May 3, 2017 | Written by: Russ Kennedy
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The digital media industry has long faced a unique data challenge. Radio, television and film productions generate massive unstructured data sets, which can measure up to hundreds of terabytes total for a single large-scale film project.
The amount of data generated by these digital media productions is growing rapidly. In fact, IDC has projected that the world’s total amount of data will grow to 44 zettabytes (or a billion terabytes) by 2020, and a whopping 80 percent of that growth will be from unstructured content, much of it created by the digital media industry.
Managing and storing all this digital content can be both complicated and costly.
Not only do digital media companies manage the data generated by their current projects, they also store and organize all the data associated with their previous broadcasts, shows and movies. In this way, they take on the role of archivists, not only to manage their own assets, but also to archive them in a way that provides historical value to society. You’ve seen this come into play when broadcasters share old audio clips and video footage in order to provide context and historical perspective on current events.
In the past, managing this massive amount of data in a way that makes it easily searchable and accessible has been labor intensive. The early process of doing this required that content be manually logged. This produced basic details of the footage, including subject matter, date and time. Eventually this was automated using media asset management (MAM) software, which saved in human capital costs but did not necessarily deepen the understanding of usable assets broadcasters had in their archives.
Today, we are seeing that the industry is rethinking its data strategy in an effort to redefine the value of content and how that impacts the bottom line. Organizations are discovering new ways to use and manage their data in a way that turns what used to be a costly obligation into a potential revenue-building asset.
Danmon Group, an international broadcast and media solutions provider headquartered in Denmark, delivers solutions ranging from complete turnkey television and radio stations to outside-broadcast vehicles and satellite communications as well as virtual studios, archive & storage solutions and workflow management software.
When Danmon sought a new strategy for helping its customers storing and managing their massive collection of unstructured object data, it identified two critical needs: a global, scalable cloud infrastructure and the ability to reach into its data library to quickly pinpoint specific content on demand.
Danmon chose a combination of IBM solutions to fulfill its goals. First, the company taps IBM Aspera High Speed Data Transfer to upload content to the IBM Cloud data center. Then, to efficiently manage all the data, Danmon turns to IBM Cloud Object Storage and IBM Watson.
Data is ingested into the IBM Cloud frame-by-frame while its metadata is analyzed by IBM Watson Visual Recognition. Danmon uses this Watson API to apply visual recognition to the clips and footage and adds meta tags on-the-fly and does so, according to Danmon, with a level of granularity that far exceeded that of a MAM system.
It will be a game changer as it can help Danmon’s customers turn their once burdensome data storage and archiving process into a potentially huge asset – a library of easily searchable, accessible content in the cloud.
Another Danish company that has recently turned to IBM Cloud Object Storage and Watson technologies is any.cloud, which provides cloud hosting for customers across a wide range of industries, including digital media.
Any.cloud deployed IBM Cloud Object Storage to manage the massive amounts of unstructured data generated by its digital media customers, and it is currently testing IBM Watson Visual Recognition for a new service. The expansion of any.cloud’s data archive offerings, by combining its 24×7 storage availability on IBM Cloud with Watson services and technology, can help clients derive new insights and value from the data they store on any.cloud, giving both any.cloud and its client base new revenue streams.
Learn more information on IBM at NAB 2017. Find out more about IBM Cloud Object Storage.
A version of this article originally appeared on the IBM THINK blog.