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Insights from "Venture Intelligence Day" Part II of II
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Last week, I shared the first batch of key insights and session recordings from the āVenture Intelligence Dayā. Today is part II of II.
Thanks again to our friends at Vestberry who agreed to make the exclusive recordings available for our DDVC community via our platform āThe Labā. You can get 40% discount on your membership today and access all Venture Intelligence Day recordings here (together with tons of other resources such as the recordings of our Virtual Data-Driven VC Summit 2024)
5 Key Takeaways (Part II of II)
āEven the most well-connected investors face challenges in ensuring they see every promising dealā
āPrimary mission is to create a unique data asset, which forms the foundation for all insights across the firmā
āAI alone cannot replace the intuition and strategy integral to VC decisionsā
āThe potential lies in using AI tools to enhance efficiency in tasks like information extraction and industry researchā
āJust as in public markets, knowing which companies exist doesnāt mean all investors make the same decisionsā
Session Snapshots (Part II of II)
#5 VC Tech Stack Deep Dive at Atomico
Harry, a software engineer on Atomicoās intelligence team, discussed the data infrastructure and tech stack his team has developed to support Atomicoās data-driven approach.
Atomicoās intelligence team, comprising data analysts, software engineers, and data scientists, operates independently to serve all other teams in the firm. Their primary mission is to create a unique data asset, which they treat as a "data product" that forms the foundation for all insights across Atomico.
This data-first approach enables them to provide a single source of truth across core entitiesācompanies and peopleāand offers insights on aspects like funding rounds, team positions, headcount, and web traffic, aiming to support data-driven decision-making across the entire VC lifecycle.
Harry explained Atomico's tech stack and tools, emphasizing DBT as key for orchestrating data transformations within their data warehouse. This stack supports Atomicoās various data applications, including Looker, which enables teams to visualize data and create dashboards for self-service access.
For sourcing and analysis, Atomico also leverages machine learning models for predictive insights, such as predicting a companyās likelihood of raising funds soon or gauging team strength. Additionally, theyāre integrating LLM capabilities, using tools like Glean for indexing unstructured data, which feeds into tools like āDora,ā an automated briefing generator that consolidates all company data for internal use.
By focusing on both structured and unstructured data, Atomico aims to make their data ecosystem accessible and actionable for all teams, driving consistent engagement and up-to-date records.
When discussing Atomicoās approach to building vs. buying tools, Harry noted their preference for investing in custom-built tools that allow greater control and flexibility, especially as off-the-shelf options evolve to meet VC needs. For example, they use a CRM and Glean to manage emails and document indexing, respectively, but they avoid sourcing tools that result in founders being inundated with redundant messages.
Harry emphasized that while many tools are advancing in capabilities, Atomicoās priority remains direct access to raw data, allowing them to leverage machine learning models that maintain Atomicoās competitive edge and ensure a tailored approach to data-driven venture investing.