An Architecture of Meaningv2

Andrew C. Schenkel This brief is intended to begin a new discussion about data discovery and how a unique methodology can produce insights. This methodology reveals an architecture of meaning through which data discovery tools can be applied. Discovery has a process that begins with how unique the information is which feeds its process. Simply asking business managers to discover creates a certain amount of risk, and risk taking needs to be taken in order to innovate. But the banality of the information can retard efforts to innovate. The author notes that many of the examples and this methodology is attributed to the late Jim Williams. Jim Williams was a pioneer in market theme investing and founder of Williams Inference, which has clients such as Fidelity, Wellington Asset Management and Lazard. This brief will be continued to be updated until complete. The first draft’s release date: March 26, 2015

I n f e r e n c e P a r t n e r s , L L C 2 8 8 O l d F o r g e C r o s s i n g , D e v o n , P A 1 9 3 3 3

An Architecture of Meaning

Written by Andrew C. Schenkel © Inference Partners, LLC 288 Old Forge Crossing, Devon, PA 19333 © Inference Partners, LLC 288 Old Forge Crossing, Devon, PA 19333

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Introduction

The search for meaning in business is more urgent now than ever before in the history of

humanity. Through analyses of data sets in different settings, businesses are attempting to

determine meaning underneath surface features in information. The search begins with data

discovery. With Big Data analysis, for example, a business can discover meaningfulness of a

consumer viewing a specific product on website, which can improve sales. When using implied

meanings, competitive analysts can better interpret change in a competitor’s activities in the

context of an industry’s landscape. The context of the change can describe the implications and

consequence of the competitor’s activity—those salient features betraying its corporate

personality. Executives seek to find meaning in the wider context of human activity, as societal

shifts present a business with threats and opportunities to a strategy. Hidden meanings in

societal shifts are discovered through inferring changes in demand. Meaning has many levels

and qualities, and a variety of different applications. The implied or hidden meanings have

great value.

Hidden meanings are found through inference. When true, some call the inference an insight.

An inference is a logical assumption based on direct, indirect and circumstantial indicators.

Inference is based in facts, not supposition. An inference closes a gap in understanding, or it

sees through what hides the truth. You infer when you don’t know or cannot know exactly. It is

not a guess as it is based on facts, and there is a rudimentary, disciplined processes involved in

arriving at an inference. An inference draws a pattern of understanding, completing a picture

that is logically consistent. Some call inference an assumption, but the word assumption sheds a

negative light on an otherwise noble thought process. Assumptions are inferences that are

based on probabilistic contexts, and when people infer incorrectly, they generally assume.

When the probabilities weigh against that information environment or context is exactly the

time that the negative connotation of assumption emerges.

Two classes of inference are useful to business. The first is statistical and the second originates

from literary criticism. The use of statistical inference in data analysis is well-known.

Statisticians use Bayesian inference to describe the probability occurrence of an event or the

likelihood of an outcome. Stock price analysts apply stochastic calculus to examine the

distribution of seemingly random equity prices. Although stochastic calculus is not, strictly

speaking, inference, it arrives at close approximations in price behavior that is hidden in

Written by Andrew C. Schenkel © Inference Partners, LLC 288 Old Forge Crossing, Devon, PA 19333


randomness. A Martingale is a good example of applying probability behavior to price analysis.

Similarly, big data projects use machine learning to understand the characteristics of large

datasets. Machine learning hunts for patterns and correlations in the data, and those patterns

can be used to predict consumer behavior or see new market opportunities. Here, different

types of algorithms test large datasets, assembling the characteristics, or patterns, for the

business user’s consumption. A second less known use of inference is quite useful to business,

but its origin is akin the use of inference in literary criticism. Many pundits and executives who

don’t use a formal process when they apply inference succumb to the risk that what they assert

as truth in a given situation will be inaccurate and misleading. And this can cause problems for

data discoverers with data discovery tools. The following pages contain examples of how

executives inferred incorrectly and laid a course of action based on false assumptions or were

lacking evidence through which an inference can be made. When you infer without supporting

facts, you simple assume, which is a type of failure. When you infer in the absence of anomaly,

your conclusion will lack insight. Inference in business settings has a formal process. Insights

can be predictably made using a formal technique. Without a formal process, executives and

analysts attempt to generate insight, which will result in varying degrees of success and failure.

