Managing
Organizational Ignorance
Knowledge Directions , Volume 1, Summer, 1999, pp. 36-49
Michael H. Zack
214 Hayden Hall
(617) 373-4734
m.zack@neu.edu
ÓMichael H. Zack, April, 1999
Ignorance is always
correctable. But what shall we do if we take ignorance to be knowledge?
—Neil
Postman1
The value of managing organizational
knowledge has been widely discussed. I propose that it is more important for organizations
to manage their ignorance. Knowledge management strives to locate, map,
collect, share, and exploit what the organization knows. Ignorance
management, on the other hand, recognizes that it is never possible to know
everything, or even a lot of things, well. Acting from an assumption that the
organization knows enough may represent hubris at best and bad management at
worst.
Business organizations, however, have
historically operated as though their competitive environments were known,
describable, and relatively stable and predictable. Rigid planning processes,
routine operating procedures, hierarchical structures and vertical
command-and-control communication patterns reflect this belief. Even
organizations that have adopted more flexible and responsive network forms to
better deal with unpredictable environments still assume that the world is
understandable and understood consistently and coherently across the
organization. Those assumptions are not valid when the environment and the
challenges it poses are vague, inconsistent or even unknowable.
Rather than recognizing the diversity of
knowledge, perspectives, values and interests that may actually exist within a
firm, today’s approaches to knowledge management often assume that the shared understanding
needed for effectively communicating knowledge exists. They focus on locating
and sharing good answers rather than collectively formulating good questions.
They do not address how to manage knowledge when the firm doesn't understand
its problems, know what questions to ask, or even agree on what it knows.
Organizations need knowledge management guidelines that help them recognize and
respond to a variety of "knowledge problems" that have to do with
what they don’t know or don’t understand.
To help give some shape to the realm of
organization ignorance, I define four specific knowledge problems and show how
they form an integrated picture of an organization’s state of knowing. I use
that framework to discuss the role of information technology in managing
knowledge and ignorance.
The strategic challenges faced by
organizations can be framed in knowledge-based terms such as uncertainty or
complexity. For example, the competitive landscape may be considered highly
uncertain because the organization doesn’t know enough to predict how
competitors will act. It may be seen as complex because the problems it poses
cannot be addressed by known and familiar solutions. Uncertainty, complexity
and related terms such as dynamism, volatility and ambiguity can serve as
powerful indicators of an organization's knowledge or ignorance. These ways of
describing what is known or not known have been inconsistently and imprecisely
defined in the popular and academic management literature, however, so there
are few coherent prescriptions on how to manage them. Consequently,
organizations often end up implementing knowledge management solutions that may
not be appropriate to their particular knowledge problems. What is needed is a
coherent framework for describing and managing organizational ignorance.
What do I mean by "organizational
ignorance"? I define it in terms of four unique knowledge-processing
problems (
Figure 1):
1. Uncertainty: not having enough information;
2. Complexity: having to process more information than you can
manage or understand;
3. Ambiguity: not having a conceptual framework for interpreting
information;
4. Equivocality: having several competing or contradictory conceptual
frameworks.
Each problem describes a particular form of
organizational ignorance, calling for a particular knowledge-processing
capability. Each in some way also represents a fundamental organizational or
strategic management problem. Taken together they define the range of knowledge
processing capabilities an organization must have to manage its ignorance
effectively. These four knowledge problems can be categorized along two axes:
1) the nature of the knowledge being processed, and 2) whether the solution is
to acquire more knowledge or to place restrictions on what you have.
Knowledge can be divided between factual
information and the richer, more complex knowledge that supports interpreting
information and using it to generate insights, build inferences and
explanations, and pass judgment about how and why things work2.
Information processing is associated with managing situations within some
agreed-upon and meaningful interpretive context, using analysis, manipulation
and communication of facts. Information-processing problems of complexity and
uncertainty are convergent problems that "deal with distinct,
precise, quantifiable, logical ideas that are amenable to empirical investigation.
Convergent problems are solvable problems; as they are studied more
rigorously and precisely, answers tend to converge into a single accepted
solution."3
Knowledge processing, on the other hand, is
associated with resolving or managing situations that require interpreting,
creating, sharing, and negotiating meaning. The knowledge-processing problems
of ambiguity and equivocality are divergent problems. They "are not
easily quantifiable or verifiable and seem not to have a single solution. The
more rigorously and precisely they are studied, the more the solutions tend to
diverge, or become contradictory and opposite."4
These four problems may also be distinguished
by the notion of restrictive vs. acquisitive processing. Complexity and
equivocality require restriction to create structure and meaning. Solving
problems of complexity requires the restriction of factual information; dealing
with equivocality calls for restriction of diverse viewpoints or
interpretations. In contrast, uncertainty requires the acquisition (or
estimation) of information or facts, while ambiguity requires the acquisition
of knowledge or interpretive frames. Therefore restrictive processing is
generally internally focused (work with or make sense of the information and
knowledge you already have), while acquisitive processing requires a search for
more information or knowledge.
With this basic framework in place, we can
describe the four problems in more detail.
