Connections

By Ben Kus, Senior Director, Product Management, Box –

The competition and complexity facing M&E companies and the management of their data today has never been greater. Given the accelerating race to get to market faster, content needs to be created and produced efficiently, organized automatically, and then delivered across the globe to eager fans. But between dispersed and transient production teams and multiple legacy systems, the more data you have, the harder it is to make sense of it all. As content moves between teams and systems, businesses must rely on manual data entry and workflows to organize, analyze and use all this information.

But with the cloud and maturing machine learning algorithms, businesses can now automatically add structure to all their information and automate processes around it. This would save hours of manual review and data entry, as well as allow business to retire legacy systems, which would translate to real cost savings and optimization for businesses.

We’ve all heard that artificial intelligence and machine learning will change the way people work in the next several years, but amid all the hype, here are a few practical ways that media and entertainment companies can apply these breakthrough technologies to the way they work today.

Contract text extraction

Managing contracts is a complex process involving a wide range of document types and many different parties. M&E companies can streamline the process by automatically extracting specific fields from documents according to an organization’s needs, leveraging machine learning-supercharged optical character recognition (OCR) and natural language understanding (NLU) technologies. Once extracted, fields can automatically trigger processes or routing to the appropriate systems or individuals for review.

Freelance document classification

Managing the ebb and flow of individuals required for individual projects can be overwhelming and costly. Machine learning can help automatically classify documents, such as tax forms and agreements, as part of on-boarding, managing and off-boarding freelancers and crew for production. Documents can be classified and labeled using machine learning-based classification technologies, triggering applications of specific policies based on the type of sensitive personnel document, such as a data retention policy.

Image, audio and video labeling

M&E companies manage large libraries and archives of content, ranging from images, footage, posters and trailers. During pre- and post-production, marketing departments within these organizations can leverage machine learning to recognize objects, characters, text and faces within all these images and recordings to streamline automatically, rather than pouring through pages and pages of files.

Beyond easier discovery, these technologies can transform content-based workflows. For instance, like with the document examples previously mentioned, once images are automatically labeled with a set of tags based on what’s inside the image, businesses can automate the subsequent process around that content – assign the image to the editor for final touch-ups, send to leadership for approval, and then trigger pre-specified distribution that piece of collateral.

Furthermore, with custom machine learning models, businesses can automatically label a particular actor or character in a movie, or even members of their own executive team, to improve the efficiency and standardization of huge libraries of visual and audio assets.

The applications for machine learning around content in the M&E industry are plentiful. We’re only starting to scratch the surface when it comes to harnessing the power of artificial intelligence and machine learning to enhance how media and entertainment businesses get their work done and the next several years are going to be monumental.

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