Sharp venture capitalists make remarkable inroads with alternative data

Posted byBy Ryan Kh

The University of Hawaii reports that big data is shaking up the venture capital industry in unbelievable ways. Venture capitalists are finding new ways to leverage alternative data effectively for much higher yields.

Big data plays a role in shifting the risk-reward calculus in the favor of venture capitalists. Venture capital is a high risk, high reward game. To put it into perspective, 90% of new startups fail, which means that investors can lose a lot of money while hunting the potential “unicorns.” Historically, venture capital has been regarded more as an art form than a science.

Investors were known for following their intuitions, impressions, and carefully cultivated personal networks rather than relying on cold algorithms. This has changed in the era of big data, which is why investing apps that use data analytics have really taken off. Data capital management could be a huge thing in the future.

What Are the Benefits of Alternative Data in VC?

For investors, the main questions to answer is what are the most promising companies and are they actually worth the investment. And today more than ever, venture capital firms are harnessing the power of alternative data to find those answers.

Modern investors use machine learning and AI models to gather and produce signal information that generate insights on worthy startups. The exciting thing is that the sharpest minds in the game have realized the one secret behind gaining an even stronger informational advantage. That’s alternative data.

Data Capital Management

Tapping the increasing amounts of publicly available information can offer deeper insights to support investment decisions. As Greenwich Associates study reports, 72% of investment firms see clear evidence that the alternative data enhances their signal. Good examples of these data points are market trends, significant shifts in the industry, and talent information, obtained from alternative sources.

To fully capitalize on big data, investors are increasingly relying on automated methods of information collection and screening. For venture capitalists worldwide, such as 645 Ventures, Ardian, Connetic Ventures, and Georgian Partners, the new status quo means depending on the intelligence provided by machine learning and artificial intelligence for their investment decisions.

Challenges behind signal data acquisition and forecasting with alternative data

The perfect data for signal generation are the insights on founders and their previous business experience, key employees and their movement within the company, the growth of separate departments, hiring trends, customer satisfaction, and social media discussions, among other data points. Sadly, these are unobtainable even from the most comprehensive monthly reports. On the other hand, the public web is a goldmine for this sort of alternative information.

The only thing stopping from digging it deeper are the challenges following the acquisition of reliable public data at scale. In fact, a large volume of high quality, trustworthy, and organized public web data is notoriously difficult to extract. This process requires not only strong technical knowledge and experience in the field of web scraping, but also an extensive and robust information extraction infrastructure.

Another problem is that the data needs to be consistent, complete and continuously updated to maintain its relevance for insight extraction. All in all, few companies in the world can achieve consistent success in dealing with the aforementioned struggles and it’s nearly impossible to do as an in-house operation.

Light at the end of the tunnel

As a response to these challenges, venture capitalists resort to readily available alternative data offered by modern data brokers. These accessible datasets allow investors to identify business profiles that signal risky patterns or future success trends.

Most well-known outlets supporting data-driven decisions are sites such as LinkedIn, Crunchbase or Owler. In fact, companies like Coresignal extract, aggregate and update raw data from numerous publicly available sources, making it immediately available for analytics.

“Considering the risks behind the venture capital model, obtaining more public alternative data nearly always equals better informed decisions. And taking into account the complexities of extracting heavy information loads, Coresignal effectively helps investors skip this cumbersome step. Ready to use, fresh datasets are available to streamline the pre-investment analysis”.  – Jeremy Ward, CMO at Coresignal.

Alternative Data Can Be a Booming Field for VCs in the Future

As the demand for big data and AI is increasing in the venture capital scene, the market is responding with progressively better tech solutions. A number of startups are developing software, AI and machine learning systems aimed at automating the identification of the most promising investment opportunities. Integrated new technologies will continue to evolve scoring systems and identify new investment-worthy sectors.

Sure, venture capital is unlikely to lose the human element in the selection process. However, the trend of using data-driven solutions is forecasted to continue in the future to fuel generation of reliable signals which support smarter investment decisions.

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