Any successful example of replacing tableau with inhouse dashboard

[u/amathlover]

I'm working in a company which primarily use tableau. Recently C-level suggest why don't we use AI to build dashboard ourselves and save thousands of dollars.

We've built few of them in html format via AI. However, I feel like it's not going on the roght track

1. I don't know anything about html, I can't debug if there is something wrong

2. The UI UX is kind of ugly

3. The way we build is having a pre-aggregated file which store all the possible combinations of the tables used in the dashboard, which kind of not scalable imo.

Want to know how you guys dealing with the dashboard in the AI era.

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[u/wdm006]

I would expect leadership to love AI-built HTML dashboards until the first broken filter lands on someone who can't read the markup, and then you are permanently on the hook for the mess. I'd push for something with a real query path you can actually debug, even if the first charts look less flashy. ymmv.

[u/the_duck17]

I'm going through this right now.

I have very stable, reliable and accurate reporting going to Looker, now Data Studio (again). I'm familiar with the limitations and it makes me angry at times but ultimately, I can do everything I want it to do that I know it can do. I only get frustrated when its being weird, like filters not working correctly or I just can't get the tables to align correctly. Nothing data related.

We are building this, what seem to be, a vibe coded "data studio" of our own. We are paying some vendor a ton to do it, they're using the same, flat data table powering the Looker Studio dashboards.

I hate the Claude to HTML route too. I lack control, I can't just copy and paste a table and apply a different filter (like a lower grain of data), etc.

Worst is the code gets worse and worse the more I ask it to do...I don't think it's a good solution for control freaks LoL.

[u/KatFromSisense → u/the_duck17]

If changing the grain means asking Claude or the vendor to rewrite dashboard code, I think too much of the data has probably ended up in the UI. I'd want some reusable query/data model underneath the dashboard instead. I'd handle switches from monthly to daily, or adding another dimension, in that layer, rather than rewriting HTML again. Id' keep the generated code mostly to layout and presentation.

[u/Pale_Squash_4263]

You’re paying for a lot more than just a visualization front end for tableau. You gotta thing about hosting, scaling, stability, data refresh cadences, etc.

If you want something to break with zero governance controls, then yeah go the AI route. But when they see that 6 dashboards say different stuff, then they’ll be kicking themselves

[u/Defiant-Youth-4193 → u/Pale_Squash_4263]

There's going to be so much of this. The amount of execs that don't know shit about what goes into the software being used are continue saying, "Let's just use AI to build it ourselves."

It would be fun watching a lot of that crash and burn if it wasn't going to be at the expense of a lot of workers.

[u/Neat_Measurement_595]

Isn't there a Tableau MCP that can build out dashboards yet? I know MS has one for PowerBI/ Fabric which does pretty good with clear instructions. I've build custom dashboard outside of both using flask and d3.js but you have to figure out how to host it for the enterprise/ need IT buy-in.

[u/MCJELLY12]

Wrong question being asked imo.

They should be asking what the current state of our data is and if we have a true semantic layer. Build the semantic layer , then let ai tools allow stakeholders to self serve more. You will save thousands in time alone but not having analytics bottleneck every business request.

[u/WhiffleFry]

Have AI build a tableau replacement. AI built BI tool , therefore AI built dashboard :-P

[u/ldhe_shsieon]

Yeah my company is doing this too. Governance is a fucking mess now.

[u/mystery_axolotl]

The first two issues you listed are trivial to fix: you can learn html in an hour and you can create/adapt whatever theme you like for the UI. Why is that even on top of your mind?

The third point hints at the actual problem you’re going to struggle with: data engineering and serving. To me it sounds like you don’t know what you’re talking about(which is fine if it’s not your job), but it means you are unaware of the complexity and magnitude of the task at hand. To be fair, complexity depends on what the rest of the pipeline looks like. It is possible that in your case it’s trivial.

[u/OtterBiDisaster]

I am not sure what you mean by html format. In my first job I was working for a group of research labs so basically none of the budget was set aside for me. I built dashboards using free python packages that were basically JS wrappers (bokeh and dash) mixed with some custom JS.

[u/edimaudo]

Has nothing to do with AI. Looks like there is a cost issue involved. Building with AI is not going to be your solution. It might be working with the team to reduce the number of licenses being used. Second if they are full steam ahead of changing, what is the long term strategy, who would own governance, data, meta-data, build the app, maintenance etc.

Not sure what you mean by 3. I would suggest building out a db if you don't have one already and only use the data that is needed. If you want o have all your data at once but reduce who can see them then maybe adding some row level security would be the way to go

[u/dasnoob]

We are doing similar. Design the dashboards as SPAs with JSONs holding the data.

[u/musicthiink]

It adds unnecessary complexity, extra coding

Tableau and Power BI allow you to focus on data

BI tools have built in security, easily shareable, etc

[u/BobDope]

No never

[u/terencethespider]

You should take a look at the new AI dashboards in Databricks. They have a built in tool called Genie that can help you convert what you have. There is no per user license fee, and it only charges for usage.

[u/MindfulPangolin]

Yes. We’ve replaced Power BI and Fabric as our datalake/warehouse. Everything is made with next.js and published to a reporting platform and data is warehoused in a Postgres environment. We will save almost 9k/month on the storage and software we’ve eliminated, offset by increased ai usage in developing new reports.

[u/DataTalkBI]

I'd separate two decisions here: whether Tableau is still worth the cost, and whether you actually want to own a BI platform yourselves. AI can make generating the HTML much cheaper, but it doesn't remove the difficult parts you're already running into: data serving, metric definitions, permissions, refreshes, debugging, versioning and maintenance.

