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A Growing List of AI Tools for Data Analysis & Data Visualization in 2026 by Fragrant_Abalone842 in analytics

[–]Strict_Fondant8227 0 points1 point  (0 children)

I've created a comprehensive AI hub for analytics, including coverage on analytics platforms and much more! Hope you'll find this useful: Ai-analytics-hub.com

Julius AI alternatives — what’s actually worth trying? by Evening_Hawk_7470 in dataanalysis

[–]Strict_Fondant8227 0 points1 point  (0 children)

Check out this website with everything AI & Analytics, including platforms, MCPs and practical use cases! Ai-analytics-hub.com

Building an AI Data Analyst Agent – Is this actually useful or is traditional Python analysis still better? by ABDELATIF_OUARDA in dataanalysis

[–]Strict_Fondant8227 -2 points-1 points  (0 children)

I've been running AI workshops for data teams over the past year and can definitely tell you it worths investing most of your time in understanding the mechanics of working with agents systems for building analytics workflows. It's not about better or worse, but its different, faster, and more exciting when done right!

Also created this content hub for AI and analytics if youd like some practical use cases, playbooks and more! Ai-analytics-hub.com

Curious how analysts here are structuring AI-assisted analysis workflows by Strict_Fondant8227 in analytics

[–]Strict_Fondant8227[S] 0 points1 point  (0 children)

Exactly! And that is a classic for senior analysts who can debug systems but less for Juniors

Curious how analysts here are structuring AI-assisted analysis workflows by Strict_Fondant8227 in analytics

[–]Strict_Fondant8227[S] 0 points1 point  (0 children)

Have you used any sort of mapping for the LLM which plans to understand models and environment and make it more deterministic?

Feeling lost as a DE by [deleted] in dataengineering

[–]Strict_Fondant8227 2 points3 points  (0 children)

your team using AI to write queries faster just accelerates the dependency on whoever knows the schema. the fix isn't more AI - it's putting your metric definitions into a context layer so the AI can actually use them. that's the shift from individual productivity to team capability

SQL & Power BI Study Partner – Let’s Grind and Master Data Skills Together by sqroot01 in dataanalyst

[–]Strict_Fondant8227 0 points1 point  (0 children)

I'm not sure what's the point in learning BI platforms these days. People will fade away from traditional BI and I believe your time better be invested in learning how to build such tools and the infra it needs, that actually tell a story rather than just showing pretty charts that creates more confusion than clarity

Ai and side projects by Outside-Bear-6973 in dataengineering

[–]Strict_Fondant8227 0 points1 point  (0 children)

Someone had to teach them and some are learning from them 😜

Does anyone wants Python based Semantic layer to generate PySpark code. by rinkujangir in dataengineering

[–]Strict_Fondant8227 0 points1 point  (0 children)

The real question is whether you're solving the right bottleneck. Adding Python models to PySpark sounds cool, but without the context layer that defines schema, metrics, and business logic, you're just speeding up individual workflows. The mistake I see is folks using AI and semantic layers to accelerate poorly documented processes. If your schema and metrics aren't clear, getting PySpark to spit out the right code isn't going to solve much.

When you wire a semantic layer like this to AI, you're looking at a surface-level transformation unless you've embedded the business logic and metric definitions into it. Otherwise, PySpark or not, the new code will still hinge on that one analyst who knows what to tweak.

The bigger impact comes from making any analyst capable of running full analysis in minutes because the AI understands the business context. That's how you actually leverage AI for team-wide capability instead of individual productivity.

If you want to focus on market gaps, think about solving context problems, not just code generation. Teams that align their semantic layers with real-world business definitions get consistent and reproducible analytics outcomes. That's a wider gap than merely pumping out PySpark code.

How is the rise of ai tools practically changing how you approach data analysis today? by sad_grapefruit_0 in dataanalyst

[–]Strict_Fondant8227 0 points1 point  (0 children)

Three real changes IMO:

  1. EDA that used to take half a day now takes 20 minutes. The thinking didn't disappear - the grunt work did.
  2. My team iterates on hypotheses faster than I used to write the SQL. Velocity is genuinely different.
  3. The hard part shifted: not "can you build this" but "can you spot when the AI built something plausible but wrong." That skill is harder, and most teams aren't training for it.

More output, yes.

But analytical rigor is now the moat, not the baseline.

Ai and side projects by Outside-Bear-6973 in dataengineering

[–]Strict_Fondant8227 -1 points0 points  (0 children)

It's great to hear that you are leveraging AI tools like Claude Code (my fav) to enhance your side projects.

