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Weak Signal® Research
Part I: Introduction
Bryan S. Coffman
January 15, 1997
Explanatory Note
In this series, I will spend a bit of time examining the scientific theory
of information, cybernetics, and some topics under the heading of complexity,
or self-organization. Then I'll apply this examination via metaphor, simile
and analogy to the human venture, or enterprise. The purpose of this application
is to stimulate thought, to cause it to shift perspective or basis, and
to make it more elastic by consequence. The theory of information and
communication deals with the transmission and recognition of electronic
signals; complexity and self-organization derive many of their conclusions
from computer simulations. Results from these fields may be instructive
in the realm of the enterprise, but I do not suggest that the principles
of the science of electricity, for example, maintain any one-to-one correspondence
with or transference to the art of growing organizations.
I do believe that the prevalent beliefs that society holds
as to the nature of the universe and reality largely govern its expression
of culture. Even though, for example, the principles of quantum mechanics
belong expressly to that atomic and sub-atomic realm, the popularization
in business of terms such as "quanta", "the uncertainty
principle", and "the dual nature of light", has led people—rightly
or wrongly—to apply these principles to their business and look for
their metaphorical representation.
It may be that these principles apply to business in more
than metaphorical ways. Take, for example, the quantum jump. In physics
it refers to the transition from one energy level to another, and the
release of a packet of energy equivalent to that of the transition. Energy
is not radiated or absorbed on a continuous scale, but discontinuously,
in these discrete packets called quanta. This governs how electrons move
between orbits in the atom and how many electrons can inhabit each orbit.
An electron can either be in one orbit or another, but it doesn’t
create new ones, or inhabit space in-between. Using this knowledge as
a filter, might we spot the same phenomenon in our organizations? Perhaps
enterprises are tuned to operate at discrete levels of performance but
not in-between. Perhaps there is not a linear correlation between investment
and performance in some cases, rather, increased performance occurs in
sharp, discontinuous spurts—you invest a lot of money into an idea
over time and all of a sudden, it "pops" into viability. Once
you wear the "quanta filter" you may see your enterprise in
a whole new way.
Or maybe not. And that’s the point, oddly enough. Creativity
is full of blind alleys and dead ends. Creativity is the elimination of
options.
So as we go forward, the reader will understand that I’m
not blindly applying science to situations where it does not belong, but
using the principles of various sciences as windows or filters through
which we may gain some illumination on the challenges facing our various
enterprises.
What is a Weak Signal and What is
the Purpose of Weak Signal Research?
In organizational dynamics, and for the purpose of these papers, a weak
signal is
- an idea or trend that will affect how we do business, what business
we do, and the environment in which we will work
- new and surprising from the signal receiver's vantage point (although
others may already perceive it) [To learn about one mind bender that
you may not be familiar with--nanotechnology--click here]
- sometimes difficult to track down amid other noise and signals
- a threat or opportunity to your organization
- often scoffed at by people who "know"
- usually has a substantial lag time before it will mature and become
mainstream
- therefore represents an opportunity to learn, grow and evolve.
Once you perceive a weak signal and understand it, a whole host of other
signals may become visible. These comprise the complete ecosystem of ideas
and trends that will support each other in the journey from dream to manifestation.
No weak signal ever rises to dominance by itself, but is accompanied by
shifts in political, economic, technological, and social thought and invention.
Weak Signal
Research refers to those organizational
traits and organic components that enable the enterprise to detect weak
signals as a matter of course, build models and stories that illustrate
the possible effects of whole sets of signals over time, and redesign
itself efficiently to take advantage of these possibilities.
There are some real kickers in this definition. First, for most organizations,
weak signal research is not a natural function. Most living systems are
interested in excluding and rooting out radical ideas that threaten to
infect them like viruses. Mutations may be an evolutionary driver, but
we work hard to keep them from happening. In order to maintain organizational
integrity and stability (otherwise known as homeostasis) the enterprise
can't possibly entertain and play 'Spoze with every new idea that appears
on the horizon. So the weak signal research function has to be approached
with discipline and creativity. And new tools must be invented to help
us entertain large numbers of ideas at once and view their interactions
as a synthesis. We can prepare ourselves for a synthesis of ideas more
easily than we can for a million different contingencies. Another skill
that's required is storytelling, which has become a lost art. Synthesis
and forecasting of weak signals can't rely on numerical analysis alone
(although the more nonlinear forms of analysis that are emerging show
some promise). It relies instead on the complexity inherent in building
a story that illustrates the interaction of characters, plot, and settings
over time. [click here for
a University of Houston overview of forecasting and click here
for a Sloan Management Review article on scenario building.]
