When Venture Capital Met Big Data

Posted by: Yu Zoe Zhai March 13, 2014 in For Venture Capitalists Leave a comment

“Big data” was one of the hottest topics in 2013. According to CB Insights, from Hadoop distributors to text mining platforms, private Big Data companies raised $1.28B across 127 deals in the first half of 2013. With an average of over $210M in funding and more than 20 financing deals per month, industry pundits will have some credibility when they inevitably describe 2013 as the “year of Big Data”.

Recently, the “war” between the e-commerce giant Alibaba and social media titan Tencent in China keeps reminding us how important the data means nowadays. Now Li Jiang states in “When Venture Capital Met Big Data” that in order to understand what Big Data analytics and software will do to the venture capital industry, it’s important that we dissect exactly where products can impact the venture investing process. Given the infographic entitled “Venture Capital Stack”, he illustrated an analogous venture investment “stack” with several distinct areas where tech entrepreneurs can hope to add value (and gain paying customers).

venture capital stack

To further explain this illustration, Li Jiang offers us five explanations.

1. Identifying

1. Identifying

Startups such as Mattermark are tracking website views, funding, employee and hiring, board of directors, social media activity, and creating a single score across the various metrics. Admittedly, this is better for consumer facing startups (though you can get a free trial and decide for yourself).

2. Qualifying + Screening

2. Qualifying + Screening

The line gets blurred a bit between the process of discovering a company and screening them for further due diligence as most of the startups addressing venture capital are in both. Resources such as Mattermark or CrunchBase and others fall into this category. They contain some basic information that investors care about in the initial screening such as capital invested, valuation and size, team and board, etc.

3. Evaluating

3. Evaluating

The traditional due diligence process is here to stay. Meeting the management team and digging into the details of a company’s financial, business model, etc. is at the core of venture capital. The process, however, can be greatly enhanced by data platforms that track the performance of a company’s product. App Annie and Flurry are two such companies that can act as non-biased sources to confirm a company’s actual engagement and traction (for mobile only).

4. Investing + Processing

4. Investing + Processing

AngelList is the big “disruptor” by allowing angel investors to set up their own syndicates, like a micro-VC fund. There are other software tools that help make the paperwork of funding an investment easier, better documented and more organized.

5. Managing + Reporting

5. Managing + Reporting

Coming to the bottom of the venture capital stack and the least sexy (just like hardware) part, software tools are making it easier for investors to track portfolio companies, organize company data and metrics and report their portfolio to limited partners or other constituents.

By knowing the impact of data imposing on the venture capital industry, it may affect policy makers as well as institutional investors. Forbes has already given a list of “Top 10 Most Funded Big Data Startups” in 2013, and the most active investors are as well mentioned by various sources. (See the infographic below from CB)

bigdatamostactive

 

However, according to Li Jiang, observations and data over the long term can form guidelines but not exact rules. Economics is bad at predicting black swans in the same way that data analytics will likely be bad at predicting startup unicorns, but the jury on this next generation of startup data platforms is still out and likely will be for another few years.

To read more, please visit here.

Leave a Reply