Around 2012 I started to investigate the potential of data-driven intelligent systems for sustainability. I was (and still am) very motivated by the potential of the Internet of Things (IoT), data analytics, and artificial intelligence to create intelligent systems that can contribute to a sustainable society. As a computer scientist with a background in distributed systems and data management, I felt I could make a modest contribution to the design and construction of these intelligent systems. There was significant for data-driven and artificial intelligence techniques to power intelligent systems for sustainability. However, for these approaches to be viable, they would need to be cost-effective and deployable. Working with my industrial collaborations, it was clear that a critical barrier to adoption of intelligent systems was, and still is, the high upfront costs associated with data sharing and integration. For decades we have seen the consequences of data silos within Enterprises with estimates of 80% of the costs of data projects going to data integration and preparation activities. This limits large-scale data management projects to large organisations that have the necessary expertise and resources. This needs to change if we want a broad effort for sustainability that enables smaller stakeholders to engage and leverage the value available in data.