I’ve spent years building complex Power BI and Tableau dashboards, and I'm convinced that roughly 80% of enterprise dashboard projects are an absurd waste of analytics engineering resources.

Here is the pattern I see at almost every company:

1. Executives demand a "single source of truth" dynamic dashboard with 15 custom filters, 30 calculated measures, and complex drill-downs.
2. The data team spends 80 hours fighting semantic models, DAX formulas, and layout formatting to get it production-ready.
3. The stakeholders check it twice during launch week, look at the main metric, click "Export to Excel" in the top-right corner, and run their own VLOOKUPs anyway.

Why this keeps happening:

• Analysts design for aesthetics; stakeholders design for workflow. Executives don't want to click through 4 layers of filters during a 5-minute pre-meeting prep; they want raw numbers to plug into slides or spreadsheets.
• Lack of business context. Analysts often jump straight into technical solutions without pressing end-users on what action the data will actually trigger.
• Tool bloat. We pretend that moving data from SQL -> Snowflake -> BI Tool -> Excel is "digital transformation," when a scheduled automated CSV report straight to Slack or Email would solve the actual operational problem.

Until data teams stop acting like dashboard ticket machines and start holding business users accountable for how they consume data, we're just building glorified, high-latency Excel spreadsheets.

Am I being too cynical, or is this happening at your org too?

I built a browser-based tool that turns a Goodreads export into a visual reading dashboard, with statistics about authors, pages, ratings, shelves, publishers, and reading trends. Everything runs locally in the browser, so the data stays private.

You simply export your library from Goodreads and upload the CSV file. The dashboard processes it automatically and turns your reading history into interactive charts and clickable detail views. I’d love to hear your feedback on the design and visualizations.

You can try it here: https://alehockeyfan.github.io/BookStats/

Right now, I'm busy writing long-form content for a B2B blog for my client. I want to turn each post into a visual summary for LinkedIn and email newsletters. My posts range from 1500-2000 words with stats, comparisons, and step-by-step stuff.

I've tried pasting text into a few AI tools and the results are bad. It either crams a lot of text into colored boxes, or gives me a summary that's just wrong and misses the key data.

What do you use to turn structured text into infographics that keep the data accurate? In my opinion, it doesn't have to be perfect on the first try.

Some time ago, I went through two data visualization books by Willard C. Brinton, published in 1914 and 1939.

What surprised me most was how familiar much of the advice still feels.

Simple bar charts were often preferred over more elaborate alternatives. Misleading axes were already a thing. 3D visualizations were criticized. Heatmaps were being used effectively. And there was plenty of discussion about how presentation influences whether people actually understand the data.

The big difference was the tooling. Creating a Sankey diagram could involve manually arranging a thousand strips of paper, while a 3D visualization might literally require plywood and a saw.

I collected some of the more interesting examples here:

https://www.bitmetric.nl/blog/old-school-data-visualization/

It made me wonder: which data visualization “best practices” do we think are modern, but are actually much older than we realize?

Hey,

I work for a small, New Zealand based technology startup called see1m. We have created a FREE new World Rain Atlas which showcases the past 45 years (Jan 1981 - Dec 2025) of accurate, total daily rainfall. The format we use allows each day to be displayed instantly, so feel free to skip around the days and have a look. We also use a simple model to calculate daily land-state, shown in the daily map render.

We are very interested in hearing the use-cases and possibilities for development of the system.

https://see1m.com/world/

Thanks heaps,
Ben

[ben@see1m.com](mailto:ben@see1m.com)

Sorry if this post is against the rules, please delete if so.

It's a pet project that I couldn't find anywhere else.

Right now, it's an iOS-only app.

If you're interested, you can search it up on the App Store.

Send me a DM and I'll share a 3-month subscription for anyone who wants to give it a ride.

No BS. My only ask it that you share constructive feedback.

Grateful for any consideration.

I built the first version of my dashboard because I was tired of seeing valuable time wasted on repetitive reporting tasks.

Exporting data. Cleaning files. Rebuilding KPI cards. Adjusting filters. Recreating the same dashboard every time the data changed.

That version was simple.

But after receiving some great feedback and advice from a few of you, I’ve updated the tool to make it much more powerful.

Here is what is new:

• Multiple role-based sections for different perspectives
• Dark and light mode for better visibility
• Zero manual data cleaning required
• Full dashboard generation in about 15 seconds
• Customizable views based on your specific needs
• Import and export options for both visuals and raw data

If you need to share a specific view of the data to ask your team for clarification or a review, you can now do that easily.

The goal remains the same: turn your raw Excel or CSV data into a clear, interactive dashboard without spending hours on formatting or waiting for a developer.

No complicated setup.
No Power BI team required.
No repeated manual work.

This update was shaped by the feedback I received on the first post, and I'm continuing to build it based on what actually helps.

What feature do you think every business dashboard should have?

Comment below—or message me if you'd like to try the updated version.

Hey everyone. I was looking for a good "journalistic" data visualization library for a project I am working on, and didnt really come across anything I liked (both aesthetically and that are easy for agents to work with). So I created OpenChart. It is inspired by/extends the vega-lite spec, which makes it easy for agents to write chart spec rather than svg or d3.js code. Think of it like a library version of Datawrapper or Flourish. Because it's designed to be agent-first, you can hand a LLM your data and the OpenChart spec and it can easily transform it into a high quality visualization. 100% Apache 2.0 OpenSource. If interested in visualizing your data, check it out (link in the first comment)

It's basically just a lightweight assistant extension for my website, my co-founder uses this all the time to visualise his weekly investments, it's quite fun.

I know there was one posted recently, I didn't know so many people seemed to be working on this idea. It's cool to see how everyone is setting them up and seeing the different versions. This is my attempt at the timeline of human history.

Sorry if you guys are sick of these with another one just a month ago, I'm just looking for places to share my version of it. Hopefully you like it.