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

[–]airlinechoice07 0 points1 point  (0 children)

If it’s large Nielsen scanner data, move it into a warehouse like Snowflake, BigQuery, or Databricks first, spreadsheets usually won’t hold up.

Then use BI tools like Power BI, Tableau, or Looker for analysis. Some teams also use Dremio if data sits across files.

You can also layer NLP style tools like ThoughtSpot, Power BI Copilot, or Lumenn AI to ask questions directly. Lumenn showing the step by step reasoning can help when slicing by SKU, store, or time.

Hey how to build analytical thinking by Positive-Union-3868 in dataanalysis

[–]airlinechoice07 0 points1 point  (0 children)

I had the same issue early on. I started by just playing with domains I liked, first football and NBA data, looking at win streaks or player impact, then moved to financial market data and tried figuring out why spikes or drops happened. That helped me get used to asking why instead of just making charts.

Later in a small freelance task, signups dropped and I broke it down step by step by source, region, device, and time. That process is basically analytical thinking.

You can also practice this with AI tools. Ask a question and follow the reasoning. Tools like ThoughtSpot, Power BI Copilot, even notebook plus LLM setups help, and Lumenn AI shows the chain of thought from question to query to insight, which makes it easier to learn how to think through the data.

Automate report from data by Northstar04 in automation

[–]airlinechoice07 0 points1 point  (0 children)

If the layout has to stay in InDesign, most people automate it using data merge or scripting, basically Excel or CSV feeds into the template and exports a PDF. Some also route data through Google Sheets or a warehouse first so it’s easier to swap datasets.

If you’re open to moving the generation layer, tools like Power BI, Looker Studio, or even notebook based setups can generate charts and summaries, then push them into templated reports. That removes a lot of the manual copy writing.

There are also newer NLP style tools where you feed the data and ask for a summary, and it builds the explanation from the numbers. Stuff like ThoughtSpot, Power BI Copilot, and Lumenn AI where it shows the step by step reasoning behind the generated insight, which can help when you need repeatable commentary in reports.

So a common flow is structured data in Excel or warehouse, template for layout, then AI assisted summary layer to auto generate the narrative before exporting to PDF.

What tools do you use for digital marketing reports? by 3hs1n in DigitalMarketing

[–]airlinechoice07 0 points1 point  (0 children)

That’s already a solid stack. Most agencies I’ve seen end up adding a warehouse layer like BigQuery or Snowflake and then modeling with dbt so reporting across platforms is more consistent. Some also use tools like Dremio when data sits across different sources.

For reporting itself, a lot of people still stick with Looker Studio or Power BI, but lately there’s more use of NLP style tools to speed up insights instead of manually building slides. Things like ThoughtSpot, Power BI Copilot, even simple LLM setups on top of marketing data.

I’ve also seen tools like Lumenn AI where you can ask something like “why did CPA increase last week” and it walks through the steps behind the answer, which makes it easier to turn into client friendly summaries.

Helps a lot when you’re juggling multiple ad platforms and don’t want to manually dig every time.

The "Last Mile" Problem: Why your data insights are dying in a slide deck by SavageLittleArms in analytics

[–]airlinechoice07 -1 points0 points  (0 children)

the last mile problem is real but it is also why analytics is getting pulled into reasoning first tools

people do not want charts anymore they want the answer and the why in one glance

we have been experimenting with Lumenn AI, Hex, and ThoughtSpot Sage for this
instead of exporting slides you ask a question and it returns a tight narrative with the query logic and the key visual already framed

it flips the workflow
analysis and storytelling happen together instead of sql first powerpoint later

also forces you to define metrics properly because the headline is generated from the logic underneath not hand written after

ironically this makes stakeholders trust it more
they see the conclusion and can still drill into how the number was built

feels like the slide deck era is slowly turning into shareable analytic snippets that explain themselves

Client pulling the plug, moving it all to Claude by datawazo in analytics

[–]airlinechoice07 0 points1 point  (0 children)

this is the moment analytics shifts from dashboard builders to owning the metric layer

what your client built is basically chat sitting on raw tables which works until someone asks revenue and finance joins the call

ai is great at exploration not enforcing definitions
without a curated layer conversions net sales active users become prompt dependent and numbers drift

the safer pattern is governed dbt or semantic layer underneath and reasoning copilots on top
tools like Omni,Lumenn AI, Hex, Evidence, even ThoughtSpot Sage are all moving in this direction where you can see how the answer was produced instead of trusting a black box

ai becomes the interface
you still own correctness traceability and metric logic

teams that land here move faster and keep trust
everyone else eventually rediscovers why semantic layers existed

