Watson Trickle-Down? Will $100M More Trickle Watson Down to SMB Enterprises?

IBM Watson  logo (via Wikipedia)

Bloomberg News reported that IBM plans to invest an additional $100 million in its Watson technology. Earlier in 2011, Watson exceeded previously unmet expectations for artificial intelligence by easily overwhelming two Jeopardy! champions on national TV. While Watson-like technologies could be used in a variety of settings (e.g., network management or health care), the steep investments IBM has already made suggest that global services giant has its eye on a revenue stream whose major tributaries are large enterprises: Proctor and Gamble, Pfizer, ExxonMobil, JPMorgan Chase. 

ArnoldIT's April Holmes put it this way:

IBM has a Tundra truck stuffed with business intelligence, statistics, and analytics tools [SPSS, InfoSphere Streams and Cognos come to mind - ed.] IBM has no product. IBM . . . has an opportunity to charge big bucks to assemble these components into a system that makes customers wheeze, "No one ever got fired for buying IBM."

Promising but out of reach? Few have been fired for asking, "Can we afford IBM?" In a recent Technology Review interview, IBM Analytics head Chid Apte admitted that "This technology will form the basis of a new product we will in the future be able to offer all of IBM's big customers."

The reasons for the anticipated cost are readily apparent. It has been widely reported that Watson took four years to build, runs on around 2,800 Power7 processor cores, has 15 terabytes of main memory, can operate at 80 teraflops (80 trillion operations per second), and employs IBM's SONAS file system with a capacity of 21 terabytes. Watson software components included some familiar open source technologies IBM had already adopted elsewhere, such as Eclipse and Apache Hadoop, but new ground was broken in creating a natural language understanding system tailored to perform in the Jeopardy! question and answer format. The cost for that capability alone was considerable.

IBM believes this revenue stream will be substantial. According to the Bloomberg article, IBM projects $16B from "business analytics and optimization." This estimate is probably not unfounded. A 2011 IBM-sponsored study of 3,000 CIO's reportedly found that 4 out of 5 executives indicated that applying analytics to IT operations was part of their "strategic growth plans." 

But what are the prospects for small and medium sized enterprises (SMB's)? Large data warehouses are not only associated with large enterprises. Small firms - even a one-person consultancy -- can easily amass huge quantities of data, and may be even more highly motivated to make sense of that data. However, they are unlikely to have Watson-scale budgets.

Still, there are a few possible scenarios in which Watson technology could reach SMB's:

• Cloud-based Watson resources, with cost reductions made possible by scale (a la Google search), could become more widely available 
• "Watson Light" -- Restricted vocabularies and data sources, possibly sold through IBM partners
• Bundling of certain Watson components with existing, more affordable IBM products 
• A la carte offerings, such as the CRM-integrated "Next Best Action" recommender systems envisioned by Forrester's James Kobielus 
• Industry-specific offerings in which the raw Watson capabilities are harnessed behind the scenes by IBM specialists

The challenge of providing robust hardware and software capabilities to collect, host and access large scale data warehouses using Watson-like technologies is not a near term possibility for smaller enterprises. It should be remembered that existing natural language technologies, such as the highly effective speech recognition technology Microsoft seamlessly integrated into Vista and Windows 7,  have not been widely adopted, even though for many types of human-computer interactions, it is an efficient and easy to use technology. Other obstacles await earlier adopters: problems of data quality, provenance, standardization, consensus building for metadata, and dealing with special scalability problems such as DR and privacy concerns. Early adopters may rely on third party specialists to pull many of the levers.

Nevertheless, some steps can be taken by SMB's to lay a foundation for the Watson Era.