There are a couple of broad categories of weak signals. One type identifies
those signals that threaten homeostasis or offer incremental improvements
but don't require the complete overhaul of products, processes, projects
or organization. The other type offers large leaps in productivity or
threatens catastrophic loss of capacity, and in this case, the organization
must undergo radical redesign and evolution.
A Weak Signal Research Story
In the late 1970’s I worked as a junior geologist for a gas company
in Colorado. I was assigned to investigate a persistent leak in one of
the company’s storage fields. During the summer months, we pumped
gas back into wells to store it in porous and permeable sandstone reservoirs
deep underground. In the winter we pumped it out to meet the increased
demand of that season. Some of this gas was leaking somewhere, as evidenced
by the changing pressure at various well heads around the field. As a
part of my investigation, I pulled the logs from all of the wells in the
reservoir and from neighboring wells outside our field and mapped the
paleogeography to plot the overlapping strata of ancient river beds nearly
100 million years old. After sorting out the various pumping cycles I
uncovered a faint pattern in the data. The gas was migrating in one particular
direction, following an ancient river bed. It was capped at this end by
a fault, but ancient flooding had left a trail of sand that had broken
the banks of our river bed and connected to a small pocket of sandstone
outside our property. I checked the log of a well that bored into this
pocket and discovered that it had been dry since it was drilled but within
the last couple of years it had suddenly become a producer. They were
selling our gas! A minor adjustment in our pumping cycles fixed the problem
and saved the company a tidy sum of money.
I had been on the hunt after a weak signal. The engineers
in the field had first picked up signs of it in the records of a trickle
off of well head pressures. I picked up the trail in my analysis of the
subsurface geology. After sifting through and understanding all of the
noise in the data, the pattern of the signal stood out in sharp relief
and I could address the problem with confidence. Notice the pattern here:
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Something just "feels funny" about the behavior
of a particular system. There’s something different happening
and we can’t quite pin it down. Usually this is confirmed only
by a hunch or occasionally stray pieces of data call attention to
themselves.
-
In order to uncover a full picture of the situation,
a great deal of noise must be processed. Note that the noise is frequently
eliminated only by understanding it. I had to understand what was
happening underground and also had to sort out various pumping cycles.
Once I did this analysis, the data began to sort itself into groups—into
context. In fact, I built an entire ecosystem in context so I could
see relationships between these sets of data.
-
The signal now stands out clearly against the noise
and can be mapped in relation to other factors influencing the problem.
-
Once mapped, a solution can be designed and implemented.
Numbered steps 1, 2, and 3 are called "creating the
problem," and step 4 is "solving the problem." These are
the two halves of the creative process. [Click here
to read more about our Seven Stages of the Creative Process Model.]
In the example, the problem was not "we’re losing
gas." That was the condition or situation. The true problem was,
"overpressure in the field is causing the gas to flow through a lower
permeability sandstone into an adjoining layer, from which it is being
pumped by another operator." Conditions, however vexing, have no
solution. They are not specific enough to allow a solution. They await
the application of creative vision, determined intent, and the serendipity
of insight so that they can be crafted into problems that can be solved.
Likewise, the solution to the problem was not, "stem
the loss of gas from the field." This was the desire that fueled
intent, perhaps, but it lacked insight to meaningful action. Once the
problem was formulated, a number of true solutions could be tried in the
engineering and testing phase of the creative process. Taking legal action
as a solution was untenable, and suboptimal from a geological point of
view. Instead, gas was piped out of well heads at the periphery of the
field and pumped back into well heads in the center of the field. This
kept the pressure at the boundary low enough so that the low permeability
sandstone leading to the adjacent pocket would resume its role as a barrier
to the flow. The solution was incorporated as an option to implement once
pressure at periphery well heads reached a certain level—it was built
into the operating procedures of the system. Then it was actually used
by the engineers in the field and its results evaluated.
Another Example--Innovation and Evolution
That’s a pretty simple example. We have all collected and analyzed
data in order to spot malfunctions in the system before they grow large
enough to cause real trouble. This is one use of weak signal research—to
maintain system homeostasis and stave off threats to this balance. But
there’s another whole realm of weak signal research whose purpose
is to allow systems to evolve and innovate. It’s to this realm that
we turn our attention. It’s the realm where
uncovering a weak signal before anyone else does, gives you an edge in
development and may allow you to witness the emergence of an entire ecosystem
of interlocked, collaborating ideas, inventions, and enterprises.
In a narrow sense, we’re talking about tracking trends
but more generally, we’re interested in spotting non-linear, hard
to predict ideas long before they reach mainstream recognition. And we’re
not looking for ideas or trends in isolation. It’s rather meaningless
to hunt down and map a potential trend for automobiles powered by hydrogen
fuel cells, for example. For such a trend to materialize, a whole host
of other factors—psychological, political, social, technological,
economic—must emerge. How will petrochemical companies respond to
the possibility? What about the public’s connection of hydrogen with
disasters like the explosion of the Hindenburg (unfounded in the case
of fuel cells)? What infrastructure of fueling stations, repair shops,
and trained mechanics must be in place and how might this infrastructure
emerge? Now, what are the probabilities of all of this occurring in an
interconnected way? There's no way to apply metrics and measurements to
such a question. The best way to approach such complexity is through systematic,
disciplined story telling, or scenario building. The trick in such story
telling is to suspend disbelief long enough to play 'Spoze with the idea. In other words, assume
that the ecosystem of ideas and trends actually does emerge at some future
date. Then build a complete picture of that future ecosystem and extrapolate
BACKWARDS to fill in all of evolutionary landmarks reached along the way.
In doing so, with rigor and some depth of research, you will
likely uncover true weak signals hidden in the periphery of your scenario.
These become your target. The original weak signal that you used to play
'Spoze with may only be a foil through which you can discover more surprising
possibilities.
So now we can modify our process somewhat for weak signals
that portend great innovation, growth or evolution. The steps are slightly
different than for weak signals that indicate a system is out of homeostasis.
-
Some particular idea or set of half-conceived ideas
are hanging around the periphery of your comprehension. There’s
something different happening and we can’t quite pin it down.
Usually this is confirmed only by a hunch or occasionally stray pieces
of data call attention to themselves.
-
In order to uncover a full picture of the new idea,
a great deal of noise must be processed. Note that the noise is frequently
eliminated only by understanding it. In this case, you’ve got
to understand the biases and ignorance that keep you from seeing the
new idea clearly. You’ve got to put yourself in a position to
play ‘Spoze intelligently. This means suspending biases (even
cherished ones) temporarily and doing some research to more clearly
understand the language, behavior and position of the new idea. You
can’t play ‘Spoze if you have too little data.
-
The signal now stands out clearly against the noise
and can be mapped in relation to a complete ecosystem of ideas at
some future time. This is advanced storytelling and scenario building.
-
Once mapped, one of several outcomes may be realized:
- The new idea can be discarded as a low probability occurrence.
- The new idea may have potential and the organization must be redesigned
to take advantage of it.
- The process of building the scenario uncovers a number of other
weak signals that have at least as much potential as the original
one.
- The process may lead the designers to CREATE their own new weak
signal and redesign their organization to take advantage of it.
Most weak signals that enable innovation and evolution are
not so profound as the development of an entirely new technology, or a
radically different economic system. On a daily basis we scan the environment
for clues on how to do our work much better, much faster, for greatly
reduced cost, and with results that appeal to customers in a more lasting
and satisfying way.
The most valuable weak signals don’t start, and can’t
be found in whatever industry we happen to be working in. Great ideas
that are born within an industry for use in that industry tend to mature
quickly and are shared broadly. No one can generate more than marginal
gains from them. Or, worse, they are ignored in a blindness that comes
from a survival instinct to maintain the status quo. There are many stories
about industry leaders failing to detect and implement new ideas that
threatened entire product lines and ways of doing business, or, worse
yet, just seemed outlandish and silly to someone who had been in the business
for years and was obviously an expert. The most valuable weak signals
usually come from someone working outside of the field who happens to
invent a solution in search of a problem, or a solution to someone else’s
problem. The book Megamistakes by Steven P. Schnaars is full of examples.
[click here
for a Star Tribune article on forecasting] And sometimes, before
the idea can be made useful, it must undergo several transformations,
so it’s not simply a matter of spotting a ready made solution lingering
in the bowels of some unrelated field of endeavor.
What if there were a process that could be followed to systematize
the hunt for weak signals? Perhaps there are techniques or strategies
that can be employed in the four stages that were mentioned above. That’s
our hypothesis, and we invite you to experiment along with us to see if
such strategies and methodology exist.
Part 2: Information Theory
Part 3: Sampling, Uncertainty and Phase
Shifts in Weak Signals
Part 4: Evolution and Growth of the Weak
Signal to Maturity
Part 5: A Process Model for Weak Signal
Research
Other material on Weak Signal Research on this website
 
copyright © 1997, MG Taylor Corporation.
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