Where we come from
Our story began with the creation of Uncharted Technologies in 2018, an AI research lab ambitioning to bring the benefits of new technologies to the investment world. We grew our team to more than 10 within a year and had many great achievements such as the joint-publication of several AI research papers at prestigious conferences with high-profile researchers from Oxford or Moscow.
But we rapidly felt the lack of practical applications and the difficulty to make a business from AI research and we soon faced a tough choice: persevere or pivot towards another field where we could leverage our knowledge and experience. We eventually decided to start over with a product approach, but we kept a genuine passion for big data and analytics.
Alongside this exciting endeavor, we worked with some of the leading asset managers to improve the way they managed their portfolios. For over two years, we had more than €100m under advisory. From this experience, we acquired a real understanding of the day-to-day work of asset managers.
Working on both front simultaneously, we gained invaluable insights on the challenges of asset managers and understood early on that new technologies have the potential to solve them.
The insights we learned along the way
We realized that in recent years, investors merely replaced one problem — data scarcity — with another: data overload. Indeed, until the 1990s, the main issue used to be data collection. Investment professionals would spend most of their time sifting through annual reports and newspapers to find the information they needed. Since the advent of personal computers, excel, and the internet, investors have had access to a wealth of data that they can organize and process.
However, both our own experience and the testimonies of the many investors we interviewed confirmed that this development has not been entirely positive. Instead of focusing on elaborating investment theses, investors now mainly produce and compile information on Excel sheets — a tool which is in no way adapted to their needs — which makes them increasingly feel like robots. Furthermore, such an amount of data does not necessarily enable better and clearer investment decisions, quite the contrary. It first makes it harder to identify relevant actionable insights. Second, it makes decisions less transparent since scores of different datasets and insights now come into the equation — a significant issue given the emphasis that is put on transparency since the 2008 financial crisis. Finally, the cost of acquiring data and developing analytical capabilities has not been matched by significant portfolio performance improvements.
We became convinced that putting the human factor back at the center of the investment decision-making process is the best way to manage overwhelming amounts of data. We believe investors need to:
- leverage institutional knowledge by first building up a solid base of insights and investment theses, and then re-using these assets to avoid repeating low-value tasks;
- perform due diligence and other research & analysis tasks as a team to deliver higher quality investment ideas;
- easily share insights and analyses with external experts to challenge their views;
- edit, comment and contribute to other team members’ analyses
- update their work in order to keep up with real-time events and the latest market developments
- bring transparency to their investment process, with time-stamped documents which can be easily reviewed by peers
Our ambition today
The result is Thread, a workspace that allows for deeper collaboration around highly relevant data. With Thread, our ambition is to enable investors to improve their investment decisions and deliver consistently higher performance.
We hope this story gives you a better sense of why we developed Thread. Building a workspace to help investors collaborate around data is not an easy task. If you would like to learn more about our product or sit around the table and experiment with us, reply to this publication and we will be happy to schedule a call!