• Identify the most high-payoff opportunities, then refine enterprise-specific use cases to match
• Develop canonical, standardized systems for metadata and taxonomies 
• Leverage existing standards while monitoring current work on evolving standards 
• Develop small, prototype projects using current technologies to assess where payoffs are likely to be for your organization (e.g., low cost experiments with Hadoop or similar technologies)
• Include nontraditional sources, such as email, web traffic, internal and external documents and project management artifacts
• Begin to address data quality and provenance by improving internal processes and assigning metrics (even if initially manual)
• Plan for scaling out warehouses several orders of magnitude beyond current forecasts 
• Collaborate with other groups, especially within industry-specific subcommunities 
• Be on the lookout for template-based "blueprints" that work for industry-specific needs (e.g., subscription-based businesses with periodic renewals, or importers whose margins depend greatly upon shipping costs, etc.)
• Through internal education, networking, consultants and recruitment, improve staff capabilities and awareness

Watson technologies are a force to be reckoned with. Just when they will make themselves felt in the marketplace is still guesswork, but savvy early adopters will likely seize opportunities that won't be so easy to pluck later in the adoption curve.

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Watson Trickle-Down? Will $100M More Trickle Watson Down to SMB Enterprises?

Use (Corporate Knowledge) or Lose It (To Google)

Danger Sidekick (Image from Wikipedia Commons)

When a firm decides to shutter operations, the loss of knowledge capital in the form of talent should appear somewhere in the risk assessment. While significant short term savings may be achieved by closing a division (in the case of Microsoft, perhaps to save $$$ to purchase Skype?), one side effect can be a brain drain to bonanza to well-heeled competitors. A report from CNN Money today identifies several members of the original Danger (Sidekick) team who are now working at Google's new innovation wing, "Android Hardware":

Hershenson and Brit were part of the trio that founded Danger in 2000. The third partner: Android chief Andy Rubin. The three engineers launched pioneering consumer smartphones, like the once-ubiquitous-among-celebrities T-Mobile Sidekick in 2000. 

Now all three are working for Google, perhaps with added incentive.

Following was my post to David Pogue's NY Times story announcing the closing the Cisco's Flip operation.

"DP, you've got this mostly right, though I think there is a more disturbing back story that goes beyond this one. It's the life cycle of smaller to medium sized technology firms whose founders and investors cash out by selling to a major (usually public) company. Another example that comes to mind is Microsoft's killing off the Sidekick, another neat device paired with an even better cloud service to back it up. What's gone is more than the idea -- seen in its pre-acquisition form, these firms are living, breathing entities, with expert sales and marketing groups, engineers, an idea-makers. Listen up, politicians: THIS is the real "job growth," not stringing fiber into empty office suites and hosting MS Office training classes for the unemployed. Killing off firms like Danger and Pure Digital aborts the creative offspring that their collective intelligence could manifest. A few among them will have cashed out, but most of those 550 workers will be consigned to endure a personal version of the Flip tragedy. Writ large, it's the U.S. version of capitalism shooting itself in the foot just when job growth is needed most. Markets dump capital mainly into mega-firms like Cisco, whose far-flung, unwieldy enterprises are far less efficient at converting that cash into good ideas and jobs" (April 14, 2011).

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Use (Corporate Knowledge) or Lose It (To Google)

What is a "Knowledge Based Systems Strategy Advisor"?

My LinkedIn profile recites my current job title, also the title of this blog post. Unconventional? Consider that Microsoft positions include "Evangelist." A recently advertised Unilever position title was "Innovation Architect." JP Morgan advertised for a "Global Head of Problem Management." Knowledge Based Systems (KBS) are ubiquitous, but not widely recognized as such. In the global tug of war that is now underway to decide which countries will succeed in hosting jobs nearest to the top of the value chain, KBS are the battleground. While some enterprises in this post-recession era have restarted technology initiatives, there is reason to doubt whether the underlying analysis for these projects has been fully considered. The preponderance of "execution" (developer) vs. analytical hiring is just one measure of the problem.

