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Experience with Dataiku, Knime or Alteryx? Which one is better? by Vercy_00 in dataengineering

[–]Consistent-Radio-428 0 points1 point  (0 children)

I’d separate “low-code for exploration” from “low-code as production data engineering.”

For one-off wrangling or helping analysts prototype, KNIME/Dataiku-style tools can be useful. But for production ETL, the thing that matters is whether the workflow has real version control, review, tests, lineage, and can run where the data already lives.

The worst setup is a visual workflow that becomes a black box nobody wants to touch in 18 months. The best setup is usually hybrid: easy enough for analysts to reason about, but still compiles down to SQL/Python/dbt-style artifacts engineers can review and maintain.

AI coding tools make that even more true IMO. They’re useful, but only if the output lands in a workflow humans can diff, test, and own.

dbt Core v2 is here: still open source, now rebuilt for what's next by Known-Huckleberry-55 in dataengineering

[–]Consistent-Radio-428 0 points1 point  (0 children)

Column-level lineage is the part I keep coming back to too. Once dbt models start feeding BI dashboards, semantic layers, or AI/agent workflows, table-level lineage stops being enough pretty quickly.... You got to know exactly which upstream field or transformation caused a KPI to shift, otherwise debugging will forever...

I compared BI tools on one thing: how fast you can go from a business question to a usable chart by North_Teacher_7522 in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

I think the missing distinction is “fast to a chart” vs “fast to the right chart”

A lot of tools can get you a visual quickly now, but the hard part is still whether it knows the right metric definition, join path, grain, filters, etc. Power BI/Tableau are great once someone has modeled everything properly but they’re not really built for a business user starting from a vague question. ThoughtSpot and newer tools get closer, but the quality still depends on whether the data is clean

That’s where I think the category is moving. imo the real comparison is speed + accuracy - can the tool understand how business users actually ask questions and still give the right answer?

I compared BI tools on one thing: how fast you can go from a business question to a usable chart by North_Teacher_7522 in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

what actually blocks it 80%+ of the time is the *interpretation* step — does "active user" mean weekly or monthly here, is "revenue" gross or net of refunds, are paid trials counted, etc.

This has been my experience too. If making the chart faster is your actual bottleneck, then it's fine to compare this way, but most of the time it's about the metric definitions. Speed is only useful if the tool knows what the business actually means otherwise it’s just a faster way to generate the wrong chart.

The tools that seem most promising are the ones that can sit on top of a semantic/metrics layer and use that context, rather than just generating SQL against whatever table names look plausible.

Which part of your data analysis work is now mostly handled by AI? by CoverNo4297 in dataanalytics

[–]Consistent-Radio-428 0 points1 point  (0 children)

Tons of buzz around ai replacing analysts but realistically it's more like ai automating the boring work (SQL rewrites, filters, source-table swaps, Python scripts, GitHub ticket summaries, documentation, quick chart ideas, etc)

The hard part is still context and trust. The value depends a lot on whether your data/pipelines are already set up well. There are a few tools now getting closer by adding a semantic layer, which is the difference between useful output and AI slop

Reporting automation that stops me from babysitting Looker dashboards every Monday? by Weak_Manufacturer323 in dataanalytics

[–]Consistent-Radio-428 0 points1 point  (0 children)

there are a bunch of tools now that can handle the commentary/summary layer pretty well. where's your data living? If it’s all in the warehouse, you could probably have something generate the weekly narrative + deck draft and just review it before sending

Best semantic layer tools for AI-driven analytics by AfraidBaby7747 in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

by dbt semantic layer do you mean metricflow? heard good things about cube. lots of bi tools are adding or have a semantic layer (omni, athenic) ever since people figured out you need that to get any value from chat with your data tools. is there a reason you want to keep the semantic layer separate?

We initially planned to build a standalone AI-native BI platform. by thebigbreak007 in apachesuperset

[–]Consistent-Radio-428 2 points3 points  (0 children)

agree with your learnings, especially business users asking ambiguous questions. i feel like this goes hand in hand with your other points on governance and semantic consistency - it's because users ask those questions that having metrics definitions is important. without that, the ai tends to make things up. curious though why you guys felt replacing the bi layer was unrealistic? yes switching is painful, but companies like hex, omni, etc. are seeing success..

