Greg Neufeld from ValueStream Ventures discusses their investments in data-rich companies and 'data flywheel' companies. They talk about leveraging data for startup success in fintech, Ocrolus' evolution to AI integration, software vs. data as a service companies, adapting investment strategies post Chat GPT, and the current climate around AI and startup liquidity in VC.
Investing in data-rich companies is a focus for ValueStream Ventures, targeting early-stage companies with proprietary data flywheels as a defensible and offensive asset.
ValueStream Ventures transitioned from a capital market data-focused accelerator to prioritize seed-stage companies with unique data, reflecting an investment thesis centered around alternative data's defensibility and scalability.
Alternative data's significance in investment decisions is highlighted, showcasing how companies transforming unique data into valuable insights excel in investment management and systematic trading.
Deep dives
Investing in Data-Rich Companies
Investing in data-rich companies is a focus for Greg Neufeldt of Value Stream Ventures. Their venture capital fund targets early-stage companies with proprietary data flywheels. These companies leverage their unique data as both a defensive moat and an offensive asset. An example is using proprietary data in hedge funds, showcasing the increasing importance of data as a defensible asset.
ValueStream Ventures' Origin and Focus
ValueStream Ventures began as a capital market data-focused accelerator before transitioning to its current focus. They prioritize seed-stage companies with proprietary data flywheels. The emphasis on data is integral, reflecting a strategic investment thesis centered around alternative data and the defensibility and scalability it offers.
Leveraging Alternative Data for Investment
The podcast delves into the concept of alternative data, emphasizing its increasing significance in investment decisions. Companies that transform unique data into valuable insights for investment management firms often excel. The discussion highlights the transformative potential of unique data sets in improving investment research and systematic trading.
Software as a Service Companies versus Data as a Service Companies
The distinction between software as a service (SaaS) and data as a service (DaaS) companies is explored. While SaaS companies traditionally target larger user bases for revenue generation, DaaS companies thrive on automation and can offer high margins. The evolving landscape showcases a growing interest in DaaS businesses due to their potential for automation and high margins.
Venturing into Venture Capital from Alternative Data and Hedge Funds
The conversation extends to the transition from hedge funds and alternative data into venture capital (VC) roles. VC positions require skills in sourcing, understanding data assets, and building networks. Individuals intrigued by VC should be prepared for a shift towards software companies with proprietary data and an emphasis on venture investment strategies.
In this episode I speak to Greg Neufeld of ValueStream Ventures, a venture capital fund that invests in data-rich companies.
In our conversation, we discuss ValueStream’s relationship with alternative data companies, why Greg is interested in ‘data flywheel’ companies, and how a professional might make the jump into VC.
DISCLAIMER
This podcast is an edited recording of an interview with Greg Neufeld recorded in May 2024. The views and opinions expressed in this interview are those of Greg Neufeld and Mark Fleming-Williams and do not necessarily reflect the official policy or position of either CFM or any of its affiliates. The information provided herein is general information only and does not constitute investment or other advice. Any statements regarding market events, future events or other similar statements constitute only subjective views, are based upon expectations or beliefs, involve inherent risks and uncertainties and should therefore not be relied on. Future evidence and actual results could differ materially from those set forth, contemplated by or underlying these statements. In light of these risks and uncertainties, there can be no assurance that these statements are or will prove to be accurate or complete in any way.