The future? AI-enabled cloud solutions for asset management
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Lucky us: We’re living in a golden age of content. Never before have consumers had such an outstanding lineup of content offerings from which to choose – from award-winning original programming on OTT services like Netflix, to a galaxy of live-streamed and live-broadcast entertainment and sports events. With so many types of content available and viewable on the consumer’s device of choice, it’s a good news/bad news quandary for media and entertainment companies.
First the good news: “I want my MTV.” Some of us are old enough to remember that bygone slogan that expressed viewers’ craving for music videos (back when MTV was all about music videos!) and well before the multiplatform internet age. That craving for all types of content has grown exponentially in the ensuing years, creating a potential gold mine of new opportunities for savvy media companies to develop lucrative new revenue streams.
And now for the not-so-good news.
As media companies look for ways to realize that new revenue potential, they’re running up against some real institutional barriers when they try to get their hands on the valuable content assets they need. And monetization is only part of the picture; the ability to access, restore, and revive business-critical content is at the core of archive management and disaster recovery.
The struggle is real: Take the example of one of our customers, an entertainment production company. At one point, this company was using four separate and disconnected on-prem platforms to store and manage video and photo content, some of which dated back to the 1990s. To find and access relevant assets for postproduction use, staffers were relying mostly on the tribal knowledge of longtime employees and researchers to know which system to search, and how to search within that system. Without a centralized and easy-to-navigate platform for content archiving – and without that institutional knowledge of how and where to look for things – new staff members didn’t even know where to start.
That’s where advanced digital asset management comes in.
In essence, modern asset management (whether that be DAM, MAM, PAM, etc.) enables a centralized repository for all of an enterprise’s visual media assets, including video, images, audio, and ancillary files, and makes it easy for users from any global location to search for, access, and use those assets. These platforms break down the institutional barriers created by siloed, incompatible storage systems, many of which were never designed in the first place to handle rich media content.
While the idea of an on-prem solution isn’t new, next-generation solutions take asset management technology to new levels through two powerful concepts: cloud-based software as a service (SaaS), and intelligent metadata through AI. Let’s take a closer look at both.
It all starts in the cloud.
SaaS solutions in the cloud have come a long way in recent years, thanks in large part to the growth of cloud object storage services. These on-demand cloud computing platforms have legitimized the cloud for enterprise business applications by removing security concerns and offering levels of scalability and performance that simply aren’t possible with on-prem solutions.
Locating asset management in the cloud has plenty of other advantages as well. For one thing, a cloud solution removes the firewall restrictions that can further hamper internal users’ access to an on-prem solution. Just as with any enterprise cloud application, maintenance and overhead costs are virtually eliminated with a SaaS asset management solution. By offering virtually unlimited capacity (from terabytes to petabytes), cloud object storage services remove the storage volume and scalability restrictions of local hardware.
Plus, getting up and running with a SaaS asset management deployment is orders of magnitude faster and easier than deploying an enterprise software system using on-prem hardware and networks. Once assets are migrated to cloud storage, media companies are able to present their users with a single, easy-to-use interface to give them fast, seamless access to the assets they need (subject to the company’s own access and discovery policies).
AI and metadata: a perfect marriage
Structured metadata – the content about the content – is the beating heart of any effective asset management solution in media and entertainment. The richer the set of details attached to a particular asset – date produced, persons/companies featured, location description, keywords, etc. – the easier the asset will be to find and the easier it will be to monetize and use for a specific task or project.
With previous-generation asset management solutions, creating detailed metadata has been a largely manual process requiring a staff member to view an entire program and physically record specific details about the content (for instance, company X’s logo first appears at 20:25 in a broadcast of a tennis match, and again at 1:20:10). Considering how time-consuming and labor-intensive this process is, it’s no surprise that the assets in many media companies have insufficient metadata or, sometimes, no metadata at all. No wonder it’s so difficult for their production teams to find and use the assets effectively and efficiently.
Enter AI, and more specifically advanced AI-based cognitive processing such as facial and object recognition and detection, audio fingerprinting, OCR, and transcription generation. In an advanced asset management solution, these capabilities add tremendous value through the automatic extraction of intelligent metadata about the content at the time of ingest. High-value personnel are freed from the tedious task of watching hours-long content in its entirety, and they can focus their time and energy on more creative work. And, because each asset is stored with an enriched and detailed set of metadata, the ease of discovery, usability, and intrinsic value of the content is increased tenfold.
Here’s another real-world example.
Going back to the aforementioned tennis example, a well-known national banking brand made a large ad spend for the live broadcast of the championship match, one of the most visible in the world of pro tennis. As an official sponsor of the “replay review,” the bank required the broadcast network not only to document every time its logo appeared onscreen, but every time the announcer mentioned the bank’s name when cutting to the “replay review.” Using the advanced, AI-driven logo detection and audio transcription capabilities of the asset management system, the network was able to provide this validation in a fraction of the previous manual process.
Perhaps the biggest advantage an intelligent cloud-native asset management solution can offer a media organization is the ability to curate its highest-value content and leverage it downstream in an infinite number of ways. With an intelligent, intuitive user interface and the ability to perform comprehensive searches for assets based on rich metadata, users can uncover content they might not have even known existed. Companies can create specific access levels that expose different types of content to different groups of internal and external users, opening up new opportunities for production houses, studios, news organizations, sports organizations, and other content owners to license their content to outside entities.
In the end, success in today’s highly complex media business is all about content – how to manage it, how to distribute it, how to sell it, how to make it easy for licensees to buy it, and how to understand the mine of data buried within. The ability to automatically enrich metadata using sophisticated AI technologies, coupled with the efficiencies, scalability, and cost savings of the cloud, are ushering in a new age of asset management. By gaining maximum leverage from content, their second-most valuable asset (with the first, of course, being people), media companies are primed for success in a competitive and ever-evolving market.
About Benjamin Howell, Veritone
Benjamin Howell is the director of product management at Veritone, where he oversees product management, market analysis, agile development practices, and data-driven decision making. His end-to-end project management experience has a strong focus on back-end architecture, user interfaces, and design.