Data-driven VC and PE: why investors are embracing it and how you can do the same

Written by
Christiaan Wiers
Table of Contents

Using insights and formulas derived from data is becoming increasingly essential when it comes to making complex decisions for a wide range of business areas. Examples of such business areas include HR, logistics, marketing and many more. Interestingly enough, it's only since recently that the notion of data-driven venture capital and private equity have been picking up interest. 

Think for yourself, does your team have a data analyst or software engineer already?
Probably, the answer is no. 

In today’s world, there is no shortage of VC and PE firms. This means that you have to work even harder and smarter to find great investment opportunities. One way to do this is through the power of data. 

In this blog we will explain why making data-driven decisions can really give you an edge over other firms, why the best time to start was yesterday and what considerations to make when deciding to catch the wave of data-driven venture capital and private equity. 

Key insights

  • The intensifying competition within the VC/PE sector is making it increasingly difficult to find and secure investment opportunities before they become widely known. 
  • Only 53% of PE firms respondents say they use alternative data to inform their investment decision-making process, according to S&P Global.
  • It is estimated that over 75% of VC and early-stage investor executive reviews will be informed using AI and data analytics by 2025.
  • When choosing for data-driven decision-making, carefully consider if you want to develop such a tool in-house, use a commercial product or have a custom tool developed by a specialised company. 

What is data-driven venture capital or private equity?

With data-driven in the context of venture capital and private equity, we mean the way in which large amounts of data are used to make or guide investment-related decisions. Examples include:

Why is data-driven decision-making of increasing importance for PE and VC investments?

In the period of 2001-2022 the amount of dry powder in private equity has increased by a factor of roughly 5x, for venture capital the amount of dry powder has increased by a factor of around 4x in that same period. Unsurprisingly, the number of venture capital investors has increased by a factor 6x over the period 2007-2022 and the amount of active private equity firms has increased with a factor 5x in the period 2001-2018. 


The steep increase in Venture Capital and Private Equity activity leaves its impact on the industry:

  1. Increased competition between firms: if we extrapolate the data about the amount of private equity and venture capital firms to a period of 2001-2022, an increase of dry powder and an even sharper increase in the amount of private equity firms can be seen, hence underlining that competition between firms for finding attractive investment opportunities has increased significantly. This makes it harder to scout valuable investment opportunities and do so before everyone else.

  2. Crowded investment environment: with an increase in the amount of businesses, deals, dry powder and the amount of PE and VC firms over time we can conclude that the world of investments is more crowded (noisy) than every before. This makes it increasingly difficult to separate the truly great investment opportunities from the ‘noise’.

Given the trends, you may ask yourself: I am too late to pickup on all of this? We believe the answer is no, there is still in time to catch the wave. But before doing so, we will give you some examples of current possibilities that are enabled by a data-drive approach. Please, use these possibilities to inspire yourself on how a data-driven approach within your firm can be used to carve out a competitive advantage.

The current state of data-driven VC and PE

Knowing the importance of data for making investment decisions in today’s world, what is the current state of data-driven VC and PE? 

To spark your interest, here a just a couple of today’s possibilities:

  • Automatic screening of (startup) applications for funding
  • Tracking millions of companies and automatically getting notified when interesting investment opportunities arise
  • Benchmarking companies against their competitors
  • Predicting ‘future founders’ and/or notifying the investment firm when e.g. a Tesla engineer changes his current job the ‘startup founder’ on LinkedIn
  • Predicting successful exits (via M&A or IPO)

When looking at investment firms, we found the following insights by S&P Global about the use of data by PE firms.

  • Identifying emerging market trends more quickly: this has been mentioned as the number one reason for investing in data analytics by 50% of respondents.
  • This is followed by making better and faster decisions as mentioned the top reason by 33% of respondents.
  • Complexity of data: 44% of respondents noted that the single most taxing element of modern data management is the ever-increasing complexity of data with which they have to wrestle.
  • Data-driving investments: only 53% of respondents say they use alternative data to inform their investment decision-making process. 
  • Using AI: surprisingly, 57% of respondents report that they currently take advantage of some form of AI in their investment processes. The other 43% percent all mentions that they consider applying AI.

It is estimated that more than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics by 2025.

In short: being able to spot market trends faster, making better and faster decisions, but also the increasing complexity of data have been mentioned by PE firms as the top reasons to start taking data to their advantage. Yet, as of today, a very significant percentage of the firms are not taking advantage of the current possibilities. 

The importance of high quality data-sources

Before jumping into the potential as well as the considerations to be made when using data for investment decisions, first some information about the various data sources and the corresponding challenges. 

Popular data sources used by VC/PE firms include among others: Pitchbook, Factset, Crunchbase, Orbis and Preqin.

Here are some of the most important aspects to keep in mind when using popular data sources for data-driven decision-making:

  1. Missing companies: the company you are looking for may not be in the data source, this is especially the case for smaller (less well known) companies. 
  2. Incomplete information: specific information about a company may not exist in the data source (incomplete data). Furthermore, the amount of information available can differ per company. For example, don’t expect to find the same amount of information on a small company’s profile as that of e.g. Apple.
  3. Outdated information: the data available about a company can be outdated for various reasons. For example, a profile may not contain up-to-date information, this may be troublesome depending on the application.
  4. Download limitations: most data sources restrict the amount of companies you can download into e.g. an Excel file. The restricted amount of data you can download make proper data analysis very difficult. For example, in the case of Pitchbook you can download a max. of 1000 companies per day with a limit of 2000 downloads per month. 
  5. Sources unknown: another challenge that data sources may give is the fact that often times it is not mentioned where the company’s data was obtained from. This means you have to simply assume and hope that the data is correct or manually check the correctness of the data.

