For some time I have stayed away from venture capital data.  It is very popular territory for academics to research in as it is data rich (or at least data are readily purchasable, if very expensive); often includes actual company names that can be used in research through matching, surveys, or other web scraping; and VCs are just sexy (at least within the range of topics studied within entrepreneurship).  Personally, I have chosen not to focus on VC data since it seemed just about everyone else was.  My comparative advantage was to study the more boring topics like financing patterns in non-VC companies, angel capital, and just about any other topic.  But I've avoided educating myself for too long, so please help me. 

But VC data, although arguably the most developed in the entrepreneurship space, is so messy and debatable.  Take for example this posting by Brad Feld which points out many errors he is aware of in the published PWC Moneytree data.  Now, Brad wasn't really concerned here with the accuracy of the underlying data but was pointing out that the data is really difficult to use in looking at start-up funding.  Most VCs actually fund companies which the academic community would consider beyond start-up phase.

So, I don't have a lot more to say right now on this, but I wanted to throw this out there in the hopes of getting some comments on the different VC databases, as well as their perceived strengths and weaknesses.  I know the basics on the data here but I am really hoping that readers will provide some education.  Many thanks in advance. 

	Martin Holi - 1/24/2010 4:49:53 PM
	Any vc data set has certain "characteristics" as a result of the chosen data collection process and methodology. Obviously, commercial data provider are not keen on communicating these limitations and academics are often not aware of a potential bias in primary data. However, I had to answer this questions many times and the immediate reply was always: "What is the question you want to answer? – This will define the most suitable data set.” As you said - Brad did it exactly the other way round. He showed what questions you can't answer by using Moneytree data (at least not without further modification). That's not tricky - the art in vc data analysis is to show what you can proof, especially with limitations in a data set.

	PEDataCenter.com - 3/3/2010 1:44:25 PM
	Ever looked over the data at www.pedatacenter.com? Instead of coming from surveys, the data is gathered from actual regulatory filings filed by the companies. This is the only database that breaks down each deal into 15 specific deal terms per deal and offers a post-money valuation for many of the rounds of financing. It is utilized by various players in the VC and PE world, ranging from academia, entrepreneures, lawyers, VC/PE firms, portfolio companies, valuation firms, accounting firms, and anyone else who is in the space of raising capital, managing capital, and/or advising.