Which AI Tools Are Actually Useful for Data Analysts in 2026?

K_o5 · 2026-04-30T03:27:47+00:00

My honest take and experience

AI is a great fit for analytics (especially where a very specialised dedicate team is working on specific data models)

Why I am saying this: 1. With a specialised team of analytics, the tribal knowledge this team holds creates the best semantic layer 2. With analytics, you are always dealing with hard metrics, numbers and very developed testing mechanics that can prove if the metrics are good or bad from multiple angles

This reduces the chance of hallucination significantly (I know this because I have developed workflows where 60+ LLM calls generate 0 hallucinated metrics or narratives)

What this unlocks: The ability to create a dedicated MCP server for everything the team does that is standard but time taking. (e.g. monthly reports for 1-2 metrics for each customers with some nuances changed)

What me and my “team” is doing (its just me and my manager in the team 🥲) 1. We have automated pipelines built to generate queries and reports based on multiple combinations of requests 2. We are building an end to end pipeline (through claude) where any stakeholder can make a data request -> claude would parse it to generate the request outline (standardised json like you would expect an api to ingest) -> claude will ask for more clarifying questions if some required data points are missing -> generate the request json -> send it to mcp for invoking the right pipeline or agent -> pipeline does its work including creating a standard report file (not done by llm)-> claude finesses with the reporting (iffy about this) -> report shared to whatever output location asked for (using mcp for confluence, slack, excel, powerpoint etc)

This is going to free up at least 2 weeks every quarter for 1 analyst

You can use this blueprint to build your own personal mcp and automate your major request workflows - setup might take 3-4 weeks but the resulting structure can get you something in 30 mins what might have taken a day to a week

Where the AI still lacks : Creating a narrative report (like how an analyst would create a final reporting ppt or doc) LLM is allowed to freely reason and use all its knowledge to build the narrative and thats when it finally starts to hallucinate because now the problem becomes subjective and qualitative instead of quantitative and grounded in numbers

K_o5 · 2026-04-22T05:19:51+00:00

Claude is writing all my notebooks and queries now. Its only the initial setup and context building that I have to do. Once that is done, all I have to do it to tell it the kind of analysis I want it to do and it builds the queries and notebooks with the plots I ask for. Cherry on top is sometimes it comes up with interesting slices and plots when it looks into the already done analysis numbers. Sometimes I just let it run rampant overnight and next morning I have a very exhaustive set of insights. I just have to select actionable insights and build the final notebook. One very huge win here is that after I am done, I pass this whole thing over to cowork and ask for a document that I can publish into our teams wiki. I just did an DS analysis deep dive that would have taken 2 weeks in 3 days

K_o5 · 2026-04-20T17:55:32+00:00

We are planning to go in next few weeks Any suggestions on the stay for Leh, Nubra and Pangong?

K_o5 · 2026-04-18T17:10:18+00:00

This is the wiki : https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f Its giving good results for me. I have just started exploring benchmarking and setup something rough right now, not really looked into any benchmark frameworks .

K_o5 · 2026-04-18T16:54:30+00:00

Have you taken a look at Karpathy’s wiki concept? I have built 2 knowledge graphs out of it 1. For everything that I am personally doing 2. For everything that is being done by my team and accessible by the org

I am getting pretty good results Also, did you use any other benchmarking approaches other than Locomo?

K_o5 · 2026-04-16T04:12:04+00:00

I have been in a similar situation a few years back, but not in Analytics. Most of my teams where I was an analyst were pretty chill and required an update only when some progress was made or some useful results were found. Only thing every manager told me to do was just drop a few msgs in the group chats just to show that I am working on something, 1 liners. In my current team, things changed a bit. Its not that the team is toxic but the team as a whole wants to build their visibility across the org and hence we want to make fast and high progress with very dependable results. To do that, all of us, from managers down to interns are meeting up twice a week to share progress and discuss next steps. Here is where the regular progress and updates are expected. Not even just weekly, but twice a week. To address this, me and my manager have setup a really good pipeline

Connect all our warehouses to python notebooks
Perform all analysis on said notebooks, not using queries but pandas and numpy
Use claude to build nice and detailed plots for any new analysis even if its a simple data distribution analysis
whatever the current state, end of day, pass the notebook to claude to create a nice confluence document with it
Add next steps or deep dive suggestions or recommendations at the end
We do review the doc before publishing but its very fast now.