Complexity
Complexity can be defined as a large number
of intricately related elements. Complex situations are not necessarily vague
or unpredictable. Rather, the variety of elements and relationships that must
be considered simultaneously is just too large to process easily5. So, for example, complex problems have many
potential and interrelated variables, solutions and methods. Complex tasks are
made up of many interrelated steps and factors. Complex organizations have
diverse members, functions, processes, organizational units, and reporting
relationships.
What constitutes manageable complexity varies
by person and by organization. The number of different elements and
relationships that can be considered simultaneously depends on what one knows.
Novices typically must deal with each element of a problem or task and their
interrelationships one by one. Experts, on the other hand, can instantly
recognize intricate but familiar patterns, perceiving the situation as a
coherent and simpler whole6. Likewise, what
is complex for one organization may be simple for another that has greater
experience or expertise. Knowledge stored as intricate but familiar procedures
triggered by recognizable events provides groups and organizations the ability
to perform complex processes7. Bringing the
appropriate level and variety of knowledge and expertise to bear on a situation
helps an organization to manage its complexity.
In highly complex situations, where no one
individual or group can hold all of the required knowledge, complexity must be
reduced by breaking things into simpler parts. This notion is reflected in
division of labor, market segmentation, and pre-manufactured subassemblies.
This approach, however, requires integrating the decomposed elements, adding
management and coordination overhead. So while knowledge offers a solution to
complexity, decomposition merely accommodates it. Organizations facing
complexity must have the capability to locate, develop and bring appropriate
knowledge, expertise, and skills to bear on those issues. If not, they must
restructure their problems, roles and routines to simplify those problems or
render them more familiar. Because it increases the ability to manage greater
complexity, superior knowledge can provide a competitive advantage. First, the
organization can solve more complex, difficult and higher value-added problems
faster and more efficiently than its competitors. Additionally, the more
complex a set of resources and capabilities an organization can manage
effectively, the more difficult it is for less knowledgeable competitors to
understand and imitate those capabilities.8
Uncertainty
Uncertainty comes from not having enough information to describe a current state or to predict future states, preferred outcomes, or the actions needed to achieve them.9
Organizations often
experience uncertainty as the lack of enough information to make a decision or
the inability to predict events upon which some decision depends. Uncertainty
does not imply complex or vague situations or relationships; it can exist even
when the range of possibilities is small, simple and well defined—for example,
whether or not a roll of the dice will come up seven.
There are degrees of uncertainty. Current and
future states can range from being completely determined (complete certainty),
to being characterized by known probabilities (risk), by probabilities
estimated with some greater or lesser amount of confidence (subjective
uncertainty), by unknown probabilities (traditional uncertainty), or undefined
(complete uncertainty).10 In all cases,
however, the context of the uncertainty is assumed to be well-defined
and meaningful. Even complete uncertainty does not imply ambiguity or lack of
understanding of the situation.
Uncertainty can be managed by reducing it or by
increasing the organization's ability to tolerate it. Uncertainty can be
reduced by
• Acquiring additional information about
something;
• Acquiring, developing or improving the
knowledge and ability to predict, infer or estimate sufficiently well using
incomplete information.
• Using existing situational knowledge to
predict, infer, estimate, or assume facts in place of missing information, with
some resulting level of confidence and reliability;
• developing an ability to respond quickly
and flexibly to unanticipated events, for example by using programmable
machining centers or just-in-time inventory processes;
• Creating buffers such as safety stock or
temporary employees.
Some consider managing uncertainty to be the essence of strategy.11 Reducing uncertainty helps organizations make better decisions and take more appropriate actions than competitors can. Additionally, recognizing and dealing with environmental uncertainty can improve the long term value of the firm. Strategic events such as the emergence of a market, evolution of a technology, or the distribution of information among competitors can all be highly unpredictable. Because not all organizations have the same capabilities, these events give organizations different degrees of opportunity to capitalize on and to preserve those newfound advantages. Thus firms can position themselves to capitalize on environmental uncertainty by "betting" on lots of choices. According to John Browne, CEO of British Petroleum,
Given the uncertainty in the world, strategy cannot be about gambling on one possible outcome five or ten years down the road… In my view, strategy is about buying the right options that will give us a shot at competing in the future—that will give us the right to play if we decide we want to when it becomes clearer what the game is about.12
A set of alternative predictions about the
state of the future in the form of business opportunities is proposed, funded
and acted on. As more information is obtained (or eventually as the future
reveals itself), it becomes possible to narrow those opportunities to a small
number with increasing certainty and direct additional investment funds
accordingly.
Ambiguity
Ambiguity means the inability to interpret or
to make sense of something.
13 Regardless of the amount of information available
about them, situations or events are often neither immediately clear nor
understandable. Events are perceived as so new and unfamiliar that one cannot
even make a vague guess about what is important or about what may happen. If
uncertainty represents not having answers, and complexity represents difficulty
in finding them, then ambiguity represents not even being able to formulate the
right questions. No framework for interpreting or applying potential answers is
available; the ability to know what clarifying questions to ask is lacking.
