Watson and The Future of 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 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 have 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).

But what is buried in these text based natural language conversations?  There is an amazing amout 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, an 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.

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.

You can read some of my other blog postings on Watson (see “What is Content Analytics?, Alex”, 10 Things You Need to Know About the Technology Behind Watson and Goodbye Search … It’s About Finding Answers … Enter Watson vs. Jeopardy! … or better yet … if you want to know how Watson actually works, hear it live at my AIIM / info360 main stage session IBM Watson and the Impact on ECM this coming Wednesday 3/23 at 9:30 am.

BLOG UPDATE:  Here is a link to the slides used at the AIIM / info360 keynote.

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 to understand what is being said, and why, in all of these conversations.  Most of all, we’ll want to leverage this new found 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?” Undestanding the customer trends and emerging opportunities across a large set of text based conversations (letters, calls, emails, web postings and more) is now possible.

Who wouldn’t want to undertstand 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 and hope to see you Wednesday at The AIIM International Conference and Expo at info360 http://www.aiimexpo.com/.

IBM at 100: UPC … The Transformation of Retail

In my continuing series of IBM at 100 achievements … this is one of my favorites of all the ones I plan to republish here. The humble Universal Product Code (UPC), also known as the bar code, along with the related deployment of scanners, fundamentally changed many of the practices of retailers and all organizations that buy and move things, from large industrial equipment to pencils purchased in stationery stores. These two technologies led to the use of in-store information processing systems in almost every industry around the world, applied to millions of types of goods and items. UPC is planet Earth’s most pervasive inventory tracking tool.

N. Joseph Woodland, later an IBMer but then working at Drexel Institute of Technology, applied for the first patent on bar code technology on October 20, 1949, and along with Bernard Silver, received the patent on October 7, 1952. And there it sat for more than two decades. In those days there was no way to read the codes, until the laser became a practical tool. About 1970 at IBM Research Triangle Park, George Laurer went to work on how to scan labels and to develop a digitally readable code. Soon a team formed to address the issue, including Woodland. Their first try was a bull’s-eye bar code; nobody was happy with it because it took up too much space on a carton.

Meanwhile, the grocery industry in post-war America was adapting to the boom in suburban supermarkets–seeking to automate checkout at stores to increase speed, drive down the cost of hiring so many checkout clerks and systematize in-store inventory management. Beginning in the 1960s, various industry task forces went to work defining requirements and technical specifications. In time the industry issued a request to computer companies to submit proposals.

IBM’s team had also reworked its design going to the now familiar rows of bars each containing multiple copies of data. Woodland, who had helped create the original bull’s-eye design, then later worked on the bar code, writing IBM’s response to the industry’s proposal. Another group of IBMers at the Rochester, Minnesota Laboratory built a prototype scanner using optics and lasers. In 1973, the grocery industry’s task force settled on a standard that very closely paralleled IBM’s approach. The industry wanted a standard that all grocers and their suppliers could use.

IBM was well positioned and became one of the earliest suppliers of scanning equipment to the supermarket world. On October 11, 1973, IBM became one of the earliest vendors to market with a system, called the IBM 3660. In time it became a workhorse in the industry. It included a point-of-sale terminal (digital cash register) and checkout scanner that could read the UPC symbol. The grocery industry compelled its suppliers of products in boxes and cans to start using the code, and IBM helped suppliers acquire the technology to work with the UPC.

On June 26, 1974, the first swipe was done at a Marsh’s supermarket in Troy, Ohio, which the industry had designated as a test facility. The first product swiped was a pack of Wrigley’s Juicy Fruit chewing gum, now on display at the Smithsonian’s National Museum of American History in Washington, D.C. Soon, grocery stores began adopting the new scanners, while customers were slowly educated on their accuracy in quoting prices.

If there had been any doubts about the new system’s prospects, they were gone by the end of the 1970s. The costs of checking out customers went down; the accuracy of transactions went up; checkouts sped up by some 40 percent; and in-store inventory systems dramatically improved management of goods on hand, on order or in need of replenishment. And that was just the beginning. An immediate byproduct was the ability of stores to start tracking the buying habits of customers in general and, later, down to the individual, scanning bar coded coupons and frequent shopper cards. In the four years between 1976 and 1980, the number of grocery stores using this technology jumped from 104 to 2,207, and they were spreading to other countries.

In the 1980s, IBM and its competitors introduced the new technology to other industries (including variations of the American standard bar codes that were adopted in Western Europe). And IBM Raleigh kept improving the technology. In December 1980, IBM introduced the 3687 scanner that used holographic technologies—one of the first commercial applications of this technology. In October 1987, the IBM 7636 Bar Code Scanner was introduced–and as a result, throughout the 1980s factories adopted the IBM bar code to track in-process inventory. Libraries used it to do the same with books. In the 1990s, hand-held scanners made it easier to apply bar codes to things beyond cartons and cans and to scan them, eventually using wireless technology. Meanwhile innovation expanded in the ability of a bar code to hold more information.

These technologies make it possible for all kinds of organizations, schools, universities and companies in all industries to leverage the power of computers to manage their inventories. In many countries, almost every item now purchased in a retail store has a UPC printed on it, and is scanned. UPC led to the retirement of the manual and electro-mechanical cash registers which, as a technology, had been around since the 1880s. By the early 2000s, bar code technologies had become a $17 billion business, scanned billions of times each day.

The full text of this article can be found on IBM at 100: http://www.ibm.com/ibm100/us/en/icons/upc/