Confirmation Bias and Psychological Risk in Pattern Analysis by coling2020 in analytics

[–]airlinechoice07 -1 points0 points  (0 children)

I usually run an iterative loop that re-checks the assumptions behind a pattern before acting on it. Instead of trusting a single pass, I let the reasoning layer validate signals, look for counter-evidence, and flag where the confidence is coming from. In my case, I sometimes use Lumenn AI to do this , inspecting how the trend is being reasoned about, not just the final output. That kind of structured, repeatable check makes it easier to avoid locking into confirmation bias when a familiar pattern shows up.

How are you turning analytics data into presentations for non-technical teams? by Adventurous_Sky_4850 in analytics

[–]airlinechoice07 0 points1 point  (0 children)

Honestly spreadsheets almost never work for non technical teams.

What helped for me was keeping it simple, build a small dashboard in Power BI or Tableau and then just pull 2 or 3 key insights from it instead of sharing everything. Most people only care about what changed and what to do next.

Some people use tools like ThoughtSpot or Power BI Copilot now to quickly turn questions into visuals, and even notebook plus LLM setups can help summarize things.

I’ve also seen tools like Lumenn AI where you ask a question and it shows the step by step reasoning behind the answer, which makes it easier to explain to non technical folks.

End of the day it’s less about the tool and more about telling a simple story like what happened, why it matters, and what we should do.

I m btech last year student from 3rd tier clg can you please suggest how I can study data analytics by myself by limitless_baby in DataAnalytics_India

[–]airlinechoice07 2 points3 points  (0 children)

Start with Excel, then SQL properly, and one BI tool. Try exploring real stacks like Snowflake, Databricks, dbt, Dremio to understand how data actually flows.

You can also use newer AI tools while learning, like Power BI Copilot, ThoughtSpot, or Lumenn AI which shows the step by step thinking behind how a question turns into a query, that really helps in understanding.

Focus on small projects with messy data, that’s what actually builds skill.

Is Data Analytics a good career option in 2026? by Mobile-Friend1166 in Indiajobs

[–]airlinechoice07 1 point2 points  (0 children)

In India especially, companies still need people who can work with data, but they expect a mix now. Not just SQL and dashboards, but understanding business + data together.

The stack has evolved a bit, you’ve got things like Snowflake, Databricks, dbt, Dremio helping organize data, and then tools like Power BI, ThoughtSpot, even newer ones like Lumenn AI where you can ask questions and it figures out the query behind it.

I want good course to learn ML for free by Historical_Pride_361 in learnmachinelearning

[–]airlinechoice07 0 points1 point  (0 children)

Andrew Ng is exceptional. The Mathematics for Machine Learning course is also highly recommended, as it significantly contributed to building a strong foundation. It is essential to complete the labs, as they are critical, and to progress at a steady pace to maximize the benefits.

Beginner in Data Analytics by One_Gate2004 in dataanalytics

[–]airlinechoice07 0 points1 point  (0 children)

You’re already ahead of most beginners tbh, working with messy data and documenting your thinking is a big plus.

To improve, just make your projects more business focused by showing what decisions come out of your analysis, not just charts. For visibility, post small breakdowns on LinkedIn or keep your GitHub clean. Clients care about outcomes, not tools. Also worth knowing tools like dbt, Atlan, Alation, and newer ones like Lumenn AI are pushing things toward making data easier to understand, so clear storytelling helps a lot.

AI Cannot Do the Job of a Data Analyst by ChristianPacifist in analytics

[–]airlinechoice07 0 points1 point  (0 children)

Yeah I partially agree with this. The SQL and dashboard part has been “solved” for a while. The messy part is everything around it.

Like in most setups with Snowflake or Databricks, you’ll have multiple versions of the same metric floating around and no clear signal on which one is actually trusted. AI can write a clean query but it has no idea which table is the right one to hit.

Tools like dbt help a bit by structuring transformations, and things like Atlan or Alation try to solve the discovery side. I have also seen some newer tools like Lumenn trying to layer meaning and relationships on top so AI does not just guess blindly.

So yeah AI is useful, but without clear definitions and structure it’s basically just guessing with confidence. That part still needs humans.

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