerequisite for […]

The enterprise analytics landscape is currently grappling with a “Talent Paradox”. To attract and retain the next generation of top-tier data scientists, organisations must offer the tools these professionals natively speak: R and Python. However, for organisations with decades of mission-critical logic built on legacy systems like SAS® 9.4, this demand creates a difficult tension. […]

The Silent Trigger Picture a senior data scientist. They face immense pressure to deliver a predictive model for a mission-critical project. Consequently, they execute a single, seemingly innocuous line of code: pip install package_name. Within seconds, the library becomes available. Development immediately continues. However, beneath the surface, that one command triggers a “Governance Gap.” This […]

The enterprise landscape is shifting rapidly. Currently, 79% of organisations have implemented autonomous agents, yet fewer than 25% have operationalised the necessary controls to manage them. This discrepancy creates a massive operational vulnerability known as the “Agentic AI Governance Gap”. As businesses move beyond experimental phases, deploying AI without robust, enterprise-grade governance introduces severe commercial, […]