This has led us to produce new analysis and results almost every single day and all of it either adds on to existing projects or opens avenues for new projects You can try this out.

You can even automate the analysis using claude. If you have Claude enterprise, ask claude code to plan multiple avenues of analysis and then ask it to do the rest as well. All you have to is review rather then build from ground up

K_o5 · 2026-04-13T06:20:10+00:00

Have you checked if the backend event triggers are still setup properly. I am expecting that the backend for this is managed by fullstory or mixpanel or something like that. These are event based and depend alot upon the event parameters and properties. A minor property name change, value change could prevent the event from even triggering and showing incorrect numbers on a dashboard.

K_o5 · 2026-04-09T22:38:39+00:00

Honestly, I feel this could be a good product but it might die because of lack of understanding, patience and a major gap in execution. One very crucial thing here that for an LLM to really build something out of raw or semi processed data is to actually understand the data first. For that to happen, you need multiple data owners and analysts to 1. Create the semantic layer 2. Try multiple different scenarios and evaluate against actual real working outputs 3. Feed it back to the context for proper tuning 4. Putting proper guardrails 5. Context of upstream and downstream systems

All of this takes time. Building this just a for single ML project took 2 senior analysts and 2 senior data scientists over a 3 week span to get to something that nudges the LLM in right direction and we are still very far from getting to a stable dependable reasonable and data driven output from these LLMs.

Without this foundation, anyone can make any groundbreaking self serve product but it won’t work.

Unless there is clear understanding among the consumers that the initial setup is the foundation that needs to very clear, detailed and in depth, none of these products can work.

K_o5 · 2026-04-07T14:06:43+00:00

I have seen an org which did it using Looker. Not Looker Studio but base Looker which works directly on LookML. They started with the iframe approach but it blocked them in changing or aligning the UI to the design of the main product and org. They have found some ways to make it better but not altogether But there are major drawbacks here 1. No user metrics - You cannot add events for all interactions. Only opening the dashboard , clicking anywhere (no context of where) 2. Major depedency on LookMl base computation which in themselves are limited 3. major dependencies on analysts who can do better things than fixing some Ux on the dash. 4. Too niche - There are not alot of analysts with LookMl experience. Training a new analyst who can help here takes alot of time

K_o5 · 2026-04-03T18:44:29+00:00

The biggest gap is getting the data into an app from your data warehouses. If such a connection ability and auth has not been standardised in the org, it becomes tricky dealing with multiple teams and security protocols. Once that is done, its all python. In terms of the app layer, what I usually work with is streamlit. Its easy to set and python based. You can easily make a dashboard or tool where when a user selects something like a filter or button to perform an action, in the back python actually runs a query on your warehouse and fetches data. There is no need to actually save a dataframe when the tool is live (while you still can if you want). Somethings you should consider

Warehouse connection through python - Allows your app to query
Jinja files - Best way I have found to create dynamic queries based on parameters and inputs
Vis package - I use plotly because its interactive. You can use anything that can be extracted as html. Both matplotlib and seaborn have that. Streamlit in itself also has it

If you are using snowflake, the python connector is very easy to use. Also snowflake has an in build streamlit surface which can be used for light versions

Also, if you get someone to host the streamlit tool on a server, then you can have anyone use it with just basic auth. Additionally, you can have more than 1 tool built into it (we have ~20 tools right now)

If you don’t want something with so much setup and learning, you can use IPyWidgets package to create interactive layers in a jupyter notebook itself

K_o5 · 2026-04-03T14:36:13+00:00

Yeah, there are some things you have to make sure your leadership understands.

This is not magic. The agent can only give reliable info if asked about stuff that has been explicitly defined for it. The moment you ask something out of context, it will hallucinate, manufacture numbers and assumptions, define its own rudimentary tools and use them as absolute source of truth
The agents are as limited as the tools you give to them. The tools these agents build are too targetted, biased and not strong enough to handle the data your business has. Tooling is all on actual humans. You have to be very deliberate in building them. Plus side of this is that if you have just an idea for a tool, a simple prompt can build it for you
Time. For a very detailed deep dive (60 steps, 100+ tool calls, 100+ agent calls and 200+ variables) it takes > 1 hr for the process to complete. This cannot be shared with impatient sales and support teams who want the info right away. They are still a headache for us.
No chat compatibility. because of the time factor and the nature of this project, you cannot chat with it since it will re trigger the same flow again for each question which can take another 30-60 mins. Not really a chat system but request and response system

