Why the Future of Digital Asset Management Hinges on Big Data

February 7, 2015
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Cloud storage business has truly taken off in the past couple of years. According to a Markets And Markets study, the public/private cloud storage industry is expected to be worth $56.57 billion by 2019. For perspective, the public cloud storage industry was estimated to be just around $21 billion a couple of years back, according to Technology Business Review.

Cloud storage business has truly taken off in the past couple of years. According to a Markets And Markets study, the public/private cloud storage industry is expected to be worth $56.57 billion by 2019. For perspective, the public cloud storage industry was estimated to be just around $21 billion a couple of years back, according to Technology Business Review.

Besides cloud computing, one of the popular applications of cloud is in digital asset management, or DAM for short. For the uninitiated, DAM refers to the process of storing, cataloguing, searching and delivering of digital files, mainly audio, video, images and office documents. DAM is an extremely critical element of businesses like media and journalism that deal with lots of content. A typical media house, especially one that deals with videos, owns digital assets that are dozens of terrabytes large. In terms of data volume, it is common for these media houses to own northwards of a hundreds thousand files. Tagging every file, along with storing and retrieving them is a lot of work.

Despite the volume of content that DAM works with, this is still not the realm of big data analysts. It is not difficult to see why. Most small and medium sized business houses still make do with in-house CMS tools for managing their digital assets. Even the larger businesses that pay for dedicated DAM services have not more than a million or two files to store and process. Big data is typically used for data volumes that are much higher.

That could be changing in the near future though. Lately, the scope of digital asset management tools has been growing from merely storing and cataloguing data to integrate with transactional business intelligence and analytics tool to provide more useful information. According to Ralph Windsor, a senior partner at consulting firm DayDream, one utility of big data in digital asset management is for marketers to apply analytics tools on DAM to identify and interpret actionable information – like using sales data to identify the kind of images or content that works versus ones that do not.

What is driving this evolution of digital asset management is the metadata that accompanies every digital asset. For instance, consider the two images of Steve Jobs below.

 MetaData example

At the outset, they are not too different. However, there are pretty distinctive differences between the two images. The one on the left was taken during the launch of iPhone 2G, while that on the right was during the iPhone 3G launch. In addition to this, there are other differences like the one on the left being a black&white image with Steve Jobs smiling. A sophisticated media management system would include all these minor details in the metadata. This way, it is easier to retrieve a particular image from a repository of millions of images at a later stage.

A DAM system that is integrated with big data analytics tools will be able to analyze the marketability of different images and track other details like conversion rates, social shareability. Such intelligence would be paramount for a company that wants to craft a business strategy for their future product launches – should the press release for an automobile company include the image of the vehicle or that of their popular CEO? A PR agency that uses DAM tools integrated with big data will be able to offer the right answer to such a client.