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Writer's pictureCraigRhinehart
Mar 21, 20114 min read

Watson and The Future of ECM

Updated: Oct 22


Watson and the Future of ECM


Enterprise Content Management (ECM)


In the past, I have whipped out my ECM powered crystal ball to pontificate about the future of Enterprise Content Management. These are always fun to write and share (see Top 10 ECM Pet Peeve Predictions for 2011 and Crystal Ball Gazing … Enterprise Content Management 2020). 


This one is a little different though …  on the eve of the AIIM International Conference and Expo at info360, I find myself wondering … what are we going to do with all this new social content … all of these content-based conversations in all of their various forms?


We’ve seen the rise of the Systems of Engagement concept and a number of new systems that enable social business. We’re adopting new ways to work together leveraging technologies like collaborative content, wikis, communities, RSS and much more. All of this new content being generated is text-based and expressed in natural language. 


I suggest you read AIIM’s report Systems of Engagement and the Future of Enterprise IT: A Sea Change in Enterprise for a perspective on the management aspects of the future of ECM. It lays out how organizations must think about information management, control, and governance in order to deal with social technologies.


Social business is not just inside the firewall though. Blogs, wikis and social network conversations are giving consumers and businesses a voice and power they’ve never had before … again based in text and expressed in natural language. This is a big deal. 770 million people worldwide visited a social networking site last year, according to a ComScore report titled, Social Networking Phenomenon, and amazingly, over 500 billion impressions annually are being made about products and services, according to a new book, Empowered, written by Josh Bernoff and Ted Schadler.


What is buried in these text-based natural language conversations? 


There is an amazing amount of information trapped inside. With all these conversations happening between colleagues, customers and partners … what can we learn from our customers about product quality, customer experience, price, value, service and more?  What can we learn from our internal conversations as well?  What is locked in these threads and related documents about strategy, projects, issues, risks and business outcomes?


We have to find out!  We have to put this information to work for us.


But guess what? The old tools don’t work. Data analysis is a powerful thing but don’t expect today’s business intelligence tools to understand language and threaded conversations.  When you analyze data … a 5 is always a 5. You don’t have to understand what a 5 is or figure out what it means. You just have to calculate it against other numeric indicators and metrics.


Content … and all of the related conversations aren’t numeric. You must start by understanding what it all means, which is why understanding natural language is key.  Historically, computers have failed at this. New tools and techniques are needed because content is a whole different challenge. A very big challenge. Think about it … a “5” represents a value, the same value, every single time. There is no ambiguity. 


In natural language, the word “premiere” could be a noun, verb or adjective. It could be a title of a person, action or the first night of a theatre play. Natural language is full of ambiguity … it is nuanced and filled with contextual references. Subtle meaning, irony, riddles, acronyms, idioms, abbreviations and other language complexities all present unique computing challenges not found with structured data. This is precisely why IBM chose Jeopardy! as a way to showcase the Watson breakthrough.


Watson and Jeopardy!


IBM Watson (DeepQA) is the world’s most advanced question-answering machine that uncovers answers by understanding the meaning buried in the context of a natural language question. By combining advanced Natural Language Processing (NLP) and DeepQA automatic question answering technology, Watson represents the future of content and data management, analytics, and systems design.  


IBM Watson leverages core content analysis, along with a number of other advanced technologies, to arrive at a single, precise answer within a very short period of time. The business applications for this technology are limitless starting with clinical healthcare, customer care, government intelligence and beyond.



Natural language-based computing and related analysis is the next big wave of computing and will shape the future of ECM. 


Back to my crystal ball … my prediction is that natural language-based computing and related analysis is the next big wave of computing and will shape the future of ECM. Watson is an enabling breakthrough and is the start of something big. With all this new information, we’ll want to use it to understand what is being said, and why, in all of these conversations. 


Most of all, we’ll want to leverage this newfound insight for business advantage. One compelling and obvious example is to be to answer age-old customer questions like “Are our customers happy with us?” “How happy?” “Are they so happy, we should try to sell something else?” … or … “Are our customers unhappy?” “Are they so unhappy, we should offer them something to prevent churn?” Understanding the customer trends and emerging opportunities across a large set of text-based conversations (letters, calls, emails, web posts and more) is now possible.


Who wouldn’t want to understand their customers, partners, constituents and employees better?  Beyond this, Watson will be applied to industries like healthcare to help doctors more effectively diagnose diseases and this is just the beginning. 


Organizations everywhere will want to unlock the insights trapped in their enterprise content and leverage all of these conversations in ways we haven’t even thought of yet … but I’ll save that for the next time I use my ECM crystal ball.


As always … leave me your thoughts and ideas here.


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