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Posten Bring and Regeneron demonstrate that successful modernization to SAS Viya is not just a technical migration but a strategic transformation that combines cloud scalability, governance, user adoption, and disciplined planning to help organizations become more agile, efficient, and future-ready.
Learn how the SAS Viya MCP Server enables AI assistants like Claude Cowork to orchestrate governed, auditable banking analytics workflows while keeping model execution, governance and oversight within SAS Viya.
The consumerization of health care is no longer a future-state concept. It is here, and it is reshaping expectations faster than many organizations are prepared to meet. Patients today are not just recipients of care. They are consumers comparing experiences across industries. They expect the same level of personalization, convenience
Every missed appointment affects more than one patient. It delays care, leaves valuable clinical time unused, disrupts schedules and creates additional work for care teams already operating under significant pressure. For health care organizations facing growing demand and limited resources, reducing patient no-shows can improve both patient access and operational
There is no question that AI in marketing is here to stay. According to DemandSage, “56% of brands now actively use AI to tailor every customer interaction (content, recommendations and support), while 96% of companies report AI has significantly improved their personalization ROI.” And it does pay off: DemandSage research
AI gets the spotlight. Data does the work. Across industries, organizations are racing to adopt large language models (LLMs), build AI assistants and explore autonomous agents. This ambition is understandable. AI promises faster decisions, new efficiencies and entirely new ways of working. But amid the excitement, many leaders are overlooking
Life places thousands of demands on our bodies every day. Whether it's carrying groceries, moving furniture, climbing stairs, gardening, or simply getting out of bed, our ability to meet those demands depends largely on our strength. Building and maintaining strength not only makes the activities of daily life easier, but
When most people think of their core they think of fitness influencers with 6-pack abs. In reality, the core is much more than that. The core is a complex group of muscles that includes the abdominals, obliques, deep stabilizing muscles, muscles of the spine, diaphragm, pelvic floor, and even muscles
When most people hear the word power, they think of elite athletes sprinting down a field, jumping for a rebound, or exploding out of the starting blocks. While power is certainly important for sports performance, it is also one of the most important physical qualities for everyday life and healthy
A previous article describes the nonnegative matrix factorization (NMF). You can use the NMF to reveal important features in data that can be used to reduce the dimensionality of the problem. NMF is useful when the data are nonnegative, such as counts or pixel values in an image. The goal
For many SAS users, lexjansen.com was simply a destination. It was the place you went when you needed to find that paper you vaguely remembered from a conference years ago, track down an expert's presentation on a niche topic, or discover the best work that had ever been published on
Most machine learning models produce a probability, but many times logic is applied to that prediction to produce a decision. That last logic step often lives in a downstream script disconnected from the model it depends on, easy to lose when the model is refreshed. Using the home equity (HMEQ) dataset, this post walks through a practical alternative in SAS Model Studio. A SAS Code node placed after the modeling node weights the predicted default probability by the requested loan amount to produce expected loss in dollars, and the new Model Registration node (2026.05) accumulates that logic into a single model registered in SAS Model Manager. The result is a model and its decision logic captured as one governed, versioned artifact, so whoever scores the model gets the decision-ready output computed the same way every time.
Small and midsized businesses are entering a defining period for AI adoption. Data, analytics and AI platforms are becoming essential for organizations that want to compete, adapt and grow. These technologies can help smaller businesses improve decisions, automate processes and respond more quickly to changing customer needs. But access to
Open-source technologies have become a standard part of modern analytics, data science and AI. Organizations across regulated industries are adopting tools like Python to accelerate innovation while building more flexible analytics environments. But adopting open-source technologies introduces a new challenge. As organizations modernize, they must also maintain the governance, transparency
Organizations have spent the past few years experimenting with AI. The question now isn't whether AI can generate insights. It's whether organizations can consistently turn those insights into business decisions that create measurable value. That's where many AI initiatives stall. Models perform well in development but never make it to