Issue #8

State of Deep Tech | Market map: Digital healthcare infrastructure | Why you sometimes need to break the rules in data viz | Free Resources - Landscape of biomedical research, Academic Freedom Index

Pancorbo: Passing Train (1901) by Darío de Regoyos

Source: Artvee

Est. Reading Time: 4 mins

VC 🚀

State of Deep Tech

By Bessemer Venture Partners

Annotations

• Deep tech can be defined as technology that pushes the boundaries of human capability through novel research and focused commercialization.

• Deep tech is challenging due to the following reasons:

  • Difficult to establish growth and GTM (go-to-market) metrics.

  • Capital-intensive and long gestation periods, lead to higher opportunity cost of capital.

• Deep tech is capital intensive due to the following reasons:

  • Unlike software companies, deep tech deals with the physical world, and significant investment needs to be made for equipment and lab space.

  • Deep tech is highly regulated, and approvals are needed before commercializing.

  • Every stage of development (research → prototype → deployment → commercial scale) poses its own unique technical development challenges.

• Currently, as geopolitical tensions are rising, there is lesser visibility into deep tech developments due to their strategic importance to national security.

  

  Source: BVP

• The average TRL of companies on the XB100 (definitive ranking of the world’s top private deep technology companies by the XPRIZE Foundation and Bessemer Venture Partners) is close to 7.

• The value-capital-multiple (VCM) is the ratio of the latest valuation to the total amount of capital raised. It is useful in capturing how efficiently deep tech companies convert invested capital into value.

• To evaluate the technological readiness of deep tech companies, the following questions need to be addressed: 

  • Why now? 

  • What does the techno-economic analysis (TEA) look like? (TEAs are essential in evaluating the underlying unit economics of technology at its current and future TRLs).

  • What are the assumptions used? 

  • Prerequisites for this to work at scale? 

  • What has been demonstrated so far? Resources needed to go from prototype to production?

• Given the unique challenges of the industry, newer deep tech venture funds have extended investment horizons. Breakthrough Energy Ventures has an investment horizon of 20yrs vs 7-10yrs which is the norm.

• Non-dilutive funding (grants) remains the lifeblood of deep tech companies.

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Healthcare 🩺

Market map: Digital healthcare infrastructure

By Weekend Fund

Source: Weekend Fund

Annotations

• The pandemic provided a jolt to telehealth, which has since stabilized. According to the latest research from the Stanford Center of Digital Health and Rock Health, 73% of users expect to continue using telemedicine at the same or higher rate, while 60% of physicians plan to continue offering care through telemedicine.

• Virtualization of care delivery has forced providers to seek dynamic infrastructure solutions that can help with:

  • Data capture, storage, and interoperability

  • Credentialing, licensing, clinical workforce management, and clinical decision support

  • Revenue cycle management

•  Consumers are bringing their expectations of seamless experiences in retail, travel, and technology to healthcare.

• As new-age healthtech startups unbundle the hospital, there’s a need for workflow builders and automation solutions to ensure seamless patient experiences.

• There’s also an increased desire for interoperability of digital health records from consumer-facing interfaces to databases, certified EHRs, home-grown EHRs, and digital devices. 

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Data Viz 📊

Why you sometimes need to break the rules in data viz

By The Economist

Annotations

Source: The Economist

• A truncated axis (doesn’t start at 0) is usually frowned upon as it can cause small differences to appear larger, or make a rise or fall look more pronounced. But sometimes, it’s important to highlight small variations in data. When you’re truncating the axis, use a symbol to make sure the reader notices. A truncated axis should never be used with a bar/column chart as it breaks the relationship between the rectangle’s dimensions and the data.

  

  Source: The Economist

• Edward Tufte pioneered the idea of minimalism in data viz that remains influential to date. But, in recent times, there’s been a pushback against minimalism as more studies indicate that more embellished charts and those that reiterate the main trend in a visualisation, were better remembered and understood.

• It’s never a good idea to use too many colours in a data viz to depict categorical variables. Limited palettes look crisper and sharper. If your data has too many variables you can highlight the main ones and group the non-important ones into the “other” category.

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Free Resources 💡

1. The landscape of biomedical research: This interactive visualization displays 21 million scientific papers collected in the PubMed database, maintained by the United States National Library of Medicine and encompassing all biomedical and life science fields of research.

2. Academic Freedom Index: The interactive map provides a comprehensive overview of academic freedom in 179 countries and territories in 2022.