Ambiguity is resolved either by acquiring or
creating explanatory knowledge, by reinterpreting a situation to be more
meaningful, or by having an interpretation externally imposed by others.
Ambiguity cannot be resolved by gathering more information. It typically
requires repetitive cycles of interpretation, explanation and collective agreement.
Hypotheses are iteratively created and discussed until some plausible
explanation emerges. Rich, interactive face-to-face conversation among a
socially familiar and well-connected yet intellectually diverse set of
individuals is the key organizational activity for reducing ambiguity. British
Petroleum, for example, broke up its huge organization into 90 autonomous
business units to encourage within unit interaction, and created peer groups
among units around common problems to encourage cross-unit interaction.14 These interactions foster the growth of
expertise and advice networks which can be called on when needed.
The ability to make sense of the competitive
environment earlier and better than competitors is an important advantage; it
underlies better decision-making and better awareness of the kinds of strategic
problems the organization may face. Additionally, an organization’s decisions
and activities, if perceived ambiguously by competitors because they know and
understand less, can provide a key source of competitive advantage by making it
difficult for competitors to understand what the organization is doing and
imitate it.15
Equivocality
Equivocality refers to multiple
interpretations of the same thing. 16 Taken
singly, each interpretation is unambiguous, but they differ from each other and
may be mutually exclusive or in conflict. Equivocality also describes
situations where there is agreement on a set of descriptive criteria (say,
desirable market/ undesirable market) but disagreement either on their
boundaries (for instance, the point at which markets go from being desirable to
undesirable) or on their application to a particular situation (whether a particular
market is desirable or undesirable). Managing equivocality requires
coordinating meaning among members of an organization, and is an essential part
of organizing. Equivocality arises because everyone’s experiences are unique;
individuals and communities develop their own sets of values and beliefs and
tend to interpret events differently. Equivocality also may result from
unreliable or conflicting information sources, noisy communication channels,
differing or ambiguous goals and preferences, vague roles and responsibilities,
or disparate political interests.
<Unless equivocality is managed,
interpretations tend to diverge over time. For example, in cases where tacitly
held process knowledge cannot be clearly articulated or unambiguously communicated
to another person, multiple interpretations of how something should be done
emerge, resulting in no single best approach to a process. Today’s popular
movement towards sharing best practices in organizations therefore may be
constrained by the ability to meaningfully articulate those practices.
Like ambiguity, equivocality requires cycles
of interpretation, interactive discussion and negotiation, but to converge on a
definition of reality rather than create one. The goal is to achieve intellectual
consensus rather than to leverage diversity. There is a danger, though, that
overly precise or coherent policies, rules and procedures for coordinating or
imposing interpretation may misrepresent the contradiction, confusion, or
diversity of views inherent in a situation. In fact, sustaining equivocality
may be useful for avoiding premature closure, maintaining commitment, and
addressing conflicting goals.
The Role of Information Technology
Given the prominent role that information
technology is assuming in providing competitive advantage generally and in
managing knowledge specifically17, the
knowledge-problems framework can be used to identify areas where that
technology may make its most useful contribution. The key distinction is
between problems oriented towards information—complexity and uncertainty—and
those oriented towards knowledge—ambiguity and equivocality.
All information technology can be viewed as a
medium for transmitting or exchanging information. Effective communication
requires matching the relative richness and interactivity of the medium to the
knowledge problem at hand. Communicating and resolving ambiguity or
equivocality requires media and languages that allow for rich and varied
expression in an interactive context, for example, face-to-face conversation
rather than text-based documents. Less expressive and more structured media can
communicate effectively about well-defined situations, but overly precise
media—a numerical spreadsheet, for example—might prematurely impose false
clarity on equivocal or ambiguous events. Overly rich communication, on the
other hand, may introduce unnecessary ambiguity or equivocality.18
Information technology, then, can be used most
effectively for managing uncertainty and complexity, where information is more
factual, a high degree of interaction is not required, or the communicators
share an understanding of the situation.19
Appropriate tools include decision support systems and expert systems able to
process large numbers of facts, variables and relationships; database
management systems with large-capacity information storage, retrieval and
manipulation capabilities; online document repositories; and computer-mediated
communication systems such as e-mail and online discussions that support rapid
and flexible information search, routing, and communication. Table 1
identifies ways that information technology may be used to manage complexity
and uncertainty.
Ambiguity and equivocality are best managed
by frequent face-to-face communication, and reliance on a flexible and
responsive network of personal contacts to serve as a source of knowledge and
expertise. The assumptions typically embedded into particular applications of
information technology in the form of labels, definitions, procedures, and
causal relationships often impose false clarity on ambiguous or equivocal
situations. They may lead to erroneous "solutions" that create
greater problems later. As emerging multimedia technologies offer greater
richness and interactivity, they may prove useful in moderately ambiguous or
equivocal situations. They still fall short of face-to-face interaction today,
however. For example, desktop videoconferencing is emerging as a potential
complement to spontaneous face-to-face conversation, but the speed and quality
of transmission available within most organizations today is not yet sufficient
to replicate that level of richness and interactivity. Information technology
can also help to locate others with whom one might need to hold a conversation.
