Hey everyone,

I’m working on an Alteryx workflow that processes a large dataset with several joins, filters, and formula tools. The workflow works correctly with smaller datasets, but when I run it on the full dataset containing over a million records, it takes significantly longer than expected.

I’ve tried removing a few unnecessary tools, but the performance improvement has been minimal.

Are there any best practices for optimizing Alteryx workflows when working with large datasets? Would using In-DB tools, reducing unnecessary joins, or changing the workflow structure help?

I’d appreciate any suggestions from people who’ve faced a similar issue!

As marketing teams increasingly rely on AI agents to analyze customer behavior and drive hyper-personalized campaigns, we’re facing a new set of privacy challenges. Giving autonomous tools access to rich customer profiles is great for segmentation, but it complicates consent and data security.

How is your team drawing the line between leveraging customer insights and protecting user privacy?

• Consent Management: Are you updating your user consent flows to explicitly cover AI processing and agentic analysis?

• Data Masking: Do you have automated pipelines to anonymize or mask Personally Identifiable Information (PII) before letting AI models access the data?

• Vendor Auditing: How are you ensuring that third-party AI marketing tools comply with your internal data governance policies?

Would love to hear what frameworks or tools you are using to manage this!

Data platforms were designed for people, but they are increasingly being driven by AI agents. This shift is becoming a major data governance challenge.

Recent security incidents have highlighted how quickly AI-powered activity can scale, sometimes compromising hundreds of organizations simultaneously. While the headlines usually focus on cybersecurity, data leaders need to address the governance side.

How is your team adapting to this?

• Access Rules: Are you creating separate credentialing and schema restrictions specifically for AI agents?

• Auditing: How are you monitoring autonomous queries versus human-led analytics?

• Resource Management: Have you implemented specific rate limits for agentic workflows to prevent system strain?

I’d love to hear how other data leaders are managing this transition!

I’m training a model, but the loss stops improving after a few epochs. I’ve tried changing the learning rate and training longer, but the results are still poor.

What are the first things you check when a deep learning model doesn’t seem to learn?

These are phrased to encourage discussion and experience-sharing, making them well suited for Reddit or a community forum.

I’m working with Alteryx and have a workflow that processes data from an Excel/CSV file. Right now, every time the source file is updated, I have to manually rerun the workflow.

I was wondering if there’s a way to have Alteryx automatically detect changes to the input file and rerun the workflow, or if I need to use something like Alteryx Scheduler, Windows Task Scheduler, or another workaround.

Has anyone implemented something similar? What approach worked best for you?

This keeps the question:

• Natural and Reddit-like.
• Focused on one problem.
• Open-ended enough to invite discussion and different solutions.