Data is here: Data is becoming ever more important in our lives: influencing, managing and even controlling many critical aspects. The use of AI systems is a new, exciting, but potentially hazardous use of data. LLM based systems are trained on data, and it is this data which enables them to be useful. Although machine learning (ML) systems tend to be trained on specifically curated data, these can also be subject to issues such as bias, poisoning and omission. Some of this data is related to our personal safety and well-being. Consider, for example, the importance of data defining the layout of railway signals, data that indicates the position of underwater obstructions in nautical channels, or data that is used to train a vision recognition system to detect tumours in medical images. Organizations now make significant decisions (including safety-related decisions) based solely on data held in systems. Hence, organizations need to safely manage, control and process their data. In particular, they must actively manage key data properties that preserve safety. Data is growing: There are many reasons why the use of data has grown and, equally important, why it is expected to continue to grow. The first relates to the rapid expansion AI, particularly LLMs which are trained on vast amounts of data. A second area is “Big Data”. A further area is the growing use of systems-of-systems, where data is the lifeblood that connects together disparate elements and allows a cohesive capability to be built. Put simply, the need to address data-related issues is a pressing problem and will continue to be. Data is causing harm: Strictly speaking, data can neither cause nor prevent harm. However, mistakes in data or inappropriate uses of data within safety-related systems have been factors in a number of documented accidents and incidents. Examples include aircraft attempting to take off from the wrong runway (and consequently crashing), ships running aground, and patients being exposed to higher than planned doses of radiation. Against this background, the DSIWG was established under the auspices of the SCSC. The DSIWG’s aim is to develop clear, cross-sector guidance that reflects emerging best practice on how data (as opposed to software or hardware) should be managed in a safety-related context. For the most part, this guidance is based on well-established techniques, and it has been designed to be compatible with current safety standards and to integrate with existing safety management systems. What is new, however, is the explicit and relentless focus on data, making it a “first-class citizen” within system safety analyses. Because of this focus, this guidance should help organizations identify, analyse, evaluate and treat data-related risks, thus reducing the likelihood of data-related issues causing harm in the future. The guidance is in three volumes, published as separate documents. Volume 2 (this volume) contains the informative material. It describes the “how” of the guidance and should be of particular value to data safety practitioners.