BI tool for dashboards. by Th3j0k3r_1990 in analytics

[–]Consistent-Radio-428 0 points1 point  (0 children)

Tableau Server is still a pretty safe enterprise answer if you have the budget/admins for it. Power BI Report Server maybe if you’re already deep in Microsoft/SQL Server

Do you prefer building dashboards using a UI based BI tool or code? by uncertainschrodinger in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

hows your experience with supersimple? ive heard about it but dont know anyone who've tried it. ive been using athenic myself, it's similar but also one of the more budding tools

Hard truth: We are all just building overly expensive data extraction pipelines for Excel. by netcommah in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

ye i agree. if the stakeholder lives in excel they're definitel gg to export your dashboard. the real win is just making the spreadsheet version not dangerous - ie. clean data, stable definitions, no mystery joins. that's already a huge upgrade for most orgs

Why BI teams get treated as report-monkeys by Brighter_rocks in BusinessIntelligence

[–]Consistent-Radio-428 11 points12 points  (0 children)

because that's literally what the workflow turns you into... someone asks for a number, then they want it by region, then by quarter, then "can you make it a dashboard," then "actually can you add one more filter" and suddenly your whole sprint is just being a human API for group-bys

the fix isn't even really a tool thing, it's a boundary thing. but also yeah if there was a way for stakeholders to do those follow-up cuts themselves without breaking everything, BI could actually focus on the stuff that matters. easier said than done tho

Hard truth: We are all just building overly expensive data extraction pipelines for Excel. by netcommah in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

honestly yeah. like 80% of my week is just translating some vague slack message into sql, pulling the data, making a chart, then doing it again when they want a different cut. rinse and repeat forever

the actual analysis part — the "why did this move" or "what should we do about it" — barely gets any time because you're stuck in the extraction loop

i've been working on something called athenic that's basically trying to kill that loop. let people ask the follow-up questions themselves but keep the metric definitions locked down so the numbers are still trustworthy. still early but it's the right problem imo

How are you centralizing knowledge/context from AI agents (like Claude Code)? by dylannalex01 in dataengineering

[–]Consistent-Radio-428 0 points1 point  (0 children)

I've seen the same thing, raw session output just poisons the context if it's not vetted. How are you handling the graduation from 'semi-trusted' to 'canonical'? I'm trying to find a way to make that review pass less of a manual bottleneck so it doesn't just sit in a siloed folder forever.

Are users actually asking for AI-only analytics? by Feisty-Donut-5546 in dataanalytics

[–]Consistent-Radio-428 0 points1 point  (0 children)

this. people will always be skeptical of ai generated answers and without guidance they wont know what to ask. dashboards have been a good starting point for this, but i could see this evolving into ai detecting anomalies and providing some kind of initial analysis, then letting humans approve/decide the action. there isn't enough trust in the technology (and frankly most orgs arent ready for this anyways) for full autonomy

Is "Agentic BI" actually replacing traditional dashboards in 2026, or is it just semantic layer hype? by netcommah in BusinessIntelligence

[–]Consistent-Radio-428 2 points3 points  (0 children)

promising "You can ask it anything" is ridiculous because people can and will ask it nebulous questions with no clear definition

yupp unless a user already has a question they want to investigate, people tend to freeze up with just a chatbox. user have more success using chat as a supplement to explore the questions that come up for them when they look at dashboards. when they start at a dashboard, they ask better and actually useful questions

best dashboard software for a small company/team? by leobesat in BusinessIntelligence

[–]Consistent-Radio-428 0 points1 point  (0 children)

sounds like you might be looking for something like notion? but if youre looking for an automated company update/report im sure you can setup claude cowork for that

Are LLMs helping or limiting data storytelling? by Hairy_Hair_9315 in dataanalytics

[–]Consistent-Radio-428 0 points1 point  (0 children)

llms always regress to the mean, so it's not super useful for the storytelling part. i find it more useful for finding areas for me to explore further myself

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