Data science has become the transformative field of the decade within the last couple of years. It seems like every application it touches goes through a revolutionary transformation. From trend identification insights in marketing to behavioral reinforcement in education, using machine learning algorithms to work with data has allowed for the construction of programs and processes that improve with time, improving as they build a base of experience to draw from.
This is useful because it prevents the designers of those analytics from needing to recreate them from the ground up to account for changes in the phenomenon being analyzed. Instead, those changes are identified and incorporated into the results as a matter of the algorithm’s function.
It should be no surprise that investment professionals are rushing to find ways to improve their returns with these tools. Stock traders and arbitrage brokers have been using their rudimentary predecessors for decades, pushing the field to develop into the complex set of tools used across investment types today. Thanks to that, you can now use analytics to identify great real estate investments, make conclusions about trends early in their development, and improve the odds of selecting a top-performing startup to back with venture capital. There are a lot of ways they can do this.
Let’s take a look at a couple of the more prominent tools to emerge recently.
1. Identifying Innovation Hot Spots
One of the biggest factors in the rise of a company is the ecosystem it’s nurtured in. Local factors affect taxes paid, the company’s ability to attract top talent, and even the availability of top-tier support services, vendors, and suppliers. All of these resources go into the odds of a company’s success. While it can be difficult to predict which company will breakthrough based on just these early developmental resources. It is not difficult to analyze various local economies around the country in order to figure out where the most welcoming environments for new tech businesses are at today.
2. Modeling Company Performance
One of the reasons it’s tough to predict a company’s performance from just its available social and professional resources is that there are a lot of other factors that determine its success. Availability of working capital, team background, existing digital footprint, marketing plans, and availability of future funding all play a role. Modeling company performance on an individual level takes all those factors and more into account.
Since it’s an incredibly individualized simulation, each model requires the design of its own performance simulation to produce reliable predictions. When these algorithms are built properly they provide sophisticated analysis using deep data. That data can help an investor check their gut instincts before committing. Matt Ocko talks about this in his appearance on episode 23 of the ANGEL podcast.
3. Establishing Key Performance Indicators
The use of sophisticated analytics doesn’t stop when an investment is made. Today’s VC professionals know they need to inform their decision-making throughout the company launch process. So they have built data tools that help them with every step. Using analytics to set KPIs means being able to crunch data about the company’s past performance, and the ability to grow quickly.
This lets the VC then establish realistic but still essential milestone goals around the individual investment. As a result, KPIs are more achievable because they are tailored to the situation, not an objective expectation of success. They’re also more useful because using an adaptive algorithm means being able to make informed predictions about changes in the company’s capability as more personnel and other resources are added to its makeup.
4. Diversifying Investment
This last application is a little out of the ordinary for VC firms. Traditionally, angel investors and other VC participants have focused on one company at a time. This is especially true of those who participate in first-round funding for new companies because they are often brought in to coordinate future rounds of investment financing and to set the terms of growth through the establishment of KPIs.
Robust analytics and other automated tools have begun changing this, though, and that has led to the emergence of new VC firms that prefer to find promising investment opportunities to enter into as co-investors with another party, focusing on maintaining a diverse portfolio of active startups at once. This new movement represents a very, very small minority of the investment market in this area today. If it proves to be promising, it could lead to more diversification in the industry.
For now, the only firm Correlation Ventures is advertising the approach as a mainstay strategy, but there are others beginning to dip a toe into the co-investor market as a way to hedge against the risks involved in heavy investment in a single startup.
Trends To Follow
Big data is the next majorly revolutionary tool for tech investors and tech companies. Due in part to the rise of artificial intelligence as a tool in enterprise decision-making. Some of the top tech investors are leaning into it as they make decisions about who to fund next.
