How to Adopt a Data-Driven Approach in a Venture Capital Fund - Head of Data’s Strategy

Surely no one can deny the importance of data science & analytics in the private equity and venture capital market. However many funds still struggle with reaching their desired results because they rely on fragmented and manual processes. 

This article will talk about Yiliang Zhao's strategy to adopt a technology-driven data and research strategy in Openspace Ventures.

Not only that, but Yiliang Zhao also reveals, in his opinion, the most important forms of digitalization to secure a data-driven approach, the risks of implementing this approach and how to mitigate them, and what kind of data is needed to assess a company's invest-ability. 

Openspace Ventures' Stages of Data Science and Analytics 

It's no news that if you want to be successful in a competitive market, you must be on the cutting edge of technology. As more information and technology become available, you should be the first to take advantage of that by storing it efficiently. He further explains how you can then gather insights and quickly make correct investment decisions based on data. 

Openspace Ventures currently adopt that approach.  "Such systems can also mine insights from the data and detect companies that meet certain criteria or have certain characteristics that give them a significant chance of success. In the future, we are looking to add more early signals to enrich the deal sourcing support," Yilliang states. 

How to Adopt A Data-Driven Approach?

To successfully adopt a data-driven approach, leaders and experts in the PE/VC world should implement advanced technology such as automation and artificial intelligence. 

"Automation gives finance teams freedom to focus on insight generation while AI helps generate underlying patterns in data," Yiliang explains. 

He also says that using machine learning can make your data-driven approach even more effective. This is because machine learning can help you predict scenarios and improve different outcomes. 

"These insights, together with those generated by the explainable machine learning model will greatly support the investment committee’s decision-making process," Yilliang commented. 

The Most Dangerous Risks And How to Mitigate Them.

Yiliang explains that the most high-risk threat is emerging technology, such as cybersecurity incidents. To mitigate this risk, we should develop a technology risk management process with guidelines on how to reduce vulnerabilities.

He explains that there are two steps to developing a technology risk management process, 

1) Technology risk analysis, which identifies and prioritizes risks

2) Risk management plan development, which addresses each risk identified using the respective software tools for risk categorization and prioritization.

"At Openspace Ventures, we use Horangi, which provides an integrated cybersecurity platform to monitor the data and system securities of our software systems," he adds. 

How to Determine Whether A Company Will Be A Good Investment

Yiliang stated that in order to determine whether or not a company would be a good investment, you need to look at its historical data. 

Yiliang breaks this historical data down into three:

1) investment outcomes, e.g. exits/M&A, 

2) companies’ characteristics, and 

3) external factors that affect the outcomes (such as the sector’s readiness and recent advancement) 

After collecting that data, you can come up with an invest-ability score for a prospective company based on its idiosyncratic characteristics and current external/environmental factors, as Yiliang explained. 

"Gathering sufficient data entails a long and patient process, which sometimes may include manual efforts. We have some way to go in this regard," he adds. 


In conclusion, Yiliang Zhao's strategy to adopt a data-driven approach is one that can be beneficial for any business. By understanding your data and using it to make decisions, you can improve your chances of success. If you want even more information on having a data-driven approach, then we urge you to register for our free webinar on Thursday, the 22nd of September at 10:00 AM [CEST] and stay on top of the latest data science trends by clicking here 


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