Rose used to be a popular name, at the beginning of the twentieth century and then it faded to a level of about 200 in a million (these are US figures by the way). So, how do I know this? Go to Baby Name Wizard and you can get a graph of the popularity of any name.

For example, my name (Robin) which is used as both a boy’s and girl’s name (with almost equal frequency in the US) was fairly popular at the time I was born, but faded with the turn of the millennium. George is also a unisex name but its popularity has declined progressively since 1880 and even two US presidents with that name have done nothing to alter the fact (hmmm, I wonder why?). George, as a girls name, died out almost completely in the 1960s—giving way to Georgette, which tanked in 1980. Adolph, as a name, tanked in the 1940s (no surprises there) but never died a complete death until 1970 or so.

The web site is fun. You can use it to check the popularity of the names of your kids or siblings or parents. You can also use it to discover meaningless trends. For example, names beginning with high scoring scrabble letters are in the main becoming increasingly popular (check out Q, X and Z). All names beginning with a vowel seem to follow a very similar curve: declining popularity since 1880 to the 1960s and then a rise up to now. Why? Who knows.

And that’s an intriguing thing about the Baby Name Wizard, it has no real purpose unless you need to know the popularity of a name before you pin it on a child, but it is fascinating to mess with. Enola, for example, was never much of a popular name, but had its day from 1880 to 1920. It lost popularity long before anyone even heard of the Enola Gay.

So who did the data visualisation here?

The answer is Martin Wattenberg. However, this particular data visualization and others (check out Smart Money) also relate to research carried within IBM labs. The interesting thing about data maps of this kind is that they relay information very quickly and effectively. In a world pervaded by infoglut (or infomania) getting meaning from data instantly is a challenge and techniques, like the two mentioned here, help to solve the problem.