The ability to examine why, when, and how consumers enjoy media, opens up new possibilities and brings great challenges. Here’s how you can optimize the media analytics workflows with a hybrid cloud workflow, and take advantage of viewer data and content opportunities.
The media industry is facing a period of unprecedented change. From the way media is produced and managed to the methods used to protect, optimize, distribute, and analyze content, every part of the value chain is being affected. These changes have created enormous pressures—and opportunities—for media organizations and creative professionals.
Organizations big and small have adopted the public cloud for analytics to address several business needs. The ability to easily scale resources as needed provides a flexible infrastructure for a media-centric world increasingly based on analytics. Convenient: yes. Flexible: yes. Inexpensive: it depends.
The Accelerated Digitization of the Media Value Chain
Technology has enabled almost every aspect of how we live to become increasingly digitized. It is not surprising that the social desire to connect more directly, efficiently, and powerfully is also impacting the media industry. But the acceleration of this seemingly obvious digitization trend is having a massive impact on the media industry and fundamentally altering the way we interact with media, both as consumers and as content creators.
At its simplest, content creators are more connected with the business (or monetization) side, causing the two previously separate components to be inexorably linked, and providing the opportunity for a more interactive, powerful, and efficient connection at every step from creation to consumption.
Media Analytics – Viewer Analytics vs. Content analytics
Digital technology is creating fascinating opportunities for measuring and analyzing content. Organizations now have the opportunity to use metadata analytics to examine why, when, and how consumers enjoy media. By incorporating immediate feedback into the content creation process, producers can tailor their output to consumer needs, likes, and trends.
However, these analytics happen after the fact and don’t allow for understanding of some of the true cost generators of in-house processes.
First There Was Cloud
The reason that many companies use the public cloud is to be able to spin up a massive number of compute cores to run a job and then decommission it after the job to avoid the capital cost if done on-premises. The tool set available in the public cloud can also be a compelling reason to use those facilities.
The case for on-premises analytics of media content is the ability to control the environment, including security, get predictable performance, as well as move and store data without incurring charges.
Media and Entertainment is an industry that has the challenges of massive data and extreme security. It is in most cases far more cost effective to keep the bulk of data on-premises.
The Move Towards Hybrid Cloud Media Analytics
In a hybrid analytics approach you can take advantage of understanding your viewer’s behavior as well as how your content is being generated and how it flows through the production chain to where business processes commence.
Companies want to control their media, and you may realize that you can reference media through more than one analytics job, or even combine assets from one production with the production flow and business use case of similar or separate productions and syndication models. You may also have massive historic data sets that you may want to access for predictive models.
Continuously storing media and egressing it from the public cloud is expensive. A more cost-efficient approach is to have the bulk of your media stored on premises in a highly scalable, low cost object-based system. This is a great foundation for unstructured data that can already “speak cloud” for a seamless hybrid cloud configuration.
In other words, you can leverage analytics both to optimize the workflow AND to learn what part of your media is required for distribution and replication in a cloud layer. Predictive models allow for a more in-depth review of existing assets and can engender both cost-savings and help producers with launch cycles and deadlines.
A Better Workflow for Media Analytics
In running analytics and comparing them with direct viewer feedback, you learn what media is essential and what parts of your workflow may be redundant. Replicate only the data you need from your on-premises system to the public cloud, and bring back the results only, not the raw data itself. This architectural approach will significantly reduce ongoing costs. In some use cases, such as Western Digital’s analytics workflow, it can save your company millions of dollars:
As you can see in the video, the ActiveScale™ system is a perfect fit for enabling a hybrid cloud analytics workflow. The ActiveScale cloud object storage system allows you to replicate buckets in your local system to Amazon® AWS™ where you can spin up compute, analytics tools and storage as needed. Send the results back to your on-premises ActiveScale and delete the data bucket in AWS. In this way you take advantage of the resources in AWS, retain control of the raw data, you get the results of the analytics job, and avoid high export fees of AWS.
Data can be synchronized with AWS from a single GEO or a 3-GEO configuration and enabled on a bucket level so you can balance the right combination of data copies on-premises and in public storage, and take advantage of extreme data durability (up to 19 nines!).
Improve Your Media Analytics Workflow
The right balance of on-premises and pubic cloud storage should be a conscious decision based on your data strategy. Understanding what data you want to keep, and for how long is important. These questions and scenarios can help you decide the right balance of local and public storage.
This is only a brief overview of the potential and capabilities of leveraging hybrid cloud for media workflows, but it can undoubtedly help you garner more value from your data.