The inference process has been used successfully to read the subtext of anomalies and symbols

and infers change from direct evidence and reduced cues. (A cue is an indicator. So a cue that is

reduced is an indicator that is not easily or directly linked to another piece of information.)

When inference has a disciplined process, its insights can be, and even have been, the feedstock

for many investment managers, strategic planners, competitive analysts and market researchers.

Whether mathematically derived or read into, these two classes of inference share similar

principles and processes. The differences between the two classes of inference, however, define

their applications. One type of big data project described herein tries to predict whether a

consumer should be given a discount on a specific product at a specific time, and this use of

statistical inference interprets the meaningfulness of a consumer’s review of a product at a

specific point in time and then attempts to anticipate the consumer’s likelihood to purchase the

product with the discount incentive. This application of statistical inference fundamentally asks

the question: what does it mean to the business when this customer reviews this product at this

time? This is a point-specific use of inference. In other words, the specific context is

understood primarily for one specific transaction even though that transaction type may occur

several hundred times a day. With the datasets available and correlations to be discovered,

statistical inference has many applications for business. Business employs inference to predict


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specific behaviors with specific transaction types. In this sense, statistical inference is a terrific

tactical tool.

Inference, too, is the means through which strategic foreground and competitive advantage are

seen and realized. Strategic foreground is a more close reading of the present condition that

includes a sense of a market’s direction. For executives strategic foreground offers an insight

into optionality. Foregrounding is not a scenario; it is the implication of a change in markets,

consumers and the like to a business’ interests. Change is not revolutionary; it’s evolutionary

simply because people’s perceptions shut out change instead of diffusing one’s attention to

discover it. By definition, change is always betrayed by anomaly. Tracking anomalies, therefore,

leads to seeing change unfolding, evolving. It is the only way to rid one’s self of change

blindness. Some executives interviewed during this research say now in 2015 that they had seen

the potential for the market implode in 2007. One portfolio manager confided in hindsight, “It

was like a runaway train that everyone saw coming, and no one could get out of the way of.” An

“elephant in the room” explanation of conscious inactivity doesn’t mean that any decision maker

is any more prepared for that or any another black swan event, however much the term black

swan is in vogue. What was fundamentally missing from those who “saw it” but didn’t

“recognize it” for what it was.

According to a source, a president of a Fortune 500 company learned of the severity of

impending credit crisis and took strategic action about it in October 2008, which is late in the

cycle for proactive business planning. Because the company’s executives were responding to

and not proactively planning for a crisis, its plans cut severely, trimming its workforce by 11%

and budgets by a similar amount. A diffusion or awareness to the evolution of change in

consumers use of their homes as a piggy bank and not as a store of value would have clued this

executive in to the impending crisis.

While some hedge funds made billions, over 1,000 hedge funds went out of business and lost an

estimated $460 billion in 2007-2008. In sympathy with hedge funds nearly all mutual funds

lost an average of 35% during that time period. The savvy JP Morgan and Goldman Sachs

admitted they “saw” the crisis as early as 2006. Our best and brightest business leaders were

caught off guard by an event that apparently everyone could see but not get out of the way of.

Would it surprise you that the beginnings of the crisis were extracted from sources and its

meaning inferred as early as 2001?



The following passages were excerpted from two briefs on debt written in 2001. The briefs

articulate the observations and their context, drawing a telling, insightful picture. The briefs are

explorations into hidden truths regarding consumers and their use of debt. They are quoted at

length to offer context and proper illustration. Jim Williams wrote passages below and briefed

his clients as early as December 2001 to the debt bubble in the housing markets. Italicized

words below are anomalies.

