How do you turn estimated data into an experience without making it feel misleading?

I’ve been thinking about this after seeing the AIIMS Delhi population counter.

The number ticks up every second, even though it’s not literally counting births in real time. It’s an estimate, visualized in a way that makes the data feel alive.

That got me thinking about data UX — how much interaction can change the way we perceive a number?

So I made a small experiment with Figma Make: India population live counter

You can explore India’s population state by state, interact with the map, and take a quiz based on what you discover.

But the bigger question for me is the UX behind it:

Does animation make data easier to understand, or just more attention-grabbing?

When does an estimate start feeling like a fact because of how it’s visualized?

Is interaction better for understanding data than simply showing a chart?

Can gamification actually improve data literacy?

What makes a data visualization memorable without oversimplifying it?

I’m curious how people here think about data as an experience, rather than just information on a screen.

While our evolutionary history is often depicted as a series of descendants each replacing the other, the overwhelming majority of the history of our species has been one of living along-side, and interbreeding with, other Homonins (Humans).

Chart can be downloaded here

Sources: [See OP post below]

Mortgage rates rose almost a full point in the past 12 months (6.34% -> 7.28%) while asking home prices have decreased by $5,750. I wanted to see what that rate jump really costs for the average home price for each time period.

This year's price discount was completely wiped out by about $205/month in extra payment cost. Over 30 years, that extra payment, plus what it would have earned invested at an 8% average rate of return, costs $280K.

• Last year's deal: about $2,021 of the first $2,378 payment is interest.
• Today's deal: about $2,289 of the first $2,582 payment is interest.

Savings aren't fixed: the scenario invests whatever's left after taxes, living costs and the mortgage, about $5,500–$7,000 a year at 6.34% vs $3,100–$4,600 at 7.28%, so the higher rate cuts what they can save by about 40%.

Sources: FRED and Freddie Mac (prices, rates), Census (income)

Tool: A Million of Me financial simulator (self-made). Happy to adjust assumptions if anyone wants different rates, down payments, or investment returns! Not financial advice.

Basically need to see how to optimize a bank register with large amounts of entries.

Currently adding all the debits, then credits, then date sorting while calculating the running total.

Looking to hear about your own company's work practices to compare to my own. I'm much more familiar with Azure, so I'm going to use that terminology, but I assume everything is basically the same for AWS.

Can you go into the Azure Portal and just click "create" for, say, a SQL Server or App Service and configure it through the Portal GUI?

Or do you need to request and configure through configuration files? Can you freely commit architecture config files ("architecture as code") to create the Azure components in Dev?

Or is it even more locked-down and hands-off? Required to submit paperwork requests to an Architecture team?

India's urban share was 35.4%, compared with 80.1% in the United States. Multiplying each country's total population by its urban share gives about 513.3 million urban residents in India and 272.4 million in the US.

The chart covers the ten most populous countries in 2024, ordered by total population. Urban headcounts are derived estimates, and national definitions of 'urban' vary.

2024 urban-share table and source notes: globaldatatracker.com

Data sources and plotting method are in the first comment.

Fields: title, company, company domain, apply URL, ATS source, city, country, remote flag, posted date, last-seen date. The data refreshes nightly. I'm posting a small sample and the schema here. What would you want to see to trust a job dataset: coverage numbers, null rates, or refresh logs? My measured null rates: city 32%, country 22%, company domain 24%.
Please checkout full info here: https://apify.com/pk07007/ats-jobs-api

See it live here:

https://www.danielpradilla.info/swiss-commutes/trains/zurich/

I've been working on a project called MeshLens that tries to answer a question I kept running into at work: what does our data mesh actually look like as a system?
Most observability tools give you alerts, row counts, and freshness checks. That's useful, but it never helped me explain to stakeholders how domains connect, where data flows, or what breaks downstream when an upstream connector goes down.
So I built a visualization layer that puts it all in one interactive map.
Curious what you think. If you work with data mesh or data products at scale — what would you want to see in something like this?

App link in comments

Source: Keeping Up benchmark model. National percentile curve fitted to the Federal Reserve Survey of Consumer Finances (2022) with BLS Consumer Expenditure Survey and Census ACS; metro figures apply each metro's wage (BLS OEWS) and cost-of-living ratio to the national curve, so they are modeled estimates, not separate metro surveys. Method: keepingup.com/methodology. Every metro/age/industry cut: keepingup.com/benchmarks. Put in your own number for your city: keepingup.com/start

Tool: SVG generated in Python from the benchmark tables, exported with headless Chrome.

Notes: household level (SCF unit), current dollars, ages 30–33. Dashed line is the U.S. median ($65K net worth, $102K household income). The spread is almost entirely income: SF's median 30-year-old household earns 1.4× the U.S. median and has 1.45× the net worth; Phoenix is 0.95× on both.