How Machine Learning Is Changing the Game for Content Metadata


These are the best of times for entertainment content owners and distributors--but they are also very challenging times. There is more content--often great content--than ever before and also vastly more competition due to the rise of streaming services, as well as on-demand options.

How AI and metadata are taking the hard work out of content discovery


This is a particularly tough time for broadcasters and service providers. There's more competition than ever before thanks to instant streaming and on-demand viewing, and each company is in a battle for the best content. Pay TV operators have built enormous on-demand catalogues, and broadcasters are expanding their online services with more library content and short-form video. The ultimate goal for each company is to draw in as many viewers as possible, and ultimately to keep them engaged for as long as possible, too. But, with an ever-expanding sea of content in front of them, it's getting more difficult for viewers to choose what they want to watch.

Metadata feature makes immersive 3D spaces much more useful


Real estate was one of the first industries to adopt immersive 3D renderings, which are great for showing off swank pads on the haughtier real estate sites.But the technology has broad applications, from engineering to disaster recovery. Sunnyvale-based Matterport recently made news by presenting 3D models of homeless camps near the site of Super Bowl 50 in San Francisco. The STEM robot wars are heating up. Cubetto is the latest reason why you wish you were still a kid. But 3D modeling still feels a little gimicky.

How AI can enable smart and savvy content management in FS


Metadata attributes and tags allow a user to help find and retrieve content, but this data is often added by humans, which is both error prone and limiting. Automated entity extraction using business-driven AI presents another level of value to the business by delivering more attributes, with greater accuracy, and at a much faster pace than ever before. The key here is that the metadata attributes are specific to the business, which for example can then deliver tags with detailed product names, part numbers, customer accounts, etc., which instantly provides more value. This in turn drives applications such as automated image and content capture; automated launching of workflows and related business processes; even associating new content or assets with pending tasks or work assignments.

Why you need metadata for Big Data success


I recently wrote an article entitled'First Big Data initiative – why you need Big Data governance now!' and one of the comments received was from metadata expert and noted industry metadata presenter and speaker Bob Schork. I had the privilege of working with Bob in the past and have benefited from his extensive metadata insights over the years. What made me write this article was his comment stating that "metadata which is and will be ignored by many working on a BD (Big Data) project, to their own detriment." This resonated with me in that metadata is often taken for granted within the scope of Big Data projects, and the overall industry data management space for that matter, as well. This article will highlight why metadata is crucial to your Big Data project's overall success and to your enterprise data architecture organization.