“The most telling item today is the increased debt in the home. In 1990, mortgages amounted to about 30 percent of the value of the house. That percentage is now 50 percent. The worsening ratio has come even as housing prices have climbed. A recent Federal Reserve report shows that mortgage borrowing was the biggest reason for increasing debt burdens…Consumers have been taking money out of their houses, whether for home improvements, vacation, the stock market or repayment of other debt.” “The 10-year bull market in housing illustrates a big cultural shift. Home-owners no longer look forward to mortgage burning parties, instead they treat their residences like piggy banks. Unlike conventional piggy banks, however, the money in the home is debt. The ratio of mortgage debt service to total disposable income climbed to 6.46% in the fourth quarter of 2000, surpassing a record set in 1991 in the depth of the last recession. At the center of the debt-filled housing bubble are Fannie Mae and Freddie Mac. These two government-chartered companies do not actually issue mortgages. Instead, they buy mortgages from lenders and repackage them into tradable securities. In doing so, they play a major role in the mortgage market. The Fed's Alan Greenspan, has expressed concern that Freddie and Fannie, by using government subsidies to expand the housing market, create distortions. The Congressional Budget Office estimates that the two companies last year enjoyed subsidies totaling $10.6 billion. Thus, these companies encourage more and riskier lending than a completely free market would allow, over time creating a bubble. With home-equity lines of credit tied to a low prime rate, more consumers are using their houses as a way to pay off credit card debt, undertake home improvements, send children to college or buy big-ticket items. This year, by refinancing, Americans are expected to take $74.4 billion out of their homes, more than double last year's $30.2 billion. Borrowing on the home is not restricted to the U.S. In England, household borrowing has risen to more than 70 percent of the national income compared to 50percent a decade ago. Since 1997, mortgage debt in England has risen 34 percent.


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In an aggressive attempt to keep the housing market afloat, at a time of economic distress, mortgage lenders are ratcheting up the use of "loan modification" programs. For example: Wells Fargo Home Mortgage modified a delinquent loan. The lender tacked the $14,000 in missed payments onto the top of an existing $160,000 mortgage. In this manner, a delinquent loan became good. Designed to keep home owners from defaulting on their loans, programs of modified loans help the mortgage industry-including Fannie Mae and Freddy Mac by postponing real estate write-offs. This practice is masking a developing problem. At the end of the second quarter, 4.6 percent of all residential mortgages were delinquent-a rising trend. Delinquencies on government insured mortgages are far worse. Nearly 11 percent of borrowers who got loans through the Federal Housing Administration, mainly first-time buyers, were delinquent. Modified loans help the banks basically by moving a delinquent item to a performing item. But, even with this maneuver, delinquencies are escalating. In a fringe area of the housing market are trailer home owners. During the 1990s annual sales of manufactured homes more than doubled. In that era, loans were made to borrowers who had little chance of paying back. Today, tens of thousands of those people have already defaulted and have been evicted. Conseco alone has repossessed 25,000 homes so far this year. There is a disproportionately large debt market compared to the real economy. Total U.S. market debt is about 270 percent of GDP compared to a 30-year average of 145 percent of GDP. To put this in perspective, in 1929 total market debt reached a peak of 160 percent of GDP. Looked at from another angle, the biggest problem of the current U.S. economy is the sky high level of consumer debt. Consumers are paying out over 14 percent of disposable income in debt services. This is unsustainable. At the center of this quagmire, is the home debt. Even if America were to have a v-shape recovery [in the 2001 recession], starting immediately, there would probably be another year's worth of deteriorating loans, as austerity measures taken by large companies have effects on the rest of the economy.”

The anomalies in italics are the particularly telling pieces of information in the passages above.

Recognized for what they are, anomalies are of tremendous value to executives as they drive at

what Qlik’s Vice President of Innovation Donald Farmer introduces us to “cultural intelligence”.

Farmer stresses observing behavior and change, and in a depiction of cultural intelligence he

showed a baby holding a tablet. What Farmer misses is how anomalous a baby’s interaction

with such advanced technology is, for it is embedded in the anomaly that yields the business

value of the change. Anomalies are found in many different contexts and settings. Since they



are a subtext of understanding they are objects that like allegory can be mixed together. When

woven together as they can be, the anomalies, and not the narrator, tell the story. This listening

to the story, instead of telling the story, is important for data discovery. Presenting the

anomalies this way removes the ego from reading them. Ego and a prior knowledge can

diminish the value of any anomaly or inference. This is an important point that will be

discussed at length later. The anomalies and the inference (insight) captured in the passages

above require several steps in a disciplined process. The anomalies point to a precarious

position the consumer has because of his debt.

The briefs have collected observations on debt and uncover some hidden truths when they are

woven together as they were when written during 2001 recession. What we can learn from this

is at least two fold: 1.) anomalous observation is at the center of inference and 2.) comingling

anomalous observations generates a new pattern of understanding. Simply put, an anomalous

observation in a context creates the opportunity for an inference while a collection of anomalies

generates a pattern through which a new understanding is achieved. For a business to

successfully utilize inference, its information must be in the present. By more clearly reading

the change in the present, competitive or strategic analysts study the significance and

implications of change. These executives seek to discover the implied meanings in the macro

patterns of human activity and those of a competitor. Between the two classes of inference,

similar principles of data collection and interpretation apply when constructing meaning. This

essay examines implied meanings in business contexts to point out the commonalities in these

processes of discovery. Through examining the commonalities, we can establish an architecture

through which meaning is discovered or constructed in business contexts.

What is Implied or Unintended Meaning?

As I had mentioned in a recent article in Competing.com, there are two type of meaning: overt

and implied. Overt meaning is the direct statement the text or data has, and implied meanings

are what lies beneath the text. Leading a business requires executives to think ahead to envision

the future and to see the foreground of that future. An implied meaning is a message in the

subtext of a given set of information or an assumed message within a passage. Implied meaning

is often unintended, which can be realized on a conscious or subconscious level.

One of the greatest car racers in the world was Juan Manuel Fangio, an Argentinean who had

not only a lead foot but also a skill for skirting trouble. These two qualities made him a top


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competitor. While in the leading the race in Monte Carlo, Fangio sped away from a multi-car

pile up behind him, having not noticed the accident at the Tabac curve, according to his

interview after the race. Approaching Tabac on his next lap, Fangio, still in the lead, saw the

spectators standing up and all staring away from him. Intuitively, he broke hard and avoided

the dangerous pile up. How did he know to do that? Fangio foresaw danger in the fans’ heads

turned away from him and missed the disaster that befell his colleagues through intuition, the

subconscious recognition that something odd or strange is happening. Intuition and inference

share common roots in oddities. While intuition is subconscious, inference is the conscious

activity of recognizing the unintended message. Intuition and inference are sometimes

confused. Whereas intuition is a gut feeling, inference is a conscious process and follows steps

to maintain discipline. Sometimes called the sixth sense, intuition can show up not only as gut

feel but also back pain. Famous investor George Soros, according to his son, gets debilitating

back pain whenever he takes a financial position that disagrees with his sixth sense. Apparently,

his sixth sense has a very good track record.

Information is either intended or unintended, meant to be noticed or meant to be discovered.

Several years ago in Hyannis Port, Massachusetts, a bridge inspector completed his inspections

of the Hyannis Bridge, writing the annual report as was required of his position. Over the

course of those several years, the Audubon Society had recorded the rapid decrease of birds that

had nested underneath the bridge. Its study had shown that the population of birds nesting

under the bridge had dropped from 10,000 in one year to 1,000 in the next and finally down to 6

in the third year. Six months later the bridge collapsed. Two messages about the health of the

bridge were sent: the inspector’s report and the dramatic decrease in birds nesting under the

bridge. The bridge inspector’s report, although a lie, was intended. The actual condition of the

bridge was revealed by what the birds’ absence was implied about the condition of the bridge.

The lack of nesting birds indicated the deterioration in the condition of the bridge. One piece of

information indicated, or was linked to, the condition another piece of information. The lack of

nesting birds had an indirect relationship to the deterioration of the bridge, as the lack of

nesting birds could have been attributed to another reason. Direct relationships, where there is

a tight linkage between a piece of information and its consequence, occur as well.

An example occurred in the market for sugar. In the early seventies, an executive of a company

that deals in sugar had received research reports from a team of top consultants predicting the



price of sugar would rise over the next 6-12 months. The consulting firm’s analysis was base

upon demographic studies and trends that all concluded that people were increasing their

consumption of sugar. At the same time, the executive read a newspaper article with a large

bold pictures of swimming pools in Hawaii, a major sugar-producing state, that were filled with

sugar. Storing sugar, which dissolves in rain, in a swimming pool is certainly an anomaly. Its

unintended message was clear: sugar producers had so much sugar they couldn’t store it

properly. The executive sold his company’s sugar holdings. Subsequently, the price of sugar

plummeted, falling to one quarter of its price. Unintended messages produce inferences.

