DAM News Round-Up – 23rd September 2019
This Cyangate article provides a basic checklist for tips on improving the ROI from your DAM system and should serve as a basic entrée for those tasked with setting up their own initiative or aiming to leverage as much potential from their DAM systems as possible. Covering flexibility, scalability, additional costs (such as initial import), support and user adoption, the post also provides links to a ROI calculator based on WoodWing‘s Swivle – a DAM-Lite offering which we reviewed last year.
Digital Asset Management solutions vendor ResourceSpace have recently published an article offering a series of key questions to ask prospective DAM service providers. There’s not a great deal that hasn’t been said before on this subject from other vendors such as Brandworkz, and although the suggestions are mostly common-sense, it strikes me as yet another weary attempt at pulling in new customers by regurgitating tired and vague information.
A case study from DAM provider WoodWing introduces us to magazine publishing services outfit PubWorX, and how they’ve customised their DAM platform to be used as both a photo portal and a creation and workflow martech solution. The article breaks down the various practices, tools and integrations, such as Google Forms and WoodWing’s ‘Scriba’ workflow engine, that have enabled them to implement a partially automated workflow. The contributor portal allows marketing teams and editors to create, review and approve ‘channel-neutral’ content.
Frédéric Sanuy takes a closer look at numerous aspects of AI within the martech industry, beginning with the important distinction between artificial intelligence and automation and how it’s ultimately the vendors responsibility to integrate AI into their workflow by learning from user behaviour – an approach discussed in some detail in a recent article by DAM News editor Ralph Windsor. Among the topics Frédéric covers are the practical uses of AI for image recognition, image enhancement and video, along with some commentary on the current shortcomings of autotagging and the lack of contextual metadata that AI is able to provide.Share this Article: