The Data Guild is a venture studio based in San Francisco, California. We help to bring data-driven products to market - whether via spinouts from the Guild itself, by working in the trenches alongside portfolio company teams, or in concert with strategic partners. We work in healthcare, life sciences, renewable energy, and climate change – complex sectors whose needs are not well-served by the existing venture ecosystem, and in which finding innovative solutions and bringing them to market is crucial for the health and sustainability of our planet and society.
While we need to be highly selective about who we work with, and the problems we take on, we would love to hear from you Whether you're a technologist with a desire to solve some of the world's most pressing problems, an executive at a company looking to bring disruptive products to your market, or a founder at a company already working to bring something new into the world, we're ready to start the conversation. Have a look at portfolio examples to get a feel for what we're building right now.
We are steeped in machine learning, data science, and artificial intelligence, but we are adamant that technology choices be driven by a deep understanding of the problem at hand. In order to deliver real-world impact - not just tech showcases - we rely on the following principles in our approach to new product and business development.
The user and their experience is frequently, and unfortunately, an afterthought in data-driven product development. We start with that desired experience, and bring appropriate technologies to bear in service to it.
In the complex and challenging domains in which we operate, the problem space must often be enlarged to have any hope of achieving the desired result. We always take a step back to think about the diverse forces that may be at work to encourage or inhibit change.
All too often, the data that is made available to address a problem is viewed as fixed, and obtaining the desired result can feel like squeezing blood from a stone. Before invoking sophisticated machine learning, we always look for ways to collect additional data, or otherwise augment our knowledge.
Fully-automated systems may get the attention, but systems that combine human and machine intelligence in thoughtful ways are often what gets the job done. We look to leverage the strengths of both kinds of cognition to create systems with capabilities that exceed what either can accomplish alone.