Inference is a process of discovery; to infer means to complete a picture. Inference, then, is the

art of reading the unintended message or completing a picture.

Completing a picture of the world not only coexists with oddities like sugar in a swimming pool

but also with understanding things that are new. During the Vietnam War, a newspaper

reported that the US army had invested in a remedy for malaria. The article said that the new

malaria drug hadn’t worked in tests in the field. What would happen to the price of quinine, the

original treatment for malaria, when this news was fully realized? At the time, according to a

source, Schwepps, a leading producer of quinine at the time, priced it at $.38 per kilo. Once the

US army’s sourcing department discovered its new malaria cure didn’t work, the price shot up to

$12 per kilo. The mind and information travel at the speed of light, but the physical world

doesn’t. The disparities offer ripe opportunities to harvest anomalies.

What if, for a moment, we remove the relationship a step or two further away the linkage, so on

the surface there appears to be no contiguity between one piece of information and how it

indicates another? Let’s pretend its 1953 and you are the U.S. intelligence attaché to the former

Soviet Union. Your duty is to collect on all kinds of information, but you need the information

that is particularly revealing about Soviet weapons, if such information can be obtained, as it is

the height of the Cold War and distrust between the two superpowers is high. So during your

readings of Soviet media you come across a story in the news about a soccer team from a small

city in Siberia that beating all other soccer teams from all of the major cities. You are provided

with no other information. What do you make of that? Does it contain any hidden meaning?

What hidden meaning does it have? What is the process to find it?

The first thing you do is separate what’s known from what’s given. The soccer scores are given;

it is new to the context of the information environment. An information environment is the


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basis through which you make comparisons of data. Comparing the data tells you how strange

or anomalous the information truly is. My mentor Jim Williams used to say, “The stranger the

better.” So we notice the soccer scores, and the scores themselves are not anomalous, but the

team’s outscoring its opponents is anomalous. First of all, the team’s outscoring larger

opponents goes against one convention. In an environment where all other conditions are

normal, our expectation is that larger populations will field better teams simply because the

populations can generate a larger number of healthy athletes to recruit from. The second step is

to better understand what was known at the time about the former Soviet Union. The Soviet

Union was a command economy; workers were told where to go and what to make. If this is the

lens through which you view the small city in Siberia that has fielded a terrific soccer team, you

can begin to see something different. At this point, you can ask this question: Is this small city

really a small city? With the information you have, you can infer that the small city may be

much larger than is reported. Only then can you ask this question: Why would the Soviets

allocate resources to such a remote area? To hide them is logical the answer. Confirmations of

the inference were found in photographic evidence recovered from U2 missions over the Soviet

Union. The soccer score had an unintended meaning: it revealed the existence of a heavy water

facility necessary for the manufacture of nuclear weapons. Each of the steps in the process

above is the method to achieve meaning; it is an architecture for uncovering hidden meaning.

What do unintended meanings look like in a business context? A number of years ago, an

individual that had some responsibility for a loan to the department store was informed by his

wife that no one was in the store when she went shopping. Instead of reacting to this

environmental stimulus and inferring a business condition, he rechecked the department store’s

financial statements, concentrating on the numbers and concepts. His opinion, derived from

the numbers from accounting statements, was that all was well with the department store. As

the loan analyst was reviewing his research over the following weeks, the department store filed

for bankruptcy.

It is important to observe the importance of observation. In the fall of 2014, the price of oil

collapsed. It’s easy to infer how the Russian Ruble might fall in sympathy with the price of oil

since much of the Russian economy is predicated on its oil industry. But how sensitive was the

Russian Ruble to the price of oil? Since its establishment, the large Russian oil conglomerate

Rosneft has utilized debt as a means to acquire companies because its equity float was very

small proportion to its total capitalization. Buying companies made business sense for Rosneft



when oil prices were high as the cash flow from its existing production. But it also presented

hidden risks. In the mid-2000s, the company bought so many refineries and oil companies its

debt to $54 billion, which effectively locked in the prevailing price of the acquired assets. (At the

prevailing exchange rate of 40 Rubles to 1 USD, the debt outstanding during the summer of

2014 was 2.1 trillion Rubles.) The devaluation of the Ruble in the fall of 2014 caught many

analysts off guard. The observation of the anomaly (i.e. Rosneft’s debt outstanding) was not well

known; if it was, the linkages would have been more apparent and the consequential surprise

would have been muted.

