Data-driven Transformation
By White Star Capital’s Early Growth Team
As outlined in our recent post, our Early Growth Fund team has been working on four reports that detail the macro themes that we believe will drive tech innovation in the coming years:
- the impact of widespread demographic shifts;
- the digitisation of all industries and enterprises;
- the creation of new digital infrastructures and management tools to help handle, analyse and secure the zettabytes of data being produced;
- and finally, the demand for climate technology, new circular business models, and green innovation.
Our themes of focus have evolved since our first fund in 2014 in response to rapid technological adoption and the changing world around us, but the criteria we apply to each investment have remained consistent across our three Early Growth Funds to date.
Having published our first report on the Sustainable Global Economy, and our second on the Fourth Industrial Revolution, we’re excited to share our next piece of research on the rise of new solutions that address the unprecedented rise in data generation (fuelled by AI), which we’ve labelled Data-driven Transformation.
This report details the significance and potential influence of data and AI, explores the core aspects of the data value chain, including both its upstream and downstream processes, and outlines key investment opportunities from a VC standpoint.
Key findings
- There is an estimated $22.1tn in the use-case-driven value set to be created by generative AI technologies
- The data infrastructure and management market is predicted to grow to at least $1.2tn by 2032
- Cybercrime will cost the global economy $8tn in 2023
- 95% of businesses struggle to extract value from their unstructured data stores
- There were 1,000+ exits in the data-driven transformation space in 2022 (Pitchbook)
What do we mean by Data-driven Transformation?
An unprecedented surge in data generation, enhanced computational capabilities, increased cybercrime, and advancements in data science and generative AI-related technologies are all factors that have increased scrutiny of previously renowned tools and infrastructure frameworks in the modern data stack.
Coupled with the vast increase in generation each year, data also now exhibits remarkable variety and complexity. We are in an era where the physical and digital worlds are increasingly intertwined. Everything from cameras and traffic sensors to heart rate monitors is producing data, offering deeper insights into the behaviours of humans and objects. Consequently, data has emerged as a new class of corporate asset.
The time is right for a new data-driven transformation where companies and tools are designed to help organisations extract the most value out of their data while providing high cost-effectiveness, return on investment, and low implementation time.
For companies to truly capitalise on this asset, fully embracing digital transformation is essential. By digitising both their internal and external operations, companies can gather critical data. The current era offers an unprecedented opportunity to leverage this data.
In summary, our core areas of focus within the Data-driven Transformation space for Fund IV include:
- Post-Modern Data Stack: DataOps, data streaming, data mesh enablers, data reliability
- Data Management: active metadata management, vector databases, data marketplaces, data curation tools, synthetic data generation, data catalogs
- Data Security: identity & access management, cloud & infrastructure security, threat & vulnerability management, governance, risk & compliance
- AI & Analytic Tools: predictive analytics, visualisation dashboarding, AIOps, AI/ML-enhances DevTools, Generative AI UI/UX
- Productivity & Developer Tools: software delivery lifecycle, developer SaaS, developer infrastructure, build & development tools
Why now?
Several factors are creating a favourable environment for technology and data-centric businesses. First, there’s a rapid maturation of technologies and the increased availability of skilled data and engineering talent who act as the keystone between raw data and the end consumer product and/or insight.
Second, historical data from past recessions and economic disruptions indicate that companies that continuously invested in innovation during these times achieved 240% higher shareholder returns than their counterparts.
Third, the potential economic impact of generative AI as an emerging technology is substantial, with the possibility of adding $2.6tn to $4.4tn annually across 63 use cases and a total potential impact of $13.6tn to $22.1tn.
Finally, generative AI and LLM applications are currently rising at a tremendous pace. They tend to employ usage-based pricing rather than a SaaS model, which allows corporations to trial many applications without having to commit to large AOV contracts. Applications therefore have low implementation barriers but lack stickiness, which results in a high rate of churn when new applications come to market.
For us, the constant factor that stands out is the data layer on which everything runs. Over the next few years, we expect it to undergo significant re-engineering by modern startups and large corporates. This is due to the critical role of data management, quality, integration, and security in the successful implementation and effectiveness of these technologies.
If you have any questions or are an entrepreneur creating something in a sector covered by our research, please reach out to our team. They’ll be happy to hear from you!