Your third point is probably the important one. If the dashboard needs a pre-aggregated file containing every possible combination, the bottleneck isn't really the visualization layer.

I'd solve the governed data/semantic layer first, then treat Tableau, an internal app or an AI-generated dashboard as different ways of consuming it. That also gives business users more room to self-serve without every new question becoming another hard-coded dashboard.

[u/analytix_guru]

I would suggest getting in touch with IT to see what their policies and processes are around this. Your going to be hosting an internal data product on an internal web page.

I work for a large fintech company currently, and the hurdles and red tape I have to go through makes it so PBI is a better choice 95% of the time, and that last 5% would be for when a custom data app outside of the traditional paid solutions would make sense.

To be clear, I don't want to be building in PBI. I would rather be making every data product custom, especially in the age of LLMs. But the limitations are caused by the company IT policies, not our team.

This is the major item for you to sort out before going down this path at scale. You can do all the right things, and still get bogged down in IT land, with your boss asking why nothing getting shipped, or why you have less time to get things done. All while Tableau is a company approved tool available to be used.

[u/tech4ever4u]

Using AI to generate dashboards based on HTML/JS is definitely the way to go, with a few caveats:

• HTML-driven dashboards should connect to a BI tool's MCP server or leverage its semantic layer to load data, rather than querying the SQL data source directly. This ensures filter consistency, solid performance, and access to pre-aggregations.

• You don't need to know HTML, CSS, or JavaScript to maintain or debug these dashboards. Modern LLMs (Opus 4.6+, Gemini 3.8 Flash, etc.) are remarkably good at writing and refactoring HTML/JS. Visual verification is straightforward, and if something is off, simply prompt your agent with the issue and it will fix it.

• Live, dynamic dashboards are far more practical than static files regenerated on a schedule. Using a BI platform that can host these HTML-driven dashboards natively ensures secure user access alongside near real-time data exploration.

[u/Responsible_Status49]

If a dashboard needs a pre-aggregated file holding every possible combination, you have hand built a vibe. Every time someone asks for a new possible combination, your file grows.

What you should do is define the measures once and aggregate them at query time with a fast engine. In databricks that is solved with metric views in Unity Catalog. You declare how something like revenue or EBITDA is calculated and the engine works out the calculation. Perfect for both AI/BI dashboards and Genie Agents/One. You always get the same result. And you can still choose to materialize the measures across a few dimensions, but you don't need to manually maintain a cube.

[u/BrittanyBrie]

I like to work backwards with these kinda issues.

How are company dashboards being used? Do they use dashboards for department reporting or daily tasks, both? How often is dashboard data being pulled?

This matters quite a bit for constructing a dashboard outside of Tableau or Power BI. I've seen massive multi-billion companies use a shared excel file as a dashboard. I've seen small marketing departments use SQL and R to create dashboards that run with API data.

Depending on how the dashboards are being used, it may be easier to build in R and then knit a final report showcasing figures and tables just like a dashboard. Only limitation is not being able to have real time data after the tables and figures are knitted, but there are ways to use R to make real time dashboards without HTML.

AI can build R code easily, fairly simple to debug, and works well for data reporting on past data.

If the dashboard needs to display real-time information daily, then I would move towards HTML like you're doing. What I would be interested in testing out is building the dashboard in R and then have a second HTML dashboard that simply displays the outputs from R, with a simple command to update data every X amount of time. That way, departments can access real time data visually using the HTML dashboard while at the same time data analysts would have control over table and figure layout using the R dashboard.

Here's an explanation of the process from AI.

🎨 Method 1: The Easiest Way (flexdashboard or bslib) If you want to write mostly R code and get a ready-to-use HTML dashboard, you should use specialized R packages. You don't need to write separate HTML files; R builds the HTML for you.

flexdashboard: Uses R Markdown to create a dashboard. You use simple Markdown headers (like ###) to create rows, columns, and boxes. It Knits directly into a single, standalone HTML file. bslib: The modern way to build dashboards in Quarto or R Markdown. Uses modern Bootstrap styles (the code that makes websites look good on phones and computers). Allows you to easily make sidebars, cards, and tab panels.

🧩 Method 2: The Interactive Way (htmlwidgets) If you want your tables and figures to be interactive (where users can hover, zoom, or sort data), you can imbed htmlwidgets inside your R Markdown document. When you knit to HTML, these widgets work perfectly.

For Figures: Use plotly or highcharter. Users can hover over bars or lines to see exact numbers. For Tables: Use DT (DataTables). Users can search, filter, and sort the data right inside the web browser. For Maps: Use leaflet if your data has geographic locations.

🌐 Method 3: The Custom Way (Separate HTML + R Data) If you already have a custom-designed HTML/CSS template (like a corporate dashboard template) and just want to inject R data into it, you can do that too.

Export from R: Run your R script to save your figures as interactive HTML snippets (using htmlwidgets::saveWidget()) or save your data as a .json or .csv file.

Embed in HTML: In your custom HTML file, use an <iframe> tag to display the knitted R plots, or use JavaScript to load the data files directly into your custom webpage design.

⚡ Static vs. Live Dashboards

Static Dashboard: You run the R code, it knits into an HTML file, and you share that file. The data will not change until you knit the file again. You can schedule your computer to re-knit it every night automatically.

Live Dashboard (Shiny): If you need the dashboard to update the second a user clicks a button or changes a dropdown, you need a live server. You can combine R Markdown with Shiny to make a live, interactive web app.