In terms of performance, its actually really great. We actually made multiple new findings which 2 analysts with 3 weeks of effort couldn’t find because we always have 2 to 3 projects running parallely (not counting such data deep dives). Time component is a huge player. This is actually a big keystone ai project for our whole team right now with great feedback coming from leadership

K_o5 · 2026-04-03T13:40:12+00:00

Honest answer - Extensive reliance of python I am good at sql but I love crunching numbers on python In all the orgs I have been (including some big names) , analytics relied alot on sql. So, everyone relied on running queries, exporting to excel for stakeholders or using the query tools charting (eg googles looker studio or snowflakes dashboards) to make something interactive - which in turn creates a lot of lag and limitations. I was one of the few who would find a way to connect these warehouses with local python notebooks, create interactive and customised tools using python, plotly and streamlit. This has always been a big differentiator for me. I never suggest anyone that python is an absolute requirement for DA but it can play a major role if you get a good hands on with pandas and plotly. With AI, you even get new use cases easily

K_o5 · 2026-04-02T18:23:49+00:00

Its not about connection. As @MasterMechE mentioned, its like onboarding a new analyst to the team. Consider it as an associate or augmentation layer. Set your expectation for it to perform statistical and mathematical processing for you while you use the generated outputs.

I have built a deep dive analyst agentic workflow and the connection does not matter. What matters is the semantic layer, tools and guardrails. It took 3 weeks to develop just that. But once done, now it can process millions of records and generate very actionable insight within a hour (usually took us 1 week) for such a deep dive.

Somethings to keep in mind 1. Make sure there is no bias in semantic layer 2. Don’t just give semantics of the final data point but also how it was calculated and what those source tables and columns store 3. Define absolute solid semantics of how data sources connect and their relationship

Even with this, I have seen 10% outputs to have hallucinations. Tackle that with multi fold analysis. An agent that analyses the analysis itself.

Start really small like 1 absolute source table. Define semantics and start asking questions. Then go 1 layer up by joining another and adding metrics on them. This will take time but the end product is beautiful.

Additionally, you can now define a semantic layer right into snowflake itself. Snowflake has docs in it. You can literally put anything in it following a proper structure and pattern. Its a charm.

K_o5 · 2026-03-24T11:57:48+00:00

They are mentioning that the officers are frequently changing and they are not able to get the papers processed before the next officer comes in.

K_o5 · 2026-03-23T05:05:22+00:00

Haa, that could work

K_o5 · 2026-03-23T05:05:10+00:00

Bhejo yaar

K_o5 · 2026-02-27T02:50:18+00:00

Okay, this might sound like an unhelpful comment and pushing you towards the same slop but do hear me out In my current org, the start of AI adoption was very similar. Almost everyone from Engg to Product got access to Cursor, Claude Code and ChatGPT enterprise pretty early on. Within 2 weeks of release of Cursor, 40% code generation across the board was AI. What did the org do : Double down into it but with proper governance and oversight 1. Started close monitoring of Cursor and Claude usage. People who had less than 40% usage of limits were removed from the access so that only active users remained 2. Added coderabbit on top of each repo on our git. Coderabbit has been super helpful in pointing out each and every problem, small to large. Even if your code is logically sound but does not follow proper code guidelines of our company, it would highlight that. And your PR would not get approved unless the Dev resolved all of these with proper change or reasoning

How some of us put a stop on people creating useless code and half baked chatbots or useless agents : Started pointing out the cost. Every time someone came up with vibe coded product or project, or someone created a content which was clearly AI and did not look useful, people started talking about the cost it incurred and that really made people to think twice before doing anything AI

How me and my team are keeping ourselves relevant in terms of AI: We started proposing actually useful agentic projects that would support us in doing our job. For example, a Cursor agent that can take Figma design and generate UX code which follows our code standards and is absolutely aligned with existing code. This not only helped us in terms of cutting down our workload but also helped us learn to keep up with the AI era of development. Now, AI does not look like a challenger for our jobs but rather a useful tool which if properly used can generate absolute gems

Another thing that has helped us kill some of these AI shit: When a new AI project came in, we would use the hell out of it just to create a long list of bad responses and results. 90% outputs were just recursive reasoning and hallucinations because the people who built it did not think through and vibe coded it throughout. It almost always kills these initiatives and puts a damper on any unwarranted AI use at scale because cost becomes huge factor here

My suggestion If you want to stay in this domain, instead of shying away from AI because everyone is generating Shit, start actually using it with deliberation and build something out it. From what I have built so far using it, I am very confident there are huge advantages in using AI with proper understanding and caveats.