The trick is to providing quality insights is a rigid process, both in finding companies and analyzing the data. At Venture IQ we help our VC and PE clients with data-driven insights. Wheter that is providing comprehensive market landscapes or tracking targets. To do this we  developed Catalist, a dedicated software platform, which helps in finding and understanding companies across the globe based on public data.

Considerations and the potential of data-driven VC and PE in the near future

If data-driven decision-making is something you consider, first think about if you want to develop this in-house, use existing commercial software or hire a company that can develop a custom data-driven tool.

In case you want to set up a data-driven decision-making tool, you will need two things: a lot of high quality data and technical know-how. Then, when it comes to a data-driven decision-making tool, there are different types of data-driven decision-making to distinguish. 

Types of data-driven decision-making

Below, you can find the advantages and disadvantages of the various types. Think for yourself, which option is the best for your firm.

Mathematics: as the name suggests, this type of data-driven decision-making would only involve explainable formulas for making certain decisions. 

  • This has the advantage that you can really understand how a decision is made, hence you can to verify the decision suggested and also explain to others how the decision came about. 
  • The disadvantage is that this type of decision-making only allows for limited complexity. Certain decisions are made by a combination of an enormous amount of variables, which means the deriving a mathematical formula can simply be an impossible task. 

Explainable AI: in this case, AI is used to derive an understandable decision-making model from the data you provide it. By doing so, the model will derive a formula up-on-which to make the decisions. 

  • With explainable AI, you have the advantage that the model will figure out the complex formula for proper decision-making on its own. The main advantage here is that these models can actually provide inside in how the decisions came about, e.g. what metrics does the model look at and how does each metric weight in the decision-making process. 
  • The disadvantage here is that setting up a good AI model requires a lot of data and technical knowledge. Furthermore, explainable AI may (or may not) result in poorer performance compared to black box AI.

Black box AI: with this kind of AI (e.g. Neural Networks) a very complex decision-making model will be derived from the provided data.   

  • This has the advantage that decisions can be made upon data from which you wouldn’t expect decision-making to be possible. Also, the performance of this type of AI may be incredibly good (a superb example of this would be ChatGPT).
  • On the other hand, there are some disadvantages. The first one being the difficulty of creating a well functioning black box AI model which requires a lot of data, testing and technical expertise. Next to that, with this type of models, it is unclear how the model makes a certain decision. This means that you simply have to trust the model without understanding the rationale behind it. 

What VC and PE firms are already embracing a data-driven approach?

With examples provided on what options can be chosen for a data-driven decision-making model, it is interesting to see some examples on how well known VC/PE firms are already using data-driven decision-making to their advantage. Here we will highlight two popular examples, but checkout this blog post to learn about even more companies that embrace a data-driven approach. 

  • SignalFire: they have their own data platform called ‘Beacon’ which tracks the performance of over a million companies world-wide. They get notified automatically whenever one of those companies is performing in a way that makes the firm interested. Additionally, the platform can be used to benchmark a startup against competitors. Lastly, Beacon also tracks talent world-wide in order to potentially recruit those talents for their portfolio companies. One final remark, the company invests around $10 million dollars per year in their ‘Beacon’ software.

  • EQT: their venture team and private equity advisors use a tool called Motherbrain. This is an AI-based tool which tracks companies world-wide and helps the firm to guide decisions on where to invest. The company has already confirmed that this tool has allowed them to discover several investment opportunities that would otherwise only be picked up at a much later stage, with the associated consequences. Click here to learn more about Motherbrain.

With the examples above, it becomes clear that data-driven investing is already happening on a very professional level. The examples illustrate how these firms are trying to gain a competitive advantage over other firms by leveraging the power of data-driven decision-making. Whether you are a VC, PE, or M&A team, sourcing high quality deals and finding them before others do is essential to your firm's success. Old methods of relying on inbound deal flow and manually searching the web to find under-the-radar companies will put your team at a huge disadvantage. Don’t even start us on keeping track of deals in Excel (we know you are reading!). 

What’s your next step in becoming a data-driven VC or PE?

The amount of data is growing and so is the competition for great investments. Data-driven decision-making seems to be the solution, yet a large percentage of investment firms is not yet taking advantage of the enormous potential which data-driven decision-making can bring to the table. This means there is still room to leverage the power of data in order to gain a competitive advantage.
When going for data-driven decision-making, keep thinking about the quality of your data and which sources you will use. Also pick the type of data-driven decision-making that suits your needs and carefully consider if you want to develop something in-house, buy a commercial solution or hire a specialised company to develop a custom solution for your needs.

Interested to learn more about how Venture IQ can help your team become more data-driven? Feel free to reach out.

If you want to learn more about different data sources, make sure to read our blog about ‘company databases’. For more tips on deal sourcing, check out our deal sourcing guide.

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Article written by
Christiaan Wiers