To see if your organization has given adequate thought to its product or service knowledge infrastructure, consider these seven questions that could be posed by a KBS Strategy Advisor: 

1. Lopsided Expertise Investments Are you expertise-heavy in product development but undernourished in customer service, market intelligence and quality metrics? Does the team think abstractly and produce extensible, reusable models? 
2. JIT Learning Will you be able to deploy Just-In-Time eLearning concurrently with your product releases? Do your product architects assume a completely self-service universe? Is your organization's idea of elearning an FAQ produced by an inexperienced, junior associate?
3. Measurement Do you have systems in place that gather and analyze critical usage information, before products are designed, during prototyping, and after release? 
4. Reuse and Longevity Are you building products or services with built-in obsolescence? Will your product's components be reconfigurable and reusable so that next generation can build upon the last? Has the design team stuck around long enough to experience products with any longevity, so as to be aware of pitfalls?
5. Repository Development Do you know what knowledge is required to design, operate, test, deploy and train your products? Are you subject to catastrophic results should key holders of that expertise defect in this era of lightweight employer/employee commitments? Stated in software terms, can you separate your ontologies and rule systems from raw code? Do you have repositories and processes in place to keep them refreshed and fully integrated with your design and service operations? 
6. Incestuous Design Practices Many enterprises design products based on insider perceptions, concepts-of-the-hour, and incestuously vetted platforms and tools. Functions, market demand and product longevity are often not well tested against the relative objectivity of outsiders from other disciplines or realms of experience. What is being done to address the risk of excessive inward focus? Do you look outside before accepting "requirements?" 
7. Knowledge Velocity How long does it take your customer service operation to get detailed, correct, usable information about new products and services? How long before you decide it's time for a new version, new release, new patch, a separate workgroup to support a specialized service or revenue opportunity? Who's even assigned responsibility for monitoring such events in the organization?

Organizations who have a KBS strategy will have a better chance of survival in this fierce brain-vs-brain global environment.  Most are busy building systems and products that will ultimately handicap them in today's milieu of short product life cycles, fickle customer allegiance, and coming IBM Watson-informed architectures.    

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What is a "Knowledge Based Systems Strategy Advisor"?

Emerging Knowledge Work: Patient Advocacy

Most baby boomers, especially those with surviving parents, were nodding solemnly if they read the NYT story, "After a Diagnosis, Someone to Help Point the Way." The story offered several anecdotes describing the assistance offered by patient advocates. The story's author, Lesley Alderman, like most other journalists, depictsed the role played by patient advocates as an extension of the U.S. health care system, perhaps in the same league as home health paraprofessionals.

This characterization is not necessarily incorrect, though patient advocates are unofficial extensions of health networks. Presumably they're generally not referred directly by providers, and have an uncertain and ill-defined status in the health services sector. Further, there's an assumption, borne out by ad copy on some health advocacy websites, that a primary purpose for patient advocacy is to ensure expeditious and fair treatment of insurance claims. This financial function is valuable, but that facet is business-motivated rather than a broad-based health care source of support. In an ideal world, advocacy supports both patients, providers and "the system" by minimizing unnecessary treatments and helping with some end-of-life decisions. When a loved one is faced by a life-threatening illness, sober reflection and efficacious navigation of the health care labyrinth are difficult.

A broader interpretation, one favored by knowledge engineers, sees the patient advocate at least in part a knowledge worker. The patient advocate's knowledge of the health care system is the role's key asset. Certainly that knowledge is a moving target requiring constant recalibration of enterprise-specific expertise in negotiation with hospitals, providers, and the sometimes confused, even stubborn patient and family. 

Photo Credit: Markus Hanser

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Emerging Knowledge Work: Patient Advocacy

Let's Call a Rule-based System a "Coupler"

Problem-Knowledge Couplers is a product of the PKC Corporation. What's a "coupler," you might well ask?
 

One answer is that a coupler is what was once called a "rule" in the pre-disillusionment era of AI. The Navy's SSC-Atlantic (SPAWAR-LANT)  added to a $29M contract with the firm to provide health screening tools for the Department of Defense (contract N65236-10-C-3100). According to "partners" information on PKC's web site:

The Department of Defense has embedded all of PKC's clinical decision support tools into AHLTA, one of the world's largest electronic medical records. AHLTA serves over 9 million DoD beneficiaries and electronically documents 640,000 outpatient encounters a week at over 470 clinics and hospitals.

