Data-intensive science is reality in large scientific organizations such as the Max Planck Society, but due to the inefficiency of our data practices when it comes to integrating data from different sources, many
projects cannot be carried out and many researchers are excluded. Since about 80% of the time in dataintensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges
that will come with the billions of smart devices producing continuous streams of data—our methods do not scale. Therefore experts worldwide are looking for strategies and methods that have a potential for the future. The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers (PIDs) and metadata (MD). In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use. It is argued, however, that assigning PIDs is just the first step. If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed, we are close to defining Digital Objects (DOs) which could indeed indicate a solution to solve some of the basic problems in data management and processing. In addition to standardizing the way we assign PIDs, metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations. We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry. A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.