It can be used to catalog the experience and expertise of organizational
members, enabling easier search for the knowledge needed to make sense of
something. It also enables individuals to coordinate the logistics of
face-to-face meetings. Computer-mediated communication (e-mail or
computer-conferences, for instance) can help to maintain continuity and
connection between conversations, especially for those in different locations.
Regardless of how information technology is
applied to resolving ambiguity and equivocality, organizations must provide
ample opportunity for conversation, personal interaction and shared experience
to build the social relations and provide the communication mechanisms that
allow deep knowledge to be exchanged and developed.20
An Example
To see how the framework applies to a real
work situations and how the four problems relate to one another, consider the
newsroom of a daily newspaper.21 When an
event occurs, reporters or wire editors must attempt to make sense of what
happened and to determine if it is "news." This may be
straightforward when they are familiar with the situation, but difficult if
they are not. I was observing the newsroom of a major daily newspaper when the
student uprising in Tiananmen Square in
After a story has been unequivocally
interpreted, uncertainty begins to dominate. Uncertainty reflects the difficulty
of predicting the newsworthiness, quality, volatility, timing, length and
placement of each story. Working to deadlines, editors must submit a publishing
plan (called a news budget) to the layout desk several hours before the actual
stories are filed, based on their and reporters' knowledge and experience with
similar stories and situations. This budget is used to build a template into
which the stories and art elements (such as graphics and photos) will be
plugged. Most events can be predicted. However, a major story planned for page
one, for example, may "go bust", requiring a new layout. Additional
uncertainty comes from unanticipated events. Editors deal with uncertainty
first by planning, based on their ability to predict quality, timing and length
of stories. If they know an event will be occurring in the future (for example,
a town meeting or the imminent death of a prominent person) they may have most
of the story written prior to the event, or a template only needing the facts
to be "plugged in". They additionally engage in constant
communication with reporters and each other, much of it via electronic mail, to
keep appraised of working drafts and angles. They get and exchange information
constantly and maintain flexible communication networks that reconfigure to the
needs of the moment. Finally, they maintain buffers in the form of so-called
"evergreens," an inventory of already written stories that are
relatively timeless and provide material for slow news nights, large editions,
or to replace stories that do not materialize as anticipated.
Producing the paper is complex as well as
uncertain. Going from story draft to finished paper involves many steps,
departments, and people. The variety of events, beats, and subjects covered by
a typical paper is enormous. That complexity is "chunked" via
familiar and repetitive routines and work flows with collectively well-defined
roles, behaviors, and outcomes. Newspaper publishing in general can be viewed
as a process for routinizing the unexpected, and particular events and story
types are matched to particular reporting, editing and production routines.22
The process is challenged when the
classification of the event is ambiguous or equivocal, typically crossing
several story types or editorial departments. Was the O. J. Simpson murder case
a sports, people, national, legal or page one story? When the story first
broke, its classification had to be interpreted and negotiated. However, even
hybrid story-types become routine over time and new procedures are created
accordingly. For example, I observed a newsroom during the death of a famous
actress. This event triggered their familiar
"major-news-about-celebrity" routine performed collectively by the
arts/television reporting department and the page-one national news department.
This hybrid event had occurred often enough to have had a new procedure created
and made routine.
The newspapers I studied that used direct interpersonal
communication for interpreting ambiguous and equivocal events performed
significantly better than those that did not. Poor performing newspapers tended
to overuse e-mail and other computer-mediated forms of communication early in
the process and were consistently unable to develop a shared understanding of
their work, necessary for coordinating the management of uncertainty and
complexity later on. In contrast, newspapers where editors engaged in a large
amount of "walking and talking" early in the news cycle produced a
more coherent and higher quality product, relying on computer-mediated forms of
communication primarily to coordinate the uncertainty and complexity that
emerged later in the process. The large amount of time spent in repetitive,
redundant and overlapping face-to-face conversation, considered inefficient by
some, actually proved to more efficient and effective when appropriately
combined with electronic communication, than indiscriminately over-using
ostensibly efficient electronic communication throughout.
Relationships among the Four Problems
As suggested by the example above, the four
knowledge problems exhibit a natural order or hierarchy of difficulty (Figure 2).
The most difficult of the problems is ambiguity, because an interpretive
framework must be developed where none exists. Equivocality, representing
multiple possible frames or definitions is slightly less problematic. Each,
however, similarly involves managing and processing knowledge. More tractable
is the case of uncertainty, where a unique interpretation has been defined,
although only with some less-than-perfect degree of confidence or
predictability. Finally, even a single interpretation defined with certainty
may be complex, and still require considering many elements and linkages. These
four problems are not mutually exclusive. For example, after an ambiguous
situation has been interpreted, it may reveal itself to be uncertain, complex
or both. Extensive field observations of organizations representing a wide
range of industries including financial services, software, publishing,
consumer and industrial goods manufacturing, professional services, and
retailing suggest that the four problems do exhibit a patterned sequence.
