While model development is an important part of analytics, this activity can be compromised by a lack of understanding of the data used in these models and poor Data Quality. For insights to be relied upon and truly actionable, data-related issues must be addressed. The data supply chain (the set of architectural components that moves data around the enterprise from points where it is created or acquired to points where it is used) must be managed to supply the needs of analytics and other constituencies. This webinar describes how the data supply chain should be designed and operated to provide analytics with the data it needs, and how Data Scientists should interact with the data supply chain to obtain the data they need. It also covers: Data-centric considerations that must be taken into account in the development of analytic models Features of a modern data supply chain Major components in the data supply chain, with a focus on Data Lakes Major roles and responsibilities in the data supply chain How analytics must interact with the data supply chain