I remember the first time I heard the now ubiquitous term ‘big data’ – it was 2009 and Bryce Roberts (@bryce) of OATV was explaining to me why OATV had invested in what is still today one of my favorite companies: TripIt.
In short the investment thesis was actually around data exhaust–massive data sets that were being generated (or could be generated) but were being stored in ridiculous formats or not even being consumed.
In the case of TripIt, the exhaust came in the form of email confirmations from travel companies. We’ve all done it–you book a flight and a hotel for your upcoming trip, the airline and the hotel each send you an email confirmation, which you print off, and you’re on your way. But think of all the interesting data that the process generates and all of the interesting things that data could tell us!
Why should you still be reading this? Here’s my point: Think of all of the interesting data exhaust you and your organization generate.
Whether it’s in the form of hard copy term sheets, financial statements, capital account statements, and partnership agreements, or it’s buried in excel workbooks and other electronic formats, most of it is not telling you anything because it’s not organized in a format that’s usable!
What does big data mean?
Big data is about putting the data together to tell a story and predict patterns, and though we’re talking about a much smaller scale than ‘big data,’ there are plenty of stories for you to tell and plenty of trends to identify.
What good is having all the liquidation preferences for each of the deals you’ve looked at over the last 15 years if you’re not using it correctly?
Big data as a differentiator
What if that data was instead stored in data sets that you could use to cross-reference those deal terms with the valuations of each of those companies and then looked only at Silicon Valley-based companies that were pre-revenue?
That’s a pretty interesting data set that would, on one hand, make your LPs think you’re really smart when you share it with them at your annual meeting and on another serve as interesting identifiers of certain trends that help you make better investment decisions.
In my prior life in private equity, I was fascinated with data. That was taken to another level when the firm I worked for implemented AIM private capital software because the data we historically produced as the exhaust was transformed into workable, structured data sets that told us things we never knew about our portfolio and the investment opportunities we evaluated.
Migrate Data with Altvia
If you’re interested in learning more about AIM private capital software, request a demo and see how our tools can help you manage your data.