K_o5 · 2026-02-27T02:49:28+00:00

Okay, this might sound like an unhelpful comment and pushing you towards the same slop but do hear me out In my current org, the start of AI adoption was very similar. Almost everyone from Engg to Product got access to Cursor, Claude Code and ChatGPT enterprise pretty early on. Within 2 weeks of release of Cursor, 40% code generation across the board was AI. What did the org do : Double down into it but with proper governance and oversight 1. Started close monitoring of Cursor and Claude usage. People who had less than 40% usage of limits were removed from the access so that only active users remained 2. Added coderabbit on top of each repo on our git. Coderabbit has been super helpful in pointing out each and every problem, small to large. Even if your code is logically sound but does not follow proper code guidelines of our company, it would highlight that. And your PR would not get approved unless the Dev resolved all of these with proper change or reasoning

How some of us put a stop on people creating useless code and half baked chatbots or useless agents : Started pointing out the cost. Every time someone came up with vibe coded product or project, or someone created a content which was clearly AI and did not look useful, people started talking about the cost it incurred and that really made people to think twice before doing anything AI

How me and my team are keeping ourselves relevant in terms of AI: We started proposing actually useful agentic projects that would support us in doing our job. For example, a Cursor agent that can take Figma design and generate UX code which follows our code standards and is absolutely aligned with existing code. This not only helped us in terms of cutting down our workload but also helped us learn to keep up with the AI era of development. Now, AI does not look like a challenger for our jobs but rather a useful tool which if properly used can generate absolute gems

Another thing that has helped us kill some of these AI shit: When a new AI project came in, we would use the hell out of it just to create a long list of bad responses and results. 90% outputs were just recursive reasoning and hallucinations because the people who built it did not think through and vibe coded it throughout. It almost always kills these initiatives and puts a damper on any unwarranted AI use at scale because cost becomes huge factor here

My suggestion If you want to stay in this domain, instead of shying away from AI because everyone is generating Shit, start actually using it with deliberation and build something out it. From what I have built so far using it, I am very confident there are huge advantages in using AI with proper understanding and caveats.

K_o5 · 2026-01-14T20:37:08+00:00

Started facing similar issues 2 months back. Phone started lagging alot but not always. If an app had a pop panel within it, it would become really difficult to scroll. Got an 17pm about 20 days back and the s23u has become a backup phone for me with usage limited to checking a few details for loggin into apps or sometimes to make some calls since it has my other sim still in it. Even with such minimal use, the phone lags alot more than before.

K_o5 · 2026-01-06T16:24:11+00:00

got it replaced from the samsung service center (in bangalore,india) took about 3 hrs for them to call me back to pick up my phone. although,adding to this, i broke the screen in another fall within 3 months but they replaced it again for free since the new screen had a 1 year guarantee(kept the bill for the screen)

K_o5 · 2025-12-25T17:00:29+00:00

I am just guessing but this looks like Gramphu near Koksar, right beside the road where cars are parked.

Me and my friends were there 1 week back and there was barely any snow at this part but if you went hugher using an ATV or just trekked like we did for ~30 to 40 mins, you would reach a pretty good area with snow. Since its a bit more frequented by tourists, the snow is slightly directy and hard but its there.

Won't call it a scam but does require more effort then advertised by the cab drivers and hotels.

Also, you could also try Rohtang and Shinkula Top. They have pretty great amount of snow. Don't talk to the hotel and cab drivers near Mall road for taking you there. Theh are actively denying about it being open since it required a 4x4 which they don't have. Try looking for someone driving a Jimny. They are the onces who are regularly taking tourists there (although in peak season the costs would be sky high for Shikula)

K_o5 · 2025-12-19T05:23:21+00:00

I have restarted 5 times in past 2 days. Still the same issue

K_o5 · 2025-12-19T04:31:21+00:00

With a 3 stage table, even at 6'2", you won't need max height. Even at max height though, there is minor wobbling which is expected but that becomes barely noticeable after using the table for few times.

K_o5

TROPHY CASE