Are you ready for a "clinical support decision engine" in your physician's office?  Better yet, is your physician ready to square off against a trial attorney's?

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Let's Call a Rule-based System a "Coupler"

The (Hard) Boring Work of Establishing Content Validity for eLearning

An email received today from the IEEE Computer Society brought back graduate school lessons learned long ago. Think psychometrics. The lesson concerns a concept whose terminology varies, but is summarized on Wikipedia as content validity. In an examination, do test items actually correspond with knowledge required in the target domain/ For instance, considering the heated debate surrounding teacher testing spawned by the film Waiting for Superman, do tests required for teaching certificates improve classroom performance as compared to untested, uncertified teachers. Establishing content validity is hard work, which is probably why there isn't enough of it.

Here's an excerpt from the IEEE-CS message.

The IEEE Computer Society, the world’s leading provider of technical information and services to computing professionals, needs your help in updating the “Certified Software Development Associate” (CSDA) certification to keep it current with industry trends. CSDA is an entry-level certification that complements the Certified Software Development Professional (CSDP) certificate and provides formal recognition of individuals who have successfully completed a software engineering degree or otherwise achieved a level of basic proficiency required to operate effectively in the industry. We invite you to participate in the Certified Software Development Associate job analysis study. The purpose of this study is to validate the tasks and knowledge that are important to the work performed by entry level software professionals. The results of this study will be used to revise and update the associate-level examination.

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The (Hard) Boring Work of Establishing Content Validity for eLearning

Facebook's Questions App: Knowledge Engineering?

Earlier this year, the Facebook Questions app was introduced (as a beta) by the social networking juggernaut. The application allows visitors to query the "collective knowledge of the more than 500 million people on Facebook."  Is Facebook's version of crowdsourcing intended to become a casual tool aimed, as the Facebook page suggests, to discover the best places to surf in Costa Rica, or could it become a more serious destination?  For instance, could grow into an ePinions?  While it's unlikely to become as rich or as deep as Wikipedia, this concept could be one to watch, simply because Facebook has an unrivaled scale of discourse content to draw upon. ◦

Facebook's Questions App: Knowledge Engineering?

Mobile Development: Productivity-Agnostic Opinions Abound

The U.S. has morphed into a polarized Red State - Blue State country, with a pervasive trench warfare mentality. Perhaps reflecting this wider polarization, the software developer community is both confused and deeply split on the subject of mobile device platforms. Developer rifts border on the political where Apple (iPhone), Google (Android) and Microsoft (Windows 7 Mobile) developer environments are concerned. The search for balanced reporting can be arduous and painful. Consider Galen Gruman's invective-ridden InfoWorld piece, "Windows Phone 7: Don't Bother with this Disaster." Fact and opinion are so casually intermixed that the only beneficiary is web traffic resulting from posts by believers and dissenters -- a point made by at least one commenter. On the other hand, a self-described "opinion piece" by Don Burnett at least takes the time to walk through the major IDE screens involved in mobile development so that neutral parties can reach their own conclusions about what works for their particular environment.  Bit.ly shows Gruman's piece with 2,340 links as of this writing, whereas Burnett's gets only 852.  Burnett's piece doesn't pretend to be a thorough comparison, nor does upcoming Paul Thurough's Windows Phone Secrets, but at least they take the time to present the evidence. 

Why does it matter?  Because software paradigm shifts in computing are infrequent and important.  They are opportunities in the making.  They offer a chance to absorb teachings from academia, practitioners, quality managers and consumers and do some things differently.  It's not about whether the Apple Objective C-based, Google ("App Inventor") or Microsoft platform is better.  All three were conceived a long time ago (by technology's clock).  Ideally something in some way new should emerge, or at least evolve, from previous projects, whether built by Red or Blue state companies. Something beyond flame wars and productivity-agnostic opinion pieces. ◦

Mobile Development: Productivity-Agnostic Opinions Abound