From Cron to Modern Data Stack (MDS): Dataflow Automation and Its Current State
The concept that makes the technological miracles of today possible are defined by data. Enormous amounts of data are collected...
It’s a tough time to be in venture capital. Years ago, VC organizations were the only place for start-ups to get the funding they needed to grow. That’s started to change. In 2018, regulation crowdfunding became more significant in terms of speed to close and funding. For example, Venture Beat found that 60% of start-ups are successful in raising capital through this method compared to a 6.5% success rate with traditional venture capital. It’s true that venture capital currently has an advantage in terms of total capital to invest. However, these alternative funding sources are proliferating.
These developments mean that company founders are starting to question rethink their funding options. To stay competitive, venture capital firms need to bring more to the table.
To attract better deals and enhance the value of your current portfolio, there are three new capabilities you need to develop. Without these, you will be perceived as little more than a checkbook. Add these abilities to your firm, and you will no longer be seen as a financing commodity.
For a growing company, understanding performance is difficult. Usually, small firms lack the business intelligence tools that the Fortune 500 takes for granted. Without these capabilities, your portfolio companies are more likely to underperform since they will not be able to make timely decisions based on accurate data.
For a VC firm, data visualization is a helpful resource; it helps you to understand business performance. For example, rather than spending days each month reviewing customer comments and orders, a data visualization capability can summarize the data. Specifically, we advise VC firms to use data visualization to compare performance across different companies in their portfolio beyond traditional accounting measures.
Tip: Use data visualization to compare marketing effectiveness across your portfolio companies so you can identify improvements.
That’s one of the most challenging questions in business. Should you emphasize your past strategy or explore a new direction? From a venture capital standpoint, the traditional approach of exclusively relying on intuitive judgments is not good enough. A venture capital portfolio company is already a high-risk operation since such companies are usually developing new technologies and new ways to connect with the marketplace.
Predictive analytics is a vital venture capital capability because it reduces the risk of failure. At 3M, a multi-billion dollar brand, has used predictive analytics to reduce risk and address compliance issues. As noted in Compliance Week: “3M is using the findings to help prioritize how to address—and get ahead of—conflict of interest matters, as well as where compliance needs to spend more time on education and training..”
3M’s success shows the way forward to manage customer complaints and regulatory requirements. If you have portfolio companies in health care, finance, and other highly regulated industries, look at using predictive analytics to cut your risk.
As a venture capital investor, you have to be patient with your portfolio companies. Breaking new ground in technology and launching new products is challenging! However, your patience has a limit. You ultimately need to demonstrate returns and find the right opportunity for liquidity.
That’s why you need a way to help your portfolio firms with sales enablement and productivity. Without predictable revenue, your portfolio companies will end up like WeWork and other firms that lack a firm foundation.
There are a few ways you can support sales enablement at portfolio companies while respecting the autonomy of the founders.
Now let’s look at two ways you can add these capabilities to your firm.
There are two pathways to bring data science and analytics capabilities to your firm. First, you can adopt the “build” approach – hire a whole department of specialists in data. This approach can work! However, it is slow and expensive to build such a department, especially if it is outside of your firm’s core competency.
The second choice: partner with a data science firm like Blue Orange. With this approach, you get data science expertise for your portfolio firms as needed. If you fail to act on building these capabilities, your venture capital firm will fall behind VCs who provide insights as well as funding.