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CanepaBy Steve Canepa

If there is one constant in the 21st century media and entertainment marketplace – it is constant change.  As billions of intelligent devices, hybrid networks, advanced services, apps, and digital content continue to evolve in uncountable ways, media firms focusing on the production and distribution of information, news, entertainment and social experiences are facing a daunting challenge: how to make sense of it all – while providing unique value.

This is precisely why IBM’s Watson is so important.

The common denominator of this constant change is unstructured data – whether that is the content itself as presented in words — or digital images, audio and video – or the metadata that surrounds it.  We are witness to a rapidly expanding universe of human language content, web-sites, blogs, posts, tweets, social comments – not to mention books, magazines, newspapers and reports.

As rising consumer expectations continue to drive advances in consumer intimacy, segmentation, marketing, packaging and price innovation, the ability to rapidly analyze these massive quantities of unstructured or natural language data in order to extract intelligence, insight and value will be a key competency in the new world of media, entertainment and experiences.

So, what is Watson?  A computing system that rivals a human’s ability to answer questions posed in natural language with speed, accuracy and confidence. Watson represents the ability to gain meaningful insights from massive amounts of unstructured data and demonstrates in dramatic fashion how advanced analytics can be applied in creative new ways.

Watson takes analytics to a whole new level of power and sophistication, beyond traditional data warehousing and data mining. This new computing paradigm – known as DeepQA – can deliver faster, deeper insights into unstructured data and human language text  – discovering consumer preferences, contextual relationships, usage patterns and purchasing behavior — which can deliver unique value for the media consumer.

There are tremendous opportunities to turn these ‘big-data’ challenges into marketplace advantages by using advanced analytics.  Just imagine:

  • Semantic classification engines that speed time-to-air and increase content availability.
  • Collaborative and real-time campaign and brand management systems
  • Optimization routines for scheduling, programming, marketing and advertising
  • Automated multi-channel distribution
  • Improved Customer Care that goes well beyond traditional CRM systems
  • Enhanced resource optimization – both human and technology

I could go on.  The point is that the capabilities represented in Watson open up a new world of possibilities – and insight – for agile media and entertainment companies.

The data is there.  The question is, are we getting the intelligence out of it?

Steve Canepa is IBM’s General Manager for the Global Media and Entertainment Industry. He is responsible for the world-wide organization covering the Entertainment, Broadcast, On-line, Cable, Publishing, Satellite, Sports, Music and Advertising segments.  Steve has over 25 years of experience building, growing and leading profitable businesses.  Digital Media Magazine named him one of the most influential executives in Digital Media and he has received an EMMY for innovation.  His previous post on this topic can be found here.

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1 Comment
 
August 18, 2011
1:13 pm

Adding structure to human language and the massive unstructured data sets that the web makes readily available, is the wave of the future. And as you accurately described, the commercial applications are potentially limitless.

And I’ll admit that Watson is truly impressive to watch on Jeopardy but this is far from a commercial application. Big Blue is sitting on this technology, flexing a muscle for the public but what are its real plans for applying Watson to real world problems that create value for consumers and IBM shareholders? I’ve heard rumors of a Watson powered virtual sales-rep who can answer questions about any product or service but how close is that idea to being market-ready?


Posted by: Jonathan Cardella
 
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