Category: Data Strategy & Modernisation

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, […]

Welcome to the final chapter of our deep dive into SAS Viya formats. In Part 1, we exposed the “architectural dichotomy” that causes formats to vanish between the Compute Server and CAS. In Part 2, we gave you the tactical tools (PROC CPORT, FMTC2ITM, and the CLI) to migrate your legacy catalogs. Now, we face […]

In Part 1, we diagnosed the “Split-Brain” condition affecting many SAS Viya environments: the architectural disconnect between the legacy Compute Server (which relies on disk-based catalogs) and the modern CAS engine (which demands in-memory libraries). Diagnosing the problem is one thing; fixing it is another. If you try to simply copy-paste your old .sas7bcat files […]

It’s a scenario we see often: Your team has just completed a migration to SAS Viya. You’ve successfully lifted and shifted your trusted ETL jobs. You run your code in SAS Studio, check the log, and everything looks perfect. The data is clean, and your custom formats—those crucial definitions that turn cryptic codes like 1 […]