Introduction

Just as oil once propelled the engines of the global economy, powering everything from vehicles to industries, data today fuels the intricate mechanisms of the digital world, driving advancements and dictating the pace of technological progress. This shift marks a profound transformation in how we perceive value and utility in the modern economy.

Both oil and data are essential resources in their respective economies. Information can be extracted from data, similar to energy from oil. Data powers new technologies like driverless cars, just as oil powered traditional transportation. However, unlike oil, data is not finite, and it has lower transfer and storage costs. Data’s lifecycle is also defined by relationships, while oil’s is defined by processes.

In this article, we delve into a journey of understanding these parallels, which sheds light on the investment opportunities of sustainable data asset companies and their influence on our future.

The value of data asset companies in an AI-driven world

The world’s most valuable companies, including tech giants like Microsoft which we own in our portfolio, have set a precedent in utilising data to cement their dominance across various sectors. These data asset companies possess a unique blend of characteristics that not only allow them to thrive in today’s economy but also position them to be even more valuable in an AI-driven future. One of the key attributes of these companies is their possession of extensive, proprietary data sets.

Whilst some data is commoditised in nature¹, in the context of an increasingly AI-centric world, the demand for high-quality, proprietary data has surged. AI and machine learning technologies require vast amounts of reliable data to train algorithms and improve their accuracy and efficiency. Public data, often plagued with issues of reliability and legal risks, such as copyright infringement, falls short of meeting these demands. As a result, companies with large and rich data troves find themselves at a strategic advantage, with the ability to fuel AI innovations. This can lead to breakthroughs in critical fields like medical diagnostics, where it can enhance accuracy, reduce errors, and democratise access to advanced medical support.

Furthermore, ownership of proprietary business-to-business (B2B) data grants these companies significant pricing power, enabling them to unlock new revenue streams by monetising latent data through cross-selling, new product development, and subscription software services. The “build it once, sell it many times” database business model not only drives operating leverage but also ensures consistent and rising earnings per share and free cash flow generation for these companies.

Recent high-profile deals and legal challenges highlight the growing recognition of the value of proprietary data for training AI models. Google’s US$60 million per year agreement with Reddit² and OpenAI’s partnership with Axel Springer³ showcase the burgeoning demand for unique data sets to feed the insatiable appetite of AI algorithms. Conversely, legal disputes, such as The New York Times’ lawsuit against OpenAI and Microsoft, underscore the complexities and challenges of navigating the data landscape in an AI-driven era. These developments underline the critical role of exclusive data assets in powering the next generation of AI technologies and reinforce why companies rich in these assets are poised to become even more valuable as we advance further into the AI frontier.

Defining the data asset ecosystem

Data stands as the backbone of innovation. From tech giants harnessing big data to optimise their services, to those leveraging analytics for market insights, the ecosystem of data-related companies is as diverse as it is dynamic. Figure 2 shows one way in which we have endeavoured to segment a cross-section of these businesses into five broad verticals with a few representative examples, some of which we are exposed to in our strategy. Cybersecurity has been added as a sixth vertical, since this area is concomitant with the proliferation of data.

Some of the common attributes among these verticals include exposure to recurring revenue models, critical workflow-embedded service propositions, scalability, high incremental margins, customer captivity, generally low capital expenditure requirements, and high cash flow predictability. These are all features that we value in quality businesses, which gives us some confidence that businesses of this kind could positively answer one of the two vital questions comprising our investment process: will this company compound wealth?

As a team, we believe there is an inextricable link between sustainability, innovation, and long-term compounding growth. Therefore, we want to invest in compounding businesses that also answer the second question in our investment process: is the world a better place because of this company?              

Our research suggests that characteristics of the broad verticals that we have outlined align well with the ideals that we typically seek out in sustainable businesses, including mitigating financial risks, fostering financial inclusion, promoting market transparency, as well as democratising access to information, among others. Figure 3 highlights some of the factors that showcase the environmental and social impacts of these areas, making this entire complex an exciting place to explore from an investment research perspective.

How are we playing the data-asset companies?

Apart from Microsoft, our strategy is exposed to companies within the Credit Rating Agencies and Other Information Services verticals, notably, S&P Global and Wolters Kluwer.

S&P Global exemplifies how data can serve as both a competitive advantage and a moat while fostering positive global outcomes. The company’s extensive databases, built over decades, provide credit ratings, market intelligence, and unparalleled insights that are trusted globally. Its ability to deliver accurate, reliable, and actionable data creates high barriers to entry for competitors, solidifying its position as a leader in the financial information industry.

The acquisition of IHS Markit on 28 February 2022 further enhanced S&P Global’s data capabilities, enabling deeper analysis across sectors like commodities, climate change, and supply chain management. This synergy allows the company to scale its offerings and cross-sell services effectively, reinforcing its defensible business model. Moreover, S&P Global leverages advanced technologies such as AI and machine learning to innovate its analytics platforms, ensuring clients can make informed decisions efficiently.

Beyond its business success, S&P Global positively impacts the world through initiatives aligned with sustainability and economic development. It supports the energy transition by providing data that helps businesses and governments address climate challenges. Additionally, its philanthropic efforts include funding job training programs for underrepresented communities and aiding vulnerable populations in adapting to climate realities.

By facilitating efficient capital flows toward impactful investments, S&P Global drives innovation, job creation, and inclusive growth globally. In essence, S&P Global’s mastery of data not only fortifies its competitive edge but also empowers stakeholders to tackle critical global issues, embodying the dual role of a profitable enterprise and a force for good.

Wolters Kluwer is another prime example of a company leveraging data as an edge while being closely aligned with sustainability. With roots dating back to 1836, the company has evolved from traditional publishing to providing mission-critical digital solutions for professionals in healthcare, law, finance, and accounting.

Its ability to manage vast amounts of data with precision and accuracy, enhanced by AI technologies, creates high barriers to entry for competitors and ensures customer trust. The company’s Corporate Performance and ESG division exemplifies its data-driven strategy. By integrating financial and non-financial reporting tools, Wolters Kluwer helps businesses navigate complex ESG regulations, ensuring compliance and fostering sustainable practices. Solutions like Enablon and CCH® Tagetik empower organisations to streamline processes, enhance decision-making, and quantify ESG impacts on business performance.

Wolters Kluwer also prioritises sustainability and social responsibility. It has set science-based targets to reduce greenhouse gas emissions and optimises its global office footprint with eco-friendly standards. Initiatives like solar lamp assembly for underprivileged communities highlight its commitment to societal impact. Additionally, its Risk Reporter app promotes workplace safety, showcasing innovative ways to address global challenges.

By combining advanced data capabilities with a focus on sustainability and governance, Wolters Kluwer not only maintains a strong competitive advantage, but also contributes to health, justice, prosperity, and environmental protection worldwide.

Conclusion

The rise of data asset companies, underscored by their strategic utilisation of proprietary data sets and their pivotal role in the advancement of AI technologies, marks a significant shift in the source of economic and technological power. These companies not only lead the charge in the current digital revolution but also lay the groundwork for a future where AI’s potential can be fully realised, impacting everything from medical diagnostics to personal and business decision-making processes.

The journey through the similarities and contrasts between oil and data, and the deep dive into the value and impact of data asset companies, highlights the critical importance of navigating the data landscape with foresight and strategic acumen. As we stand on the brink of an AI-driven era, the decisions made today by businesses, investors, and policymakers regarding data acquisition, usage, and governance will shape the technological advancements and societal norms of tomorrow. As W. Edwards Deming remarked – “In God we trust, all others must bring data”.