But there are two sides to the coin, and I would bet you are primarily focused on the distilled and information-rich professional side, or “explicit” data side of things. The other side is an imprint of our passions, thoughts, and hobbies made by our social media pages, forum posts, and blogs: the “implicit” data. Recently featured at SXSW V2V, Peter Kazanjy founded TalentBin, a search engine for recruiters and companies to fill tech-related positions. It takes into account both explicit and implicit data for potential candidates. After all, how are recruiters and companies supposed to find the right person for the job if they are only examining half of the picture? TalentBin allows a way to narrow search criteria based on activity versus solely focusing on professional experience. Through Kazanjy’s search engine, recruiters and companies can actually query a search based off of a genre of person, like software engineer or iOS developer. That way, a tech startup involved in health and fitness can use TalentBin to find an iOS developer who is also a total health nut. TalentBin will search explicit, professional data for the term “iOS developer” while also searching for implicit data about candidates that revolves around health and fitness interest. And TalentBin is not just pulling data about candidates at random. In fact, it scours a host of sites like Twitter, Meetup, Facebook, and even the US Patent Database to find relevant implicit data. There will always be a candidate who does not use LinkedIn, but the chances that they use other sites to express their interests to the Internet are very high. The implication, then, is that as an employer you can find a well-rounded and experienced candidate that is a near perfect fit for your company culture before the interview even takes place. The age of the activity-based search is upon us, and your online identity is the key to it all: make sure you present yourself well.