[Insert Business examples.]

Observations can come from a variety of sources, but they all must be factual in nature. The best

observations are those that show the magnitude and severity of change. Opinion, especially

polls, needs to be removed from the analyst’s lexicon of “fact”. A simple test illustrates the

reason why what people say is not the best measure of fact. Most people consider themselves

ethical, law-abiding citizens. Yet of all those you ask whether they are will also admit that they

were driving above the speed limit the last time on the highway. However, you can use inference

to reveal conditions implied in statements made by people. A reading from Chevron’s corporate

history is a good example of how an insight is generated.

Nearly all company profiles of Chevron describe the 1984 merger between its two predecessor

companies: Standard Oil of Southern California (SoCal) and Gulf. Despite its size, Gulf at the

time was struggling to maintain profitability, and corporate raiders wanted to capitalize on this

weakness. The company experienced strong results during the early 1980s, with major

discoveries and large acquisitions of offshore acreage in the U.S. Gulf of Mexico, a $1 billion

modernization of its Pascagoula Refinery and the introduction of new Chevron Supreme

Unleaded Gasoline with Techroline. And yet the larger picture was unsettling, according to a

report, prompting the company to conclude that its normal business strategies simply wouldn't

be enough. A new context forced Gulf to reconsider its strategic options. The then Chairman of

the Board George Kelleher expressed this view when he said, "Over the next decade, the oil

business will become increasingly competitive." He added, "Flexibility in swiftly adapting to

change will be mandatory for success - and possibly survival." The change forcing Mr. Kelleher

to reconsider his strategy came from outside the oil industry. Effective management began to be

more easily scrutinized by Wall Street, who could easily compare one company against another


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through newly financial statement data services introduced by Standard and Poors in the late

1960’s. At the time, company leaders had to more closely pay attention to two currencies and

not just one. Attention to revenue growth gave way to attention to the details of earnings in per

share of equity through the careful refinement of cost structures in ever more intricate supply

chains. So when corporate raiders were posed to takeover Gulf, then CEO George Kelleher

(including the corporate history on Chevron's website), committed to merge his company with

SoCal “in a matter of hours,” citing the corporate raiders who wanted to tear Gulf to pieces and

sell it piecemeal for a quick profit. Chevron’s website says, “With these strengths [of the

combined companies] came a companywide enthusiasm to fulfill a corporate mission of being

‘better than the best.’” “Better than the best” is a particularly telling clue in this context, for it

implies pervasive fear within Chevron’s boardroom at the time and how corporate messaging

tried to mask that fear. How does this imply fear? Suddenly the size of a company was not a

protection against destruction. The struggle with profitability is leverage through which you

gain insight into Chevron’s boardroom. The fear in the Chevron boardroom drove decisions and

inspired programs. The board’s focus on profitability even appeared to have launched the career

of a young profitability analyst, John Watson, who became instrumental to Chevron during this

crucial time. The implied condition at Chevron and its subsequent corporate actions and

programs, when taken together, indicate a picture of the corporate personality at Chevron. John

Watson is now CEO of the company. Under his leadership, Chevron’s identity is not a wildcatter

but rather a prudent steward of its businesses. The merger occurred while Mr. Watson was in

the first few months of his career at Chevron as a pricing analyst. Through leveraging the text

and subtext do we gain a more holistic understanding of Chevron. Implied meanings of a

company’s “personality” inform strategy and optionality. With this level of understanding, a

strategist can better anticipate the likelihood of a competitor’s next move.

As a corporate personality offers insight into the likelihood of a competitor’s behavior, strategic

foreground is seen through inferring change and reading the unintended message in the subtext

of the social subconscious.

[Next: Symbols and archetypes use in business.]

An Architecture of Meaningv2

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