Hi Experts,   I need to replicate a SAS 9.4 data environment in a Linux environment. Using rsync -av works like a charm for this.   The challenge I'm facing is that the source environment also contains SQL views in the form: select * from lref.table using libname lref "<source_root_path>/<relative path>"; I need to re-create these views in the target environment by replacing the source_root_path with the target_root_path.   Do you know of any other/better way than executing a SQL describe view, capture the log output in a file, parse out the view definition, change the path and then use this new code to create the views in the target environment?   I couldn't figure out any other way to retrieve the view definition.   Thanks, Patrick ... View more

Hello I am new with ses enterprise 8.3. I opened a sas program and run it. I see a message- sas enterprise guide detected a sas studio enterprise edition 3.6 but requires 3.7 or greater to display sas studio tasks. What does it mean? Is there any bad results for this problem? What action should I do? ... View more

Hi SAS,   The suggestion is the following: Allow to reassign cases to different profiles in Customer Support Center.   The motivation 1: User X has profile [email protected]. X creates a ticket with Tech Support. X resigns form job in "some.mail.com" and want to reassign the ticket ownership to user Y with profile [email protected].   The motivation 2: X creates a ticket with Tech Support using email [email protected]. X does not have profile associated with [email protected] but has a profile connected with other email, e.g., [email protected]. X want to reassign the ticket ownership to the "other" profile.   Currently reassignment is not possible, but it would be very useful.   Bart ... View more

SAS Viya Copilot shows that an embedded LLM chat interface helps productivity inside SAS tools: code generation, explanation, natural language Q&A, and so on. Right now that's only available to SAS Viya 4+ customers, and it's tied to one fixed backend (Microsoft Azure AI Foundry). Many SAS users, especially in regulated industries like pharma, will stay on SAS 9.4 Foundation for years. They have no equivalent native option.   I'd like to propose a chat pane for SAS Studio and/or Enterprise Guide on 9.4 Foundation, similar to Viya Copilot's code assistant. The key difference: the LLM call should be a documented macro hook that users can override, not a fixed vendor backend. This lets organizations point the chat pane at whatever provider their data governance allows: OpenAI, Anthropic,  or a self-hosted model.   Why this is feasible SAS programmers already build this by hand using PROC HTTP and the JSON libname engine to call LLM APIs from SAS 9.4, parsing each provider's JSON response and using the macro language to build prompts dynamically. See "Getting a Prompt Response Using ChatGPT and SAS" (PHUSE EU Connect 2024) (proceedings:) and the related SAS Ask the Expert session, "How Can the SAS Macro Language Enhance LLM Integration in SAS 9.4 for Clinical Programming?" (How Can the SAS Macro Language Enhance LLM Integration in SAS® 9.4 for... - SAS Support Communities) This proves the integration works against SAS 9.4. It just isn't native. SAS already has a precedent for macro-driven, interactive dialogue with a user: %WINDOW and %DISPLAY in the classic Display Manager System. A window pauses execution, takes input, and resumes. They're limited to classic windowing mode and don't render in EG or Studio, but they show the underlying idea (macro code driving back and forth with a user) is already part of SAS's own toolkit. A modern equivalent, a persistent chat pane in SAS Studio and EG, would bring that same idea to the interfaces 9.4 users actually work in. Proposed capability A chat pane in SAS Studio / Enterprise Guide, similar in placement to Viya Copilot's code pane. A documented macro or API hook so the LLM call itself (endpoint, auth, payload, response parsing) is supplied by the customer, not hardcoded to one vendor. Conversation state kept across turns in a session, so follow-up questions keep context. Ability to insert LLM-suggested code straight into the active editor, like Viya Copilot does. SAS already has a way to keep credentials out of the log: PROC PWENCODE. Right now, anyone wiring up an LLM API call by hand has to manage the API key carefully or risk it showing up in plain text in the log or saved code. A native chat pane should use the same encoding approach by default, so this is a known, solved problem within SAS rather than a new one. This closes the AI-assistant gap between SAS 9.4 Foundation and SAS Viya without requiring a platform migration, and respects the need for regulated organisations to choose or self-host their own LLM provider rather than use one fixed backend. ... View more

