# How the Sausage Gets Made {% embed url="" %} ​What happens to those all-important keywords, vocabularies, and facets when they finally get to retrieval and 'consumption'. A platform-agnostic understanding of how data gets delivered, and a discussion of best practices so as not to lose our objectives in the midst of multiple and sometimes conflicting workflows. * Date/Time: Friday, February 2, 2024 8:00 AM - 9:00 AM PST * Speaker: Laura Langford ### ​Meet Laura Langford ​Laura has worked in the trenches of digital production since 2005. Most recently, she worked for Adobe Customer Solutions (AEM) and Caterpillar (OpenText Media Manager) as a DAM Strategist and DAM Librarian. She has deep experience with the day-to-day challenges of content delivery and management. Her favorite aspect of digital marketing is, of course, asset organization/curation and creating metadata schemas and taxonomy supporting asset lifecycle. She enjoys getting deep in the weeds with developers and technical architects, as this is crucial to ensuring streamlined content delivery. She looks forward to sharing her knowledge of the data delivery 'metaverse'. {% tabs %} {% tab title="Presentation " %} {% embed url="" %} {% endtab %} {% endtabs %} *** ### Hosted by Taxonomy Talk ​Taxonomy Talk is a Discord community that is open to anyone working within information architecture and knowledge organization spaces, no matter their level of experience or knowledge. This includes people who work on, manage, or are responsible for taxonomies *even as just part of their job*. *** ### ​Questions? Comments? Contact the moderator team at --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://www.information-architecture.world/taxonomy-talk/events/how-the-sausage-gets-made.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.