Ganesh G Kamble

What I work on

BabyNameSuggestion.com started because the naming information available online is mostly recycled. The same meanings are copied between sites, popularity claims are rarely sourced, and almost nothing links back to the agency the numbers came from. I wanted a reference where every figure could be traced.

So the site is built on primary data. Popularity, rank and birth counts come from national statistics agencies and civil-registry releases — the same published sources demographers use — normalised into one comparable dataset covering 28 countries and territories. That currently amounts to 1.9 million individual birth records behind 219,846 names.

Not every country is on the same footing, and I would rather say so than round it up. Of the 28 countries and territories covered, 26 have data from an official government statistics agency or civil registry. India and Japan do not publish a national given-name ranking at all, so their figures come from published survey and aggregated sources instead. Those are useful, and they are clearly the best available — but they are not registry data, and calling them official would be an overstatement.

Why the data work is the hard part

Merging dozens of national datasets is where naming references usually go wrong, because the failures are quiet ones. A few that I have had to find and fix in this dataset:

• Accent folding silently destroying counts. Because the database treats Ömer and Omer as the same key, one spelling was overwriting the other instead of adding to it — understating that name by hundreds of births, and German totals by roughly 37% overall.
• Population counts mislabelled as births. Finland and Iceland publish how many living people hold a name, not annual births. Presented under a "births" label that overstated one name by about 29 times. Those figures are now explicitly marked as a different kind of measurement.
• Trend labels asserted without evidence. Roughly 34,600 rows carried a rising/falling label derived from no actual time series. They now show no trend rather than a fabricated one.
• Comparing years that are not comparable. Germany's data is a moving panel of city registries — cities join and leave — so raw year-on-year counts measure reporting coverage, not naming behaviour. It is compared as a share instead, and no trend is claimed where the evidence is too thin to support one.

None of that is visible to a visitor, which is precisely why it matters. A naming statistic that is quietly wrong is worse than one that is missing.

How I handle what I do not know

Name meanings are the genuinely hard case. Etymology is often contested, and for many names there is no authoritative answer at all. Rather than presenting a guess as fact:

• Meanings cross-referenced against established etymological references are labelled as such on the page.
• Meanings that are AI-assisted and not yet independently verified carry a visible notice saying so.
• Names where no documented meaning could be found get an honest placeholder and are excluded from search indexing rather than padded with invented content.

The underlying registry data is not mine and I do not claim it. Each figure belongs to the agency that published it, and anyone republishing it should cite that agency directly.

For journalists and researchers

I am happy to answer questions on naming trends, how national birth-registry data compares between countries, and what the data can and cannot actually show. If a figure does not hold up to scrutiny I would rather say so than see it printed.

The dataset is also public: the API and open-data page documents how to query it directly, with no key or signup. Methodology is written up in how we curate names, and Name Lists collects the analysis pieces.