Strong customer analytics are the foundation of consumer trust, and recent trends with genAI are adding more fuel to the fire. Year after year, brands continue to explore and stress-test new mechanisms in deriving customer analytical insight. A powerful trend in martech today is centering around trust and transparency, which is critical when leveling up to AI marketing effectively.   This concept focuses on data/analytical literacy based on how information is used to build models, accurate interpretation in models themselves, and confidence in a brand's ability to use algorithmic scoring to make intelligent customer decisions. We are excited to announce that in May of 2026, SAS was recognized as a Leader in an influential report entitled the Customer Analytics Technologies WAVE (Q2-2026) released by Forrester Research relevant to the martech and data science sectors.   Image 1: 2026 Forrester Customer Analytics Technologies WAVE Results   According to Forrester's evaluation, " SAS places trust at the center of its strategy ... The company is well-positioned to deliver on this vision by pairing analytical rigor with market‑leading responsible AI practices." The fun didn't end there. As mentioned by Morningstar News, "SAS remains the leader in analytical depth, supporting advanced use cases such as customer segmentation, churn prevention, and customer lifetime value analysis". The report further notes SAS "differentiates through decisioning capabilities that make complex, multistep decisions transparent and governable via visual logic while embedding advanced analytics."   Wow! As awesome as that sounds, let's drill in a bit deeper. The shift from static, retrospective insights to dynamic, in-the-moment orchestration is a theme SAS believes it is well positioned for, with a strategy centered on:   Embedding analytics and decisioning together to drive next-best-action recommendations at the point of interaction Scaling real-time personalization using advanced AI and machine learning approaches Extending analytics into orchestration across other SAS & 3rd party systems and channels   Forrester's evaluation notes went on & shared that SAS has a roadmap that "prioritizes AI investment in core analytics and decisioning workflows." It also states, "SAS is keeping pace with agentic AI innovation, developing multiagent architectures and what it refers to as Retrieval Agent Manager, a no‑code orchestrator for complex agentic workflows."   The hype of martech innovation in 2026 continues to soar, and every technology vendor is claiming to bolster the customer experience with AI. However, the question I want all readers to reflect on is whether your AI initiatives are centered in genAI only, or all of what AI has to offer. At the intersection of data, AI, and marketing, detecting and acting on moments of significance between a brand and a customer matters.   Is all AI built the same? No. Yet it seems every software vendor is pitching it. SAS recognizes the critical importance of serving multiple enterprise personas through augmentation (for example, embedded Agentic AI and natural language explainers to assist users). This spectrum of personas ranges from business/marketing users who want out-of-the-box benefits to savvy analysts and/or data scientists who want to build assets from scratch.    It is extremely challenging for any brand or supporting vendor to predict if a do-it-yourself (DIY) approach vs. a do-it-for-me (DIFM) approach will be more effective.   Image 2: User Personas & AI Assistance   SAS constantly observes, accepts and uses this challenge to inspire our software’s design principles to enable capabilities to reflect the balancing needs between marketers, analysts and data scientists, as well as improve team member interactions with one another.   The language of marketers and customer experience is rooted in use cases and outcomes. Domain expertise, acceleration and simplifying the process of analytically injecting data-driven intelligence into marketing workflows is the desire, and year after year, SAS clients share feedback on this challenge. You want to see your analytical assets bring rewarding impact to your brand, right? You want to observe your efforts making a significant positive difference in customer journeys, correct? Then the democratization of marketing team enablement via customer journey orchestration and prescriptive analytics benefits from speaking their language.   The widening gap between the AI “haves” and “have nots” is especially visible in marketing, where teams face rising expectations. Accountability is intensifying as marketers face mounting demands to deliver more of everything in the future as compared to the past.  Organizations are acutely aware of the need to bridge the gaps in their customer experience. But even with AI, delivering memorable experiences is getting tougher. Personalization is not just a name in a subject line; it’s about creating deep connections. Brands stand out and build loyalty when they successfully deliver relevance and recognition at the right moment.   Image 3: Solutions for Data-Driven Marketing   Our objective is to create synergy improvements between marketers and data scientists while elevating self-sufficiency in running analytics at scale that package the best of SAS capabilities in a simple-to-use interface. In other words, SAS is introducing AI and advanced analytic capabilities FOR marketing use cases acutely. For a moment, reflect on the idea of a software application that is:   Designed for the domain space and themes of martech. Focuses on use cases while minimizing adoption friction related to statistical jargon frequently misunderstood by anyone outside of the data science profession. Uses the best of both worlds - GenAI blended with best-practice machine learning, predictive & segmentation capabilities in a no-code rapid-scoring mechanism that seamlessly integrates with the broader SAS CI360 solution, or external 3rd party martech tools. As we continue to partner, guide and help find incremental value with our customer partners, SAS realizes the time in NOW to release a solution that bridges all the power and innovation of prediction, machine learning, and GenAI together. ... View more