In today’s article, we’ll be delving into Prospect Visual’s proprietary database. As some of you may know, Prospect Visual was founded in 2003 by Sandro Gvelesiani while reading a Wall Street Journal article. His interest in searching for hidden relationships has become what we know today to be the Prospect Visual database. The idea of relationship mapping through our database is now responsible for the success of top institutions around the world.
To build an effective way to map relationships, data must first be collected to support any notion of an actionable connection. The main problem that came to mind while planning to collect data for Prospect Visual was “How can we collect enough data to find even the most discreet of connections?”
There were two options considered at the time of the creation of the database. Either record all data by hand or record all data by automation. With high hopes of a large database, Gvelesiani decided that automation of data collection would be the most effective way of producing a database worthy to be used for relationship mapping. Armed with a background in Computer Science and an extensive platform building background, he found natural language processing.
According to Wikipedia, “Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction.Many challenges in NLP involve natural language understanding — that is, enabling computers to derive meaning from human or natural language input.”
After finding NLP, Gvelesiani recruited a team of developers to engineer a system specifically made for relationship mapping. Day by day, the database grew as NLP pulled information from SEC articles. The selection of articles grew as Bloomberg Businessweek and BusinessWire articles were also included in the extraction process.
At the end of 2013 (the time in which I am writing this article), the Prospect Visual database has swelled to contain over 90 million individuals. With the addition of each Prospect Visual client, the database grows, and information is updated and enhanced through the inspection by Prospect Visual data analysts. It is important to note that the Prospect Visual team never takes data from clients but rather enhances the data by doing their own research on each contact.
As the database grows, more and more obscure relationships are revealed through Prospect Visual and its algorithms for determining connection strength. The sheer potency of relationship mapping in fundraising has never been this robust.
With Prospect Visual becoming a platform used by top-tier organizations around the world, the team at Prospect Visual has no intention of ever slowing down its aggregate database. With each day, the database is ever-increasingly closer to the goal of 165 million individuals by the end of 2014.
As 2013 draws to a close, developers at Prospect Visual are working on new methods of increasing the amount of data and type of data added to the database. With new features and data coming out for Prospect Visual, who knows how Prospect Visual can revolutionize how you fundraise.