Commentary

The Metadata Dilemma: The Prequel

Imagine winning an eight-figure lottery only to find that your winnings are locked in a vault - and you must guess the combination.  You have your eyes firmly fixed on the prize and on all the things you want to do with your newfound assets, but you must first rise to this seemingly insurmountable challenge.

This is much the plight of content owners in the digital media business. This group possesses priceless assets in the form of huge libraries of movies and television shows dating back decades. These libraries are ever-expanding in size and content.

Whether it's movie studios, broadcast networks or TV show makers, the goal is the same: to optimally monetize all assets whether that takes the form of new revenue streams or broadening current tributaries into revenue rivers.

While that may sound overly simplistic, the actual map to monetization used to show a long path to profit minus clear-cut shortcuts. That's what automakers, retailers and consumer products giants faced beginning in the late 1990s, and well after the turn of the century.

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Learning from the Past

A case in point of the work involved in turning data into dollars is the U.S. auto industry pre-millennium. Automakers were comprised of multiple divisions and their associated brands. They collected voluminous customer data but each stored the raw data in separate silos, and let it sit.

As the wide gap between product development and customer needs grew, and the data piled up, enterprise IT visionaries finally swung into action.

First, they moved all their customer data into a central repository called a data warehouse.

Second, they acquired data mining products to help them separate the empowering data from the rest.

Third, they purchased enterprise reporting systems to make sense of the precious data they had separated out. Reports put data in perspective and historical context, for example.

Next, while reports got top executives thinking, analytics package and business intelligence software were implemented to help translate thinking into empowering smart business decisions that could improve the way the automakers did business and close the gap between their practices and the actual needs of their customers.

Bad News, Good News

The "bad" news is that this landmark, industry-wide undertaking took many years and carried a price tag that would send most into sticker shock.

The good news is that this long and arduous process, one that largely transformed the auto industry -- and others - is far easier, faster and simpler today thanks to robust frameworks that shortcut what the auto industry endured by focusing on collecting metadata on digital media assets.

And it's more good news that content owners already have their digital media assets organized in sensible libraries which dramatically truncates the process the automakers - with far-flung data silos and information of varying value - embarked upon.

Unlike the automakers of the 1990s, content owners already know their customers and benefit immensely by collecting granular metadata. And carmakers spent big time and money separating critical data from oceans of information. Metadata in content can help clients go beyond movies and TV shows to find digital media by a single frame of video content. This promises to takes search and discovery to the next level.

Tag, You're It

The voluminous data the automakers collected, much like the content assets digital media companies have and must monetize, must be "tagged" with metadata to be of value. The more detailed the metadata, the more ways it can be distributed to connected devices.

Metadata can help clients go beyond movies and TV shows to find digital media by a single frame of video content. This promises to takes search and discovery to the next level.

 This trend portends to make search and discovery for customers looking to find a specific movie, with specific actors, and specific clips far easier for generations used to near instant results from Google.

Take metadata a step farther and you have the fuel that powers recommendation engines. As we've seen from the example of Amazon and TiVo, these powerplants can drive both customer interests in content and additional revenue opportunities forward.

But remember, without granular metadata - which is more than just data about data - and the tools that are used in combination - your lottery winnings are still locked away in the vault.

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