Meaning must be established and then sufficiently negotiated prior to acting on
information. Ambiguity must first be resolved, often leading to equivocality as
multiple interpretations emerge. Resolving equivocality creates a shared
context for subsequently dealing with uncertainty or complexity, and ongoing
systematic learning.
Developing the Right Knowledge Management
Infrastructure
LeaseCo23,
a small-equipment leasing company, provides an example of how an organization
can use the four-problem framework to redefine its processes and technologies
for managing its ignorance.
Leasing involves interpreting a customer's
need for financing some asset, locating and providing the source of funds to acquire
the asset, and collecting the information necessary to determine a competitive
yet profitable set of terms for providing those assets. Lease transactions may
be classified according to the framework.
An ambiguous lease is one for which a
customer's requirements do not make immediate sense or are not understood by
the leasing company, often because it is so novel. With no similar experience
the company has no framework to define and evaluate the customer's financial
and operating needs. An example would be the first time a leasing firm financed
a turnkey manufacturing or power co-generation plant. Discussion among senior
management and experts from the tax, legal, sales, asset management, and
economics departments are often necessary to come to some agreement about how
to define and approach the lease. The customer may need to be included in a
collaborative process of defining needs and capabilities. Equivocality
emerges as various interpretations and approaches are developed. At this point,
those defining the lease need to converge on one interpretation. If that is not
possible, then a small number of competing approaches might be developed. As
more information and knowledge are gathered, proposals are dropped until one
survives. Once the transaction has been unambiguously and unequivocally
defined, it may reveal itself to be uncertain, complex, both or neither.
The factors of a complex lease are
well-known and specifiable with relative certainty, but numerous and
intricately related. For example, the 100th turnkey-factory lease may not be
ambiguous or equivocal, but coordinating the activities of the many different
departments involved is complex. Lease tracking and electronic communication
systems such as e-mail and electronic discussion forums play an important role
in coordination. Expert systems and decision support systems may help apply
expertise to routine and codifiable problems that are too complex for less
experienced analysts.
An uncertain lease is one where the
factors affecting price (like interest rates, customer creditworthiness, asset
life and salvage value) are well known, but their particular values are not
specifiable with high certainty, usually because the firm lacks sufficient
historical information. The leasing firm typically performs sensitivity
analysis and builds a safety factor (an information buffer) into the price
accordingly. It may also attempt to locate sources of additional information,
such as corroborating credit history data.
Leases for many assets – automobiles, for instance
- are routine, predictable and relatively simple. Typically these leases can be
handled by one person supported by a lease pricing system that merely requires
filling in the blanks.
Each knowledge-class of lease requires a
different approach and set of capabilities for its processing, but LeaseCo
historically processed all leases as though they were ambiguous, convening a
management meeting to pass judgment on each proposal. As the business grew,
this approach placed a great strain on the lease-processing capacity of the
organization, to the point where they were unable to quote on many leases. To
address the problem, they first made the factors influencing lease prices as
explicit as possible and gathered sufficient cost data to create a computer-based
pricing model. They began classifying lease proposals into four categories,
each representing a different knowledge problem requiring a different work
process and application of information technology.
Simple leases could be quoted from a newly created standard price list
automatically generated by the quoting system for the most typical set of lease
attributes. Anyone receiving a telephone sales query could quote a simple,
standard lease.
Complex (but predictable) leases were those for which the parameter values,
while numerous and interrelated, could be specified with a high level of
confidence. The quoting system computed the price, and many values were defined
as system defaults. When various departments were involved, the quoting system
coordinated storing and integrating the input of multiple departments. The
primary customer-contact person monitored the lease progress via the system and
might enter most of the information.
Uncertain (and also possibly complex) leases were those for
which the factors were known, but their values not known with certainty. The
pricing model embedded in the quoting system provided a template or guide for
identifying those uncertain factors whose values needed defining. Each
department was responsible for estimating those values (or probable ranges)
within its domain of expertise. The system coordinated those values, supported
"what if…" profitability analysis, and computed an overall price
within some range of certainty. The results of sensitivity analysis might be returned
to the departments for additional estimation, and discussion to coordinate the
overall estimate of uncertainty might be required.
Ambiguous leases were those that could not be specified
sufficiently to use the quoting system, and equivocal leases were not a
clear fit. Those leases would still be processed in management meetings. Those
meetings now focused on just the "difficult" leases, however.
Additionally, the specific knowledge-processing goals resolving equivocality or
ambiguity could be made explicit, making more efficient use of management's and
experts' efforts and capabilities. The management team focused on migrating
those novel transactions that became routine over time to the other more
efficient lease-handling processes as the organization learned more. The
quoting system provided a formal repository to capture that knowledge as it
emerged. Capacity was continually freed for handling strategic novelty, and the
overall knowledge-processing capability of the firm increased.
Knowledge Problems Pervade the
Organization
As the examples here show, organizations face
problems of interpretation and analysis in the course of their day-to-day
operations. Events must be interpreted and then acted on to provide an
organization’s products or services. In most cases, adequate knowledge exists
to handle routine situations, even those that may be complex or unpredictable.
Organizations must be open to novel and apparently inexplicable events,
however. Their ongoing goal should be to continually convert novel events that
require special processing into routine events by systematically learning more
about them. This may require assigning explicit roles and responsibilities for
recognizing anomaly and novelty and applying the appropriate sense-making
routines. (Who in your organization functions as the Director of Anomaly, Vice
President of Novelty, or Manager of Strange Events?)
Organizations also may encounter the four
knowledge problems in working with the feedback they use to measure how well
their day-to-day operations are performing. Internal feedback traditionally has
been provided by financial variance and resource utilization reports. It may
also include non-financial measures such as new product development metrics or
employee satisfaction. External feedback may include customer satisfaction
regarding service and quality, warranty performance, or industry benchmarking.
Regardless of the feedback used, it can be complex (as is often the case with
incentive and reward systems) or unpredictable in how it varies. It also may be
ambiguous or equivocal if does not appear to make sense. Each problem
represents a potential source of ignorance regarding the organization’s
knowledge about itself. Only after these control-oriented knowledge-problems
are acknowledged, identified and acted on, will the organization know enough to
use its performance feedback to bring its operations back into control.
Executives similarly use the information and
knowledge of the organization’s control systems to formulate strategy—that is,
to determine if its competitive opportunities and organizational capabilities
and resources are appropriate and in balance. Those strategic decisions and
actions, in turn, determine what the operations to be controlled will look
like. Even though a control system might give clear, unambiguous indications
that an organization is not in control, for example, by not meeting its
financial or product quality targets, the reasoning behind those indications
may be ambiguous, reflecting a lack of strategic knowledge about how the
organization’s capabilities match its competitive environment.
Consider a life insurance company, for
example. At the operations level, insurance applications, like leases, may fall
into any of the four knowledge classes. Most are straightforward and can be
processed automatically by computer technology. Complexity is managed by using
decision support and expert systems to compile, track, integrate and evaluate
the various pieces of information forming an applicant’s risk profile. Uncertainty
is managed by amassing and analyzing huge quantities of data describing past
experience with insuring various types of risk, to build actuarial tables.
Occasionally, novel cases must be evaluated and may require discussions among
underwriters and medical researchers to make a best guess based on their tacit
knowledge gained from experience. Often, additional expertise is brought in by
involving a reinsurance company. The outcome of these unusual cases is closely
tracked and new learning is systematically captured for reuse.
The primary measure of control in life
insurance companies is the loss ratio—a measure of how much is paid out in
claims. Based on their risk management knowledge, insurance companies expect to
pay out a certain amount which they hope will be no more than the premiums they
receive less their operating expenses. A high loss ratio may reflect a lack of
knowledge among operations personnel, or an inadequate ability to predict loss
or to account for the complex set of factors affecting mortality. These are
problems of complexity and uncertainty. However, a particular loss ratio may
also be considered highly ambiguous if its cause is beyond the organization’s
ability to understand the environment creating it. When AIDS first emerged, apparently
low risk customers were dying with abnormally high frequency. This feedback was
truly ambiguous until medical research provided an adequate explanation for the
phenomenon. At the strategic level, executives were not able to make clear
decisions about how best to serve those markets because the loss ratio measure,
although itself unambiguous, was sending a strategically ambiguous
message.
As these examples show, organizations have lots
of opportunity for ignorance and must simultaneously and continuously deal with
the four knowledge problems at several levels of decision making and action.
Implications
The four knowledge-problems framework
provides a powerful lens for viewing information processing, communication, and
knowledge management in organizations. It suggests several prescriptions and
conclusions.
• Organizations must be open to novelty
and anomaly. Only by acknowledging its ignorance can an organization put itself
on the road to learning. Organizations must recognize and accept that there are
events that may be difficult to explain because no one understands them well
enough. So, for example, a retail organization penetrating new markets might
send personnel to live in the new market rather than rely on point-of-sale or
consumer survey data, if it represents a significant departure from its normal
markets.
• Knowledge management today focuses
primarily on solving problems of complexity and uncertainty. It aims to share
and exploit what is known within well-defined circumstances and contexts, and
is dominated by information technology. Expert systems apply codifiable but
highly complex sets of rules; best practice databases attempt to share less
structured but well-documented expertise; point of sale systems attempt to
provide rapid feedback for managing market uncertainty, while e-mail and
discussion databases do the same for internal uncertainty. Much less effort has
been spent worrying about the ambiguous and equivocal situations resulting from
more profound forms of organizational ignorance. To truly manage knowledge and
expertise, however, organizations must make sure that their members work toward
building a shared fundamental understanding of the situations and problems they
face. Meetings and teams, as well as informal opportunities for engaging in
sense-making conversations that raise good questions, challenge the status quo,
and directly deal with ambiguity and equivocality are all essential. Solving
convergent, well-defined problems requires having a shared understanding in
place first. It is therefore critical for organizations to be aware of and to
solve problems of ambiguity and equivocality before diving into the more
structured problems of uncertainty and complexity.
• Information technology can play an
important role in managing information and knowledge, when it is appropriately
applied. This requires diagnosing the nature of the knowledge problem beings
solved. Information technology makes sense in cases of uncertainty and
complexity, but much less so for dealing with ambiguity and equivocality.
• Organizations need to go beyond their
own boundaries to find the knowledge they need to help them make sense of the
world. Where the organization is relatively ignorant about a market or
technology, it should include the customer or vendor in the sense-making
process. In doing so, the organization will also develop a shared understanding
and basis for ongoing communication with its customers and trading partners. As
ambiguity and equivocality give way to uncertainty and complexity, the
organization can more easily migrate to more structured technologies to
communicate and coordinate with its external partners. This will become
especially important as more and more organizations more toward electronic
commerce links. Organizations may use information technology to exchange data
and information, but they will need to use social interaction to exchange
knowledge in building a shared understanding about their commercial relationships.
• Senior executives and managers must
interact freely with those at lower levels of the organization in sensemaking
and problem-solving processes to discover what the organization as a whole truly
knows. It is not enough for managers merely to catalog organizational knowledge
by creating a "knowledge map." Rather, they must sense the
organization’s knowledge and ignorance by engaging all organizational levels in
the process of resolving the four knowledge problems.
• Like managing knowledge, managing
organizational ignorance requires an appropriate culture. In general, the
organization must create an environment in which it is acceptable to publicly
admit that one does not know something. Multinational organizations I have
observed find this to be particularly problematic in certain national cultures.
Managing complexity requires a culture in which it is acceptable to identify
and support experts and seek their advice. Resolving uncertainty requires a
culture supportive of open, clear and extensive cross-boundary communication,
and a willingness and ability to bridge various languages (both professional
and national) in use across the organization. Resolving ambiguity requires the
ability to confess ignorance and confusion. Managing equivocality requires an
environment in which it is acceptable to disagree about interpretations and
which accepts diversity of views as well as useful and productive consensus.
• Each of the four knowledge problems suggests
a different set of processes, roles, information technologies, and
organizational structures for their resolution. In organizations where those
problems can be segmented, it may be possible to create separate organizational
units for their resolution. Often, however, those problems are intertwined. In
those cases, the organization must be flexible enough to modify itself
dynamically to deal with the knowledge problem at hand. For example, LeaseCo
did not actually maintain formally separate units to handle each type of lease.
It did, however, assign roles and responsibilities differently depending on
lease type. So any particular class of lease could be viewed as being processed
by a different virtual unit. One of the most effective newspapers I observed
was routinely able to transform its structure, processes and use of
communication technology to address the knowledge problems at hand as it
progressed through the daily publishing cycle. The early stage of making sense
of the day's news used a highly interactive face-to-face network of various
department editors to insure a range of views, leading to equivocality
management via informal negotiations and resolution by the Managing Editor in
the daily afternoon news meeting. Once the "angles" on the day's news
had been set, the organization managed uncertainty by spontaneously
transforming itself into a broad, flexible, highly interconnected and
responsive network making extensive use of electronic messaging. After the
stories to run had been selected and written, the newsroom personnel
reconfigured themselves into a set of "production lines" to handle
the complexity of the layout process, centrally coordinated by the news desk,
again making extensive use of electronic messaging. In contrast, the least effective
paper maintained a hierarchical structure and centralized communication
patterns throughout the process. They used electronic messaging throughout the
process in a pattern that reinforced the hierarchy. Shared context was not
created early on, and they lacked the responsiveness needed for making rapid
decisions under uncertainty during the middle of the process. Although they
were organized efficiently for production, they efficiently produced a
low-quality product.
>• Even the non-routine or unpredictable
aspects of the four problems can be managed, or at least anticipated, in a
routine fashion. Where ambiguity or equivocality routinely arises,
organizations should create standing mechanisms to address them. Provisions
must be made for face-to-face conversations to occur among those most relevant
to resolving ambiguity or equivocality. Those responsible for executing the
resulting interpretations must also be involved so that those interpretations
can be meaningfully communicated. Uncertainty can be routinely handled by
anticipatory mechanisms for exchanging information; complexity can be handled
by anticipatory mechanisms for locating knowledge.
• The four problems suggest a framework
for managing organizational learning. Ambiguous and equivocal problems often
represent non-routine events about which the organization lacks sufficient
knowledge. The process of resolving ambiguity and equivocality, however, is the
stuff of which organizational learning is made. Ambiguous and equivocal events,
if encountered enough times, eventually become familiar enough to be migrated
to more routine processes. Organizations must have the ability to evaluate
events to determine if they are interpretable or not, route them to the
appropriate resolution process, and eventually migrate those that become
familiar to routine processes, thereby reserving the organization's capacity to
continually handle novelty and confusion.
Organizations must move beyond operating as
though they understand who they are, what they do, and what they know, to
recognizing that there is always much more to learn. Knowledge management has
started firms on the road to documenting and explicating what they currently
know. Ignorance management will move them a stage further to managing what they
don’t know.
Endnotes
1 N. Postman, Amusing Ourselves to Death: Public Discourse in the Age of Show Business (New York: Viking Penguin Inc, 1985), p. 108
2 B. Kogut and U. Zander, "Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology", Organization Science, Vol. 3, No. 3, August, 1992, pp. 383-397
3 Schumacher, E. F., A Guide for the Perplexed, (New York: Harper & Row, 1977)
5 Simon, H. A., "The Architecture of Complexity", in The Sciences of the Artificial, second edition, (Cambridge, MA: The MIT Press, 1969/1981), pp. 193-229
6 J. R. Anderson, Cognitive Psychology and its Implications (second edition) (New York: W. H. freeman and Co., 1985)
7 Cyert, R. M. and J. G. March, A Behavioral Theory of the Firm, (Englewood Cliffs, NJ: Prentice-Hall, 1963); Nelson, R. R. and S. G. Winter, An Evolutionary Theory of Economic Change, (Cambridge, Mass.: Belknap Press of Harvard University Press, 1982);
8 Grant, R. M., "The Resource-based Theory of Competitive Advantage: Implications for Strategy Formulation", California Management Review, Spring, 1991, pp. 114-135
9Garner, W. R., Uncertainty and Structure as Psychological Concepts, (New York: Wiley, 1962); Raiffa, H., Decision Analysis: Introductory Lectures on Choices Under Uncertainty, (Reading, MA: Addison Wesley, 1968)
10 Conrath, D. W., "Organizational Decision Making Behavior Under Varying Conditions of Uncertainty", Management Science, Vol. 13, No. 8, April, 1967, pp. B487-B500; Gifford, W. E., H. R. Bobbitt, and J. W. Slocum, Jr., "Message Characteristics and Perceptions of Uncertainty by Organizational Decision Makers", Academy of Management Journal, Vol. 22, No. 3, 1979, pp. 458-481; Kahnemann, D. and A. Tversky, "Variants of Uncertainty", in D. Kahnemann, P. Slovic, and A. Tversky (Eds.), Judgement Under Uncertainty: Heuristics and Biases, (Cambridge: Cambridge University Press, 1982)
11 Rumelt, R. P., "Towards a Strategic Theory of the Firm", in R. B. Lamb (ed.), Competitive Strategic Management, (Englewood Cliffs, NJ: Prentice Hall, 1984), pp. 556-570
12 Prokesch, S. E., "Unleashing the Power of Learning: An Interview with British Petroleum’s John Browne", Harvard Business Review, September-October, 1997, p.168
13 Dretske, F. I., Knowledge and the Flow of Information, (Cambridge, MA: The MIT Press, 1981); Isenberg, D. J., "The Structure and Process of Understanding", Chap. 9 in The Thinking Organization, H. P. Sims and D. A. Gioia (eds.), (San Francisco: Jossey-Bass, 1986), pp. 238-262; MacKay, D. M., Information, Mechanism, and Meaning, (Cambridge, MA: The MIT Press, 1969); McCaskey, M. B., The Executive Challenge: Managing Change and Ambiguity, (Marshfield, MA: Pitman, 1982).
15 Reed, R. and R. J. DeFillippi, "Causal Ambiguity, Barriers to Imitation, and Sustainable Competitive Advantage", Academy of Management Review, Vol. 15, No. 1, 1990, pp. 88-102
16 Aristotle, Categories, 350 BC, Translated by E. M. Edghill, http://webatomics.com/Classics/Aristotle/; Weick, K. E., The Social Psychology of Organizing, (Reading, MA: Addison-Wesley, 1969)
17 Mata, F. J., W. L. Fuerst, and J. B. Barney, "Information Technology and Sustained Competitive Advantage: A Resource-Based Analysis", MIS Quarterly, Vol.19, No. 4, 1995, pp. 487-*; Goodman, P. S. and E. D. Darr, "Exchanging Best Practices through Computer-Aided Systems", The Academy of Management Executive, Vol. 10, No. 2, 1996, pp. 7-19; Zack, M. H., "An Architecture for Managing Explicated Knowledge", Sloan Management Review, forthcoming.
18 Daft, R. L. and R. H. Lengel, "Organizational Information Requirements, Media Richness and Structural Design", Management Science, Vol. 32, No. 5, May, 1986, pp. 554-571; Daft, R. L. and J. C. Wiginton, "Language and Organization", Academy of Management Review, Vol. 4, No. 2, 1979, pp. 179-191
19 Zack, M. H., "Interactivity and Communication Mode Choice In Ongoing Management Groups", Information Systems Research, Vol. 4, No. 3, 1993, pp. 207-239
20 Brown, J. S. and P. Duguid, "Organizational Learning and Communities-of-Practice: Toward a Unified View of Working, Learning and Innovation", Organization Science, Vol. 2, No. 1, 1991, pp. 40-*; J. Lave and E. Wenger, Situated Learning: Legitimate Peripheral Participation, (Cambridge, England: Cambridge University Press, 1991)
21 This example is based on observing the newsroom of four newspapers for a total of over 200 hours.
22 Tuchman, G., "Making News by Doing Work: Routinizing the Unexpected", American Journal of Sociology, Vol. 79, No. 1, 1974, pp. 110-131
