M&E Journal: Monetizing M&E Content with Analytics, Artificial Intelligence and Machine Learning

By Erik Weaver, Director of Product Marketing, M&E Solutions, Data Center Systems, Western Digital Corporation

It doesn’t take a trained eye to recognize that the M&E industry is rapidly undergoing digital disruption. Compared to audiences 10 years ago, today’s consumers have access to unlimited amounts of content and are consuming it on an increasingly diverse range of technologies and platforms.

In this media-saturated environment, audiences demand content experiences that are enriching and convenient, and consumption patterns are changing in response. In the last 10 years alone, video streaming subscription rates in the U.S. have increased by approximately 450 percent, and this isn’t the only dramatic shift. Millennials now spend almost 15 percent of the time they devote to movies and television watching on smartphones. As a result, M&E leaders face increasing pressure to find new ways to monetize content and generate revenue.

Transformation is imperative

Companies that rely on traditional business models are now facing a new reality as their business declines, and leaders don’t have much time to act. In the U.S., for example, 27 percent of consumers without a paid television subscription indicate they cancelled it within the last year.

Film studios and production houses worldwide have captured a ton of footage over time but have not fully taken advantage of the uncovered treasures deep within these assets. There lies mineable content that may yield golden nuggets of value or that can be analyzed as part of a big data application delivering precise predictions, associations and desired outcomes.

To get to this end result, content must be stored reliably and economically, without losing any of it so that a path to monetization can be enabled through data analytics, artificial intelligence (AI) and machine learning (ML). Building a media repository and analytics platform can be accomplished on-premises, in the cloud or a combination of both.

Amazon’s public cloud has very mature services, a great partner ecosystem and provides ancillary capabilities that subscribers can tap into as part of a contracted service. Microsoft also has great partnerships and solutions that subscribers can leverage specifically for M&E use, such as Avere Systems’ enterprise cloud NAS that connects cloud storage and compute to a cloud-based enterprise infrastructure, or Avid Technologies’ audio and video digital non-linear cloud-editing capabilities.

Google’s public cloud has excellent toolsets for M&E purposes including analytics, rendering and transcoding, to name a few.

As public clouds technologically advance, it is difficult for studios to just settle on one. The best way for the film industry to monetize content is to take a neutral position toward public cloud storage.

A hybrid approach to analytics

Advancements in technology have created very interesting opportunities for measuring and analyzing M&E content. Producers can now incorporate immediate feedback and metadata into the content creation process, enabling them to tailor output specific to consumer needs, likes or trends.

Public clouds have also become very popular with production houses given their large storage capabilities, easy access and relatively small financial commitment for entry. Users can spin up massive amounts of compute cores or storage capacity to run a job and be able to decommission those resources after the job completes. There are also many exceptional toolsets available in the public cloud that studios can leverage as part of their service agreements.

Alternatively, storing content on-premises enables the most valuable M&E assets to be close, available and more economical at petabyte- scale when compared to public clouds. Today, object storage systems are becoming widely used as a central media repository or online archive for better management and control with predictable performance for data analytics. Unlike a public cloud, on-premises solutions enable users to move and store content – both files and objects — without incurring charges.

As such, production studios are now moving to a hybrid storage approach in which they can easily scale and take advantage of the results obtained from viewer behaviors, have more control over generated content, and better understand how content flows through the production process. They may also have massive amounts of historical data to analyze and develop predictive models for, or through machine learning, teach their cameras, video production equipment, and even wearables to be more effective.

Conducting analysis on-premises delivers two key benefits: First, the storage media can be referenced by more than one analytical job in the enterprise; and second, assets from any production job can be combined with the analytical workflow and use case of a similar, separate or syndicated production model to enable even further outcomes.

Why object storage?

To offset the high cost of continuously egressing content from a public cloud, a more cost-efficient approach is to have the bulk of the content stored on-premises in a highly scalable object storage system. This architecture stores content as files or objects, whether it’s a document, film, video, audio, image, photo or some other piece of unstructured data. Each stored object includes metadata that provides descriptive information about the object and the data itself. Since users define the metadata, data analytics, discovery techniques or other information can be enabled for large volumes of film or video at scale, and objects can be aggregated to deliver efficient capacity scaling. Metadata analytics are also well-suited for analyzing consumer preferences.

Object storage is where the film industry is headed as these systems can replicate data across three locations (similar to the file-based triple mirroring model), while only requiring one-third of the encoded object data at each location. They can also detect and self-heal errors behind the scenes using erasure coding and data scrubbing technologies, achieving up to 19 nines of data durability.

Object storage solutions, such as Western Digital’s ActiveScale system, can replicate locally captured film content into data buckets, and disseminate it to a public cloud. Using Amazon Web Services (AWS), for example, studios can spin up compute, analytic tools and storage as needed, and send the results back to the ActiveScale system on-premises for storage, while concurrently deleting the data bucket in AWS.

In this scenario, studios can take advantage of the resources available in AWS, retain control of the raw data, and get analytical results without absorbing export fees for the original data. ActiveScale is a fully cloud-optimized system that easily plugs into an on-premises private cloud configuration or a hybrid-cloud design, makes ingest and post-production activities more efficient, and provides an onsite content archive that supports all types of media.

Final thoughts

Production houses and studios globally have adopted the public cloud to protect, optimize, distribute and analyze their captured media content, and also use it to solve specific problems. Developing a hybrid-cloud approach with a public cloud and on-premises petabyte- scale object storage solution will help advance the monetization of content, enabling post-production teams to use the latest, best and most advanced tools and technologies available. This ability to extract further value and intelligence from captured content through analytics, artificial intelligence and machine learning is creating a currency of tomorrow — enabling the value of the content to live forever!


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