Flight Data Recorder Part 1: A look at Data Science at High Alpha
This is the first article in a multi-part series highlighting the need for data science in early stage startups, common pitfalls, and successful implementations at High Alpha over the past few months.
Earlier this year I joined the High Alpha team after spending nine years trading futures, equities, and currencies on exchanges across the globe. Coming to High Alpha was a big change for me in a lot of ways. I was coming from experiences working with companies that had strong infrastructure and large data repositories to now working with startups whose ideas, and data needs, are changing every few months.
Initially one thing I noticed was that my role is still exceptionally rare in the startup market. Most startups do not have a data scientist working with them in the pre Series A part of their lifecycle. I view this as a missed opportunity as data science is a critical component for startups and there are numerous advantages to adopting it early on.
At High Alpha I work directly with the portfolio companies in order to help them build discipline around their data and modeling processes which is critical for long-term success. By properly capturing and storing information we are able to identify the most meaningful, and predictive, aspects of our data. The rigor around data collection feeds our predictive modeling process which then drives product insights and decisions. This is a virtuous circle which all centers around data management.
Early investment in recording data in a highly organized, well maintained environment pays dividends. Not only do you save engineers, analysts, and scientists time down the road but you also create an organization focused on finding insights which may not have been obvious. Those insights lead to innovations which propel the organization forward.
While I believe data is the gateway to innovation, I can admit that not every startup needs to invest in data science from day one, however, I know that investing in your data is critical for any stage company. Think of data as the foundation of your home. It’s hard to build a sustainable home on a leaky foundation.
With that in mind, take time to really think about all aspects of your organization. Which steps can you track? At what interval should you record them? Where will that data live? How can it be easily combined, formatted, and presented? What are import aspects to a predictive model? Capturing data takes time so the earlier you start to think in this way the faster you can reap rewards.
High Alpha is a venture studio pioneering a new model for entrepreneurship that unites company building and venture capital. To learn more, visit highalpha.com or subscribe to our newsletter.