DAM News Round-Up – 31st January 2022
A collection of recent DAM and marketing technology related articles from around the web.
Digital Asset Management software provider MediaValet take a look at the benefits of using a Single Sign-On (SSO) in this recent article. SSO is an authentication method that allows users to sign onto a series of systems using a single set of credentials. The article provides a breakdown of the various benefits and how implementing SSO within your DAM system can improve security, productivity and scalability.
This fairly detailed article from DAM vendor Pics.io explores the use of CMS and Digital Asset Management within the online news sector. Topics include the advantages of paywalls over often intrusive advertising, managing updates and corrections, and a brief description of the main software titles available, such as WordPress, Joomla, and Drupal. Although the article invariably promotes their own platform, the benefits of implementing a CMS in tandem with a DAM could be applied to any combination of systems.
OpenText‘s Marc St-Pierre presents a primer in taxonomies. With a brief description of what taxonomies are, and how they can assist in enriching and organising your information to make it more discoverable, the article also differentiates between taxonomies, thesauri, and ontologies.
This interesting read from DAM provider MerlinOne takes us through the evolution of Digital Asset Management systems. From the early days of the mid 80s and the invention of digitization and compression formats such as JPEG, through to the 90s, the advent of free-text search engines, dramatic improvements in network speeds and non-local databases. The advent of video on the web in the early noughties is also covered, followed by developments in Artificial Intelligence, taking us into the current era and MerlinOne’s new wave of intelligent offerings such as their image recommendation engine IMPACT, and their most recent innovation, NOMAD – a system that relies solely on visual search and image recognition technology to find suitable content, even if it contains no metadata.
As more and more businesses and organisations implement AI solutions, a number of concerns have arisen relating to issues in the machine learning algorithms that are responsible for training such systems. In this first part of his series on the topic of bias within Artificial Intelligence, AI strategist Michael Wu investigates two key areas of concern: the so-called ‘black box problem’, whereby it’s unclear as to how an AI engine has reached its decisions; and the problem of bias in such decisions. A worthwhile read for anyone considering adopting an AI-based solution and are as yet unaware of the potential risks involved. The second instalment will focus on how to identify and correct biases in your source data.Share this Article: