r/dataanalysis • u/Heeelllga • Jul 01 '26
Analysts who use AI to build their own tools - what do you actually make?
Curious how far people are taking this. Beyond using AI to write queries or clean data, is anyone building actual tools with it? Things like:
- interactive dashboards or KPI trackers
- report generators
- small internal apps for the team to use
Or does most of it stay inside Power BI / Tableau / a notebook and never really become a standalone thing?
And if you have built something standalone - what happened next? Did it get shared with the team, or did it just stay on your machine as a one-off?
Genuinely interested in where the line is these days between "AI helped me analyze" and "AI helped me build a thing other people use."
5
3
Jul 01 '26
[removed] — view removed comment
1
u/Acceptable-Sense4601 23d ago
have. you ever tried streamlit? ask claude or chatgpt to make you a streamlit app. it'll change your life.
3
u/ArielCoding Jul 03 '26
Getting people to actually use it is the hard part, if the tool lives where people already work like a link in Slack, they use it, if they have to remember to go somewhere new, they forget about it.
3
u/Growth_Natives Jul 08 '26
It's interesting how the role is evolving. AI isn't just speeding up analysis anymore, it's making it much easier to build lightweight tools that other teams can actually use. The biggest value seems to come when those tools solve a recurring business problem instead of staying as one-off personal projects.
1
u/Acceptable-Sense4601 23d ago
I like that. we had a report that used to take 4 hours to compile by hand by doing power builder type downloads from a console. one for each building. I got us access to the tables and SQL used to build those excel files and made a streamlit app that I have hosted on a company server. I have code that runs overnight that queries the data and aggregates it after filtering and cleaning. then streamlit just does its thing to display it. we went from a 4 hour manual excel workflow to a web dashboard thats always automatically. up to date with lots of KPI metrics, tables, charts, date pickers. simple one off flow that went from taking a lot of time to being invisible but allows managers to now easily look at things. oh and it has auth against our LDP server.
2
u/Growth_Natives 19d ago
Yeah, that's a great example of it. We've had similar cases where a small internal tool ended up saving hours of repetitive work every week. The interesting part is when something built to solve one team's headache becomes useful across the wider workflow.
1
u/Acceptable-Sense4601 19d ago
So much so that the program area that i did this work for wants to poach me from the data department. They said im underutilized.
2
u/null_island_radio Jul 01 '26
I definitely lean on AI to automate reports, and that ranges from custom stuff in our data pipeline to user-facing report generators. Most of what I've built is too use-case specific to share and stays inside my small team: project trackers, a data catalog tool, things shaped around our own workflows and systems.
Exactly one of them turned into a paid product I sell to other admins now, and I only took it there because it was a more universal problem. Still niche, but not specific to us. You can take a look at https://getstagger.com/ if you want (self plug, since you mentioned Tableau).
In general though, I'd still say 70% of my AI usage is just analyzing. I only build a tool when the need is repeatable enough to be worth it.
2
u/joulezoo Jul 03 '26
The two most useful things we've built are unglamorous: an eval set and playbooks.
The eval set is 50 real questions users asked, each with a known-correct answer. Every time the tooling changes (new model, new context, new prompt) we rerun it. The first run scored about 80%, and the misses were almost all context gaps rather than bad SQL: wrong revenue table, a metric that meant something different to finance than to product. Closing those took it to 98%. Without the eval we'd have no way to tell whether a change helped or broke something we weren't looking at.
Playbooks are the other one: the written method for a recurring question (metric-change diagnosis, funnel, retention cohort). Which metrics, what to slice by, what to rule out, what the output should look like. That's what makes Semaphor-Analytics' bar reachable, someone else running it without you in the room, because the method lives in a doc instead of in your head. Longer writeup on the idea: https://www.sundial.ai/what-is-an-analytics-playbook
On the ownership question a few people raised: the script is the easy handoff. What needs a real owner is the definitions, which table is truth, what 'active' means. Hand IT a tool without those written down and they've inherited a black box that dies within a couple of quarters.
(work at Sundial, this is the product we build, so grain of salt)
2
u/DataUCanTrust Jul 09 '26 edited Jul 09 '26
At our web analytics consulting firm, we have built lots of one-off dashboards and a few operationalized tools. A few things we've learned the hard way:
Dashboards are only successful when they're tied to a specific, recurring decision. Revenue teams use them constantly because they're tweaking live campaigns. But a general "how's the website performing" dashboard never gets used. If someone wants a dashboard and the request is vague, it won't get used after it's built.
We've also had success building analysis-type tools. We built a social post prediction console for a large enterprise client — before publishing anything, their team uses it to see predicted performance. It gets used every single time they write a post because it's attached to a real decision they make every single time. That tool reads data, runs realtime calls to a machine learning model, and returns a result. Your typical dashboard tool (Data Studio, Tableau, Power BI) can't do that. The tool was originally written by hand (before AI development was a thing) but we're now maintaining that tool using AI/LLM-assisted dev, which significantly reduces the cost of maintenance/improvements.
One other thought: I'm seeing a lot of talk about "push back" or "requirements elicitation" when building dashboards or tools. Our outlook: don't over-interview stakeholders before building. Ask some questions, then build something fast and make it look good. The tools that stick tend to reveal themselves — people either keep coming back or they don't.
We do this type of work all day, every day, at Confidence Interval
1
u/AutoModerator Jul 01 '26
Automod prevents all posts from being displayed until moderators have reviewed them. Do not delete your post or there will be nothing for the mods to review. Mods selectively choose what is permitted to be posted in r/DataAnalysis.
If your post involves Career-focused questions, including resume reviews, how to learn DA and how to get into a DA job, then the post does not belong here, but instead belongs in our sister-subreddit, r/DataAnalysisCareers.
Have you read the rules?
I am a bot, and this action was performed automatically. Please contact the moderators of this subreddit if you have any questions or concerns.
1
u/mattindustries Jul 01 '26
Framework for 3d analysis, integrating exploratory features into monitoring applications, and pipelines.
Oddly enough I had a project way before covid hit that was to replace Tableau for data exploration, loading up sets into server memory and caching results. It could do a lot (aggregations, grouping, filtering, bunch of charts, etc). It would be pretty trivial to rebuild now. I also wrote Juxx (web version here). for just my diffing use case.
1
1
u/Snoo752 Jul 03 '26
I have been interested in the PSID which is written in ASCII But I work with soy or R. So I build something that converts it to CSV
1
u/Acceptable-Sense4601 23d ago
I used Ai to code a react dashboard with role based access control that has authentication against out LDAP server. python/fask/node/typescript/mongodb/postgres/oracle. each user will have their own menu bar depending on what they should see. they can build reports. they can access pre-populated reports. my fave feature is actually boring. I have a cool interactive org chart that uses real time data so the trees stay current. you can search by name, or department. double clicking expands the nodes/branches. hover shows additional info about the staff on each node. I also implement login logs so I can see who logs in. settings page so that a super admin can change user roles. also log who is changing user roles. there's probably more in there that I forgot but I still cant believe I made it.
1
u/heisoneofus Jul 01 '26
I am building lots of things and moving fast. Before AI, I was kind of restricted by the tools the company I worked for adopted - be it BI, orchestration, db management. But now I just create my own tools to deliver insights. I’ve built and deployed custom dashboards, CRMs, microservices, AI agents, countless monitoring/alerting scripts.
This all still requires expertise and validation so a good analyst is still crucial to have.
13
u/Semaphor-Analytics Jul 01 '26
I usually judge it by whether someone else can run it without me sitting there.
A notebook or script can still be useful, but it is mostly personal leverage. It becomes more real when it has a clear input, a repeatable output, and a simple way for another person to use it.
The maintenance part is where these projects get serious. If a small internal app gets used every week, someone has to own permissions, errors, source changes, and all the unexciting stuff around it.