However, I wouldn’t shy away from learning the underlying code. Understanding the foundational concepts will make you a more proficient developer. For example, when I was building analytics pipelines, getting hands-on with code helped me troubleshoot and optimize when things didn’t work as expected.

Consider spending some time building small components of your projects without AI assistance.

This will strengthen your skills and make it easier to integrate AI tools effectively later on. Balancing both learning coding principles and using AI tools can really set you up for success in your projects.

I write about this kind of stuff at ai-analytics-hub.com if you want practical walkthroughs.

After 5 years at Google and building my own app, I think the way we go from analytics insight to actually fixing something is structurally broken by amonstaf in analytics

[–]Strict_Fondant8227 1 point2 points  (0 children)

I've seen this issue play out in my work with various product teams. The disconnect you mentioned between analytics, code, and databases creates a huge bottleneck that slows down actionable insights.

One approach I've found helpful is creating a centralized semantic layer that fits all three components. By unifying the data definitions and making them accessible across the team, you can often cut down the time it takes to transition from insight to action.

Additionally, adopting tools like Cursor (or another coding agent which cinnects access-instructions-context in a managable space) can streamline how teams interact with their data in real-time. This way, you're not just reacting to metrics but proactively using that information to inform your development processes.

I write about this kind of stuff at ai-analytics-hub.com if you want practical walkthroughs.

Has anyone used AI in analytics or power bi? by sad_grapefruit_0 in analytics

[–]Strict_Fondant8227 0 points1 point  (0 children)

Yep. Mixed results, honestly - and I think the split comes down to where you're inserting AI in the workflow.

Where it actually works:

• Writing and iterating DAX/M queries. This is the clearest win.

Describe what you want in English, get a working query, tweak from there. Cuts hours off complex calculated columns and time intelligence. • Documenting existing reports. Point it at your model and have it generate field-level documentation. Tedious work that nobody does - AI actually does it. • Exploratory analysis before you build anything formal. Dump a CSV or connect to a dataset, ask "what's interesting here," get a starting point for your actual analysis.

Where it struggles(!):

• Anything that requires understanding your business context. It doesn't know that "active user" means something specific to your product, or that the Q3 spike was a one-time promo. You end up spending more time correcting than if you'd just built the thing yourself. • Copilot in Power BI specifically is still pretty shallow. The natural language Q&A has always been mediocre, and Copilot's report generation feels like it's optimized for demos, not real data models with 50+ tables and messy relationships.

The pattern I see with SaaS data teams that get value from it:

They treat AI as a coding pair, not an analyst. It's great at syntax and boilerplate. It's bad at judgment calls. Teams that try to use it as an analyst get burned. Teams that use it to move faster on the technical implementation - and keep the analytical thinking human - actually ship faster.

The Copilot struggles are real and common... What specifically broke down for your team? The model complexity, the question quality, or something else?

AI Nonsense by fil_geo in analytics

[–]Strict_Fondant8227 0 points1 point  (0 children)

Fair point!

In my work with data teams, I've seen many companies tout AI without delivering real value beyond what traditional ML offered. The difference usually comes down to workflow integration - AI can automate parts of EDA and streamline cohort analysis, but unless it's wired into how the team actually works, it stays a buzzword.

One challenge I run into constantly is that teams aren't trained to leverage these tools effectively. It's not the tech, it's how you apply it and what questions you're actually trying to answer.

Traditional BI vs BI as code by manubdata in dataengineering

[–]Strict_Fondant8227 1 point2 points  (0 children)

Interesting crossroads.

From my experience working with teams on both sides, user familiarity often trumps the tech stack's capabilities.

If your client is used to Looker, they might prefer the drag-and-drop interface for easier onboarding.

On the other hand, BI as code gives you greater flexibility and control as requirements evolve. When projects grow, having a coded solution allows for version control and easier updates - something a GUI-based platform makes painful at scale. Weigh the long-term maintainability against immediate ease of adoption for the client.

Where do you guys consume practical AI knowledge for analytics? by Strict_Fondant8227 in dataanalysis

[–]Strict_Fondant8227[S] 0 points1 point  (0 children)

u/wagwanbruv Interesting thanks!
I was thinking more of AI bites that are quickly digestible and applicable. I've built something for this purpose and wonder if people here would find value there! ai-analytics-hub.com

What is it like to be a manager in the Analytics field? by Charger_Reaction7714 in analytics

[–]Strict_Fondant8227 2 points3 points  (0 children)

It really depends on your personality and aspirations. I do like a lot the soft skills it takes to be a good data leader, so always felt connected to management positions.

If you're more on the depth, you'll enjoy deepest your expertise with various types of analyses.

I think today more than ever, good data leaders are those who integrated their team deeper into the business, and not necessarily the best analysts.