DAM News Round-Up – 5th July 2021
DAM software provider Pics.io present a series of tips for getting the most out of your metadata. With some useful background information on taxonomies and metadata, the article also provides some best-practice advice and several use cases for the technologies and techniques a typical DAM would use to improve the findability of your digital assets. These include faceted search, drawing on EXIF data, and Boolean operators such as AND, OR, and NOT.
The latest interview from Henrik de Gyor’s Another DAM Podcast series features brand and digital liability consultant Scott Smith. Covering Scott’s past and current dealings with Digital Asset Management, topics include multi-platform integration, structured content authoring tools, martech stacks, and Scott’s views on the confusing crossover between the different types of content and enterprise management system, and how such confusion can become a source of digital liability.
Digital Asset Management software vendor WoodWing speak to their Managing Director for APAC John Fong about China’s firewall and the challenges it poses for multinational businesses that operate across borders. The article presents WoodWing’s ‘China Direct’ solution to the two-fold issue of a national firewall further restricted by a regulated internet. By negotiating with one of China’s government approved telecom providers, WoodWing users on both sides of the firewall can rely on guaranteed access to their digital assets.
Author and taxonomy expert Heather Hedden provides a breakdown of the various disciplines filed under the umbrella of Taxonomy Management. The article covers maintenance, governance, tagging, integration, review, and extensions such as multi-lingual translations. Heather will be discussing taxonomy management in more detail in an upcoming series of webinars during August – details of how to register for this free event will be announced in July.
DAM software provider MerlinOne have just announced their new Visual Search technology, which promises to alleviate the problem of poor quality search results due to insufficient metadata by using Deep Learning AI technologies to deliver results based solely upon the visual content of an image. NOMAD (No MetAData) has been trained to understand language patterns as well as visual content, and also has the ability to cluster phrases of similar meaning, so even if the asset has no metadata at all, there’s still a strong chance that NOMAD will be able to identify it. My co-contributor and DAM News editor Ralph Windsor is in the process of putting a demo edition of MerlinOne through its paces and is aiming to provide a more substantial review of the NOMAD technology in the coming weeks.Share this Article: