In 2008, Paul D’Antilio, CEO of Future Point Systems called to see if I would be interested in  consulting with his company about visual analytics.  He had recently become the CEO and knew that we’d been successful commercializing a visual analytics product in Attenex Patterns (acquired by FTI Consulting).  As it turned out when he called I was in Palo Alto, helping my daughter Elizabeth move to Stanford University to start her post doctoral research in cognitive psychology.

We agreed to meet on a hot Bay Area Saturday morning at the Future Point offices in San Mateo, CA.  As our discussion ensued it turns out he’d had a very successful career in software product development and was part of the development team at State Street Bank that had developed the mortgage backed securities and received one of the first software patents.

As I presented the Attenex Patterns story and did a brief demo and shared how we’d used the tool in electronic discovery and patent analytics, Paul suddenly stood up and said “this is really interesting.  When we did the mortgage backed securities at State Street Bank we were essentially taking a tangible asset and making it intangible and then trading it.  What you are talking about is taking intangible assets like patents and making them tangible enough so that they can be traded.  It’s the mirror image of what I’ve spent my career working on.”

I stared at Paul for a moment as the thought of making intangible things tangible rolled around in my brain.  I jumped up and exclaimed “You have the other half of the knowledge I didn’t know I’d been looking for the last ten years.  You understand the valuing transforms back and forth between tangible and intangible assets.”

We both knew in that moment that we’d discovered something important, but we didn’t know what to do with it.  Paul realized that while it was a potentially big idea he had more urgent topics to deal with.  So I agreed to consult with him at Future Point and see what we could do with the PNNL Starlight technology.

After a few months we realized that there was not enough capital at Future Point to generate new product lines so we parted ways.  However, the notion of making the intangible tangible enough to be identified, valued, monetized and traded is ever present in my thoughts.

Over the last two hundred years, great wealth resulted from the systematic identification and monetization of new asset classes.  The financial services industry has profited from taking tangible assets like mortgages and turning them into intangible assets that can be traded.  In the music industry, David Bowie was the first artist to bundle together his future “hits” into a monetizable asset.

In the wine industry, Joe Ciatti put together a REIT to invest in winemaking properties that raised a large fund, but ultimately failed at the execution level.  In a different arena, Intellectual Ventures had raised billions of dollars to monetize patents rather than go through the long process of litigation.  At the micro level, fine wineries are having difficulty monetizing their customer assets due to the difficulty of marketing their authentic differences and their lack of better business models and processes.  Inventors face the same difficulties of matching their inventions to customers (enterprises or consumers) who could monetize their ideas.

In the electronic discovery market, no lawyers, developers or suppliers view the problem as identifying the few “assets” in the millions of documents that will prove or disprove their case.  Yet, each large scale complex matter is an exercise in systematically identifying the key document assets and then “monetizing” them by winning the case.

The central observations about large scale customer problems are:

• The difficulty of recognizing a new asset class soon enough to create a market for it
• The focus of asset developers are to create an asset rather than on how that asset can be marketed and sold
• Few industries create “brokers” to trade bundles of assets until the industry matures.

The experiences of using clustering and classifying mathematics in problems as diverse as mortgage backed securities, legal electronic discovery, patent brokering and licensing, and creating customers for life with biodynamic wineries suggests that there is a common solution to a diverse range of market problems that asset class monetization technology proposes to solve.

The following diagram captures my current thinking on Asset Class Monetization.

Asset Class Identification

At the core of the model is identifying new asset classes that are not yet recognized as being tradable and for which no “market” exists and no transparent information about the market exists.  Clues to these asset classes are the difficulty in selling the asset or placing a value on the asset.  Broad examples of difficult asset classes to value and sell are:  patents, enterprise software from new startups, and the selling of a startup for an exit opportunity.

An example is the valuation and selling process for a biodynamic winery.  Recently, a Southern Oregon Winery went through an assessment process to value their holdings after four years as a precursor to taking investment for expansion or sale.  They required four different types of assessors (property, equipment valuation, agricultural value assessment, and quality and volume of the wine inventory) and financial experts.  This assessment was time consuming (six months from start to finish), expensive, and not very accurate.

The above assessment is further complicated by trying to assess the value add (or lack thereof) of the certified biodynamic component of the property.  Is this a short term cachet or with the advent of a growing appreciation for authentic fine wine growing that represents the specificity of the place (terroir) and the accompanying slow food movement is this a long term trend?

While a little more advanced in its evolution, the patent market appears to be moving from a very difficult arena to monetize using litigation or the very expensive sale process of licensing to the attempt to create a market.  Intellectual Ventures and Ocean Tomo are at the forefront of trying to create a market, but their efforts have been primarily aimed at acquiring patent assets or creating an auction for those assets.  Little effort is spent at understanding how to value the assets and create a transparent information structure around those assets (like a Morningstar for patents).  As a result, Intellectual Ventures is having a far harder time in licensing their patents than in acquiring them.

Classification, Clustering, Segmentation and Matching

Once an asset class is identified, sense must be made of the collection of assets.  In most cases with complex assets, this process is expensive and highly dependent on experts.  With the large scale adoption of the Internet, this process is now becoming routine, mathematical, automatic and highly scalable.  Google Adwords and Adsense are great examples of both the power of the mathematics and on the ability to monetize the mathematics.  Wired Magazine had an excellent article on “Googlenomics” showing how Google monetizes content through massive mathematics.

Recent book length treatments of the processes, techniques and tools for classification, clustering, segmentation and matching are:

• Malcolm Gladwell, Tipping Point
• Winslow Farrell, How Hits Happen:  Forecasting Predictability in a Chaotic Marketplace
• Steven Levitt, Freakonomics:  A Rogue Economist Explores the Hidden Side of Everything
• John Battelle, Search:  How Google and Its Rivals Rwearote the Rules of Business and Transformed our Culture
• Ian Ayres, Super Crunchers:  Why Thinking-By-Numbers is the New Way to be Smart
• Stephen Baker,  The Numerati
• Bill Tancer, Click:  What Millions of People are Doing Online and Why it Matters
• Jeff Hawkins, On Intelligence
  • Numenta is creating a new type of computing technology modeled on the structure and operation of the neocortex. The technology is called Hierarchical Temporal Memory, or HTM, and is applicable to a broad class of problems from machine vision, to fraud detection, to semantic analysis of text. HTM is based on a theory of neocortex first described in the book On Intelligence by Numenta co-founder Jeff Hawkins, and subsequently turned into a mathematical form by Numenta co-founder Dileep George.
  • HTM technology has the potential to solve many difficult problems in machine learning, inference, and prediction. Some of the application areas Numenta is exploring with their customers include recognizing objects in images, recognizing behaviors in videos, identifying the gender of a speaker, predicting traffic patterns, doing optical character recognition on messy text, evaluating medical images, and predicting click through patterns on the web. The world is becoming awash with data of all types, whether numeric, video, text, images or audio, making it challenging for humans to sort through it and find what’s important. HTM technology offers the promise of making sense of all that data.
  • Thomas Redman, Data Driven:  Profiting from Your Most Important Business Asset

Redman describes the power of being data driven:

“I find looking at an organization through the data and information lens to be extremely powerful.  To do so, one examines the movement and management of data and information as they wind their way across the organization.  The lens reveals who touches them, how people and processes use them to add value, how they change, the politics surrounding seemingly mundane issues such as data sharing, how the data come to be fouled up, what happens when they are wrong and so forth.”

“Data and information are most valuable when they are flying from place to place.”

Ayres described how he used Google’s Adwords to come up with the book title Super Crunchers.  For a fee of $100 in Adwords he saved himself the $50,000 of consulting fees to name the book:

Connections

The value of an asset grows as there are more connections to that asset.  Whether we are talking about a product with a high sales volume, or a webpage on the Internet (Google Page Rank algorithm), the number of connections to an asset grows the value of that asset exponentially (see Metcalfe’s Law as described in Unleashing the Killer App:  Digital Strategies for Market Dominance by Larry Downes and Chunka Mui).

Daniel Andriessen in Making Sense of Intellectual Capital:  Designing a Method for the Valuation of Intangibles points out that the value of the three types of intellectual capital – human capital, structural capital and relationship capital (customers, suppliers, infuencers) – is dependent on the number of cross connections between the types of capital.  However, today this process of connecting is primarily a manual and high expertise process.

A key enterprise problem when it comes to connections and social commerce is Return on Research.  A.G. Lafley at Proctor and Gamble made a dramatic turnaround from <5% Return on Research to >50% Return on Research by creatively outsourcing innovation through a process of Open Innovation.  The process is described in The Game-Changer:  How you can Drive Revenue and Profit Growth with Innovation.  The result of this process led to several startups which include innocentive.com, yet2.com, and mynextcareer.com.

