Big Data and Venture Firms

Innovation and disruption are cornerstones of capitalization efforts in the venture capital industry. While venture capitalists like Mark Stevens are extremely skilled at identifying the companies and entrepreneurs that are on the forefront of coming trends, the industry itself has been undergoing its own changes in response to trends in technology and data analytics. Machine learning and artificial intelligence have made it easier for those in venture capital firms to make data-driven decisions on entrepreneurs and startups. A host of new sophisticated models have been applied to the investment processes used by venture firms.

The Evolution of Venture Firms

Data-driven processes for decision-making have re-invented the venture capital industry. In times past, the traditional venture model relied on three primary components. First, there were the carefully cultivated networks that included individual general partners, personal associations, and the different brands of venture capital firms. Secondly, it followed an apprenticeship model based on the development of investment expertise and acumen. Thirdly, experienced and seasoned investors relied on a “gut feeling” and pattern recognition in the market to make final choices on new investments. Data analytics is now being used in the different steps of the decision-making process, such as sourcing potential, screening possibilities, and monitoring the chosen investments. Analytics is also helping portfolio companies past the investment phase as well.

The Incorporation of Data

Many of the new venture capital firms are firmly relying on data analytics for their decision-making, while older companies are figuring out how to incorporate the new tech into their existing processes. While these tenured companies may not have as extreme a reliance on the use of data as up-and-coming firms, there is no denying the firm entrenchment of bid data in the industry. There is still competition in the industry, and the high rate of new firm formations coupled against the power law dynamics of venture capital efforts have companies looking for any advantage possible. Using signals that are generated by complex algorithms, firms have a more informed decision—making enhancing their investment options. Machine learning technology is continually advancing, and with data as the primary input, venture firms have a more reliable way to make choices on the potential success of a partnership.

Key performance signals are often missing for early-stage or seed firms, but data allows a firm to identify new entrepreneurs, leadership teams, and funders that have high potential. The models that are used for such decision-making gleans information from social media signals and the digital footprint, searching for product managers or highly-respected and successful engineers that may be ready to leave their jobs to start their own company. Venture capitalists can move in before the company launches, benefiting both the would-be start-up and the firm. Venture firms look at an entire company when making a decision, beyond just the team-related data. There are quantitative inputs, such as cap table structure, valuation, or deal history, that can also be analyzed through big data.

The Information Behind the Data

As companies mature, the number of tangible variables increases. These changes create new and more valuable inputs for predictive and scoring models. The company categories affect the inputs and usefulness of scoring and predictive models, though type, data volume, and availability are prime factors. Companies that are consumer-facing have more data available than their counterparts, and firms can look at information like sustainability, retention, and growth. Data is a lot harder to obtain for software or enterprise companies. Young start-ups, in particular, require more complex evaluation methods. The key performance indicators vary as a result of the business model and vertical, which makes it more difficult to establish a relative comparison. The entire purpose of the source and screen aspects of the early investment process is to narrow down selections and information in such a way that entrepreneurs are more actively sought after for perfect timing investment. However, data analytics also expands into the way venture capital firms monitor and assist their portfolio companies post-investment.

The Sustainability of Data

Rather than the traditional models of board-level and issue-driven relationships between venture capitalists and CEOs, data allows a firm to have direct access to the information revealing the health of the company. This data can be used for anticipating or projecting company-level concerns and proactively getting ahead of them. This direct access also gives capital firms the ability to share data signals across their portfolio companies in ways that hadn’t been reliable or effective before. Being able to isolate key aspects of operations in a successful company can be shared to all companies that share similar inputs or circumstances. These post-investment data revelations create opportunities for teams to more wholly understand growth, optimize resources, uncover new potential or opportunities, and narrow down damaging weaknesses or risks.

Big data has been made manageable through AI and advanced machine learning. Venture capital firms have been given a great gift with the ability to harness unprecedented information.

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