Valuation and Social Commerce

Once an asset is categorized and connections expanded, the challenge is to value and price the asset.  Once the asset is priced, then buyers and sellers must be matched.  The mechanism for providing the matching is through the “wisdom of crowds”, crowd sourcing and social commerce.

Social commerce is a subset of Electronic commerce in which the active participation of customers and their personal relationships are at the forefront. The main element is the involvement of a customer in the marketing of products being sold. e.g. recommendations and comments from customers. This happens for example when customers publish weblogs with their shopping lists.

Making the Identified Assets Tangible

I’ve been enamored with Intellectual Capital (IC) since reading several books on the topic by Leif Edvinson, Karl Sveiby, and Tom Stewart (while he was at Fortune Magazine and before he became editor of the Harvard Business Review).  The basic question these authors asked and that Leif Edvinson implemented at Skandia Insurance Companies was how to find a financial set of metrics that worked for knowledge based companies.  Traditional finances don’t take into account the difference between a company’s book value (traditional finance) and its stock price (the realm of intellectual capital).

However, the major problem as identified in the book Making Sense of Intellectual Capital:  Designing a Method for the Valuation of Intangibles by Daniel Andriessen is that valuing the IC of a company the size of Skandia takes 50 KPMG accountants approximately 3-6 months of subjective interviews and wild guesses.  Thus, the real value is completely out of date long before the accounting engagement is over.

Ijiri in his academic articles on Triple Entry Bookeeping (momentum accounting) tries to get at the same kinds of metrics but his ideas have never taken off.

All of these thoughts and the study of IC were swirling through my head while we were developing Attenex Patterns and were automatically able to do the content analytics and visual analytics to look at the digital detritus of a company in the form of their email and documents on PCs and servers.   The following slide shows a recent version of Attenex Patterns illustrating the social network view (in this case the company view by taking the information to the right of an “@” in an email address) and the semantic network view:

Once we had the semantic network, social network and timeline network views, we put together a financial transaction network view for KPMG.  In the process of doing the fraud prototype for KPMG the intangible to tangible asset thoughts really came together:

What I saw with this prototype was that we could put financial information together with other networked unstructured information – semantic networks, social networks, event networks (timelines), geolocation networks, and financial transaction networks.  Once I realized all this information could live in the same visual analytics environment and I reflected on the nature of traditional finances, I could see the way forward.

Traditional finances all rest on a simple transaction format – the debit or credit which consists of a date, time, from, to, dollar amount, and a message as to what the transaction was about.  The rest of the scaffolding of single entry, double entry, and triple entry bookkeeping evolves from millions of these transactions within a company or between companies (wire transfers).  So I asked myself, is there a functional equivalent of the debit/credit for Intellectual Capital.  As it turns out there is – the ubiquitous message we call email (or a calendar entry, or a contact entry et al in CRM systems and on and on).  These messages consist of a date, time, from, to, subject, and message.  Unlike traditional finances though, the unstructured text within the message isn’t very interpretable.  However, with a full set of interesting content analytics like we’d developed at Attenex and the appropriate multi-variable visualizations to go with them it is easy to pick out the patterns and then “automatically” calculate the Intellectual Capital value of the firm.

My assertion is that if you give me access to your email (outlook email, calendar, contacts and exchange server), your CRM system, and your traditional financial system, with the types of visual analytics described above, we can calculate in real time your Intellectual Capital values (talent capital, structural capital, relationship capital).  Because email is the distribution mechanism for most unstructured Structural Capital (business plans, performance reviews, product plans, marketing plans …) everything needed to discover, understand, and value intellectual capital is in digital form.

In the human capital area, the founder of PayPlusBenefits is trying to pitch his service to angel investors and entrepreneurs so that they can “see” the state of their startup investment at any time.  His assertion is that since the major outflow of money in an IT startup is for people, that he is already sitting on the data to understand when the startup is going to run out of money (burn rate).

So in the context of a making the intangible tangible, you now have an accounting and measurement system to actually manage Intellectual Capital which is huge step forward from traditional finances which tell you where you’ve been not where you are going.  With this IC accounting, valuing and managing capability, you now have the ability to make the intangible tangible so that it can be monetized and traded (brokered).