W4 Lecture 1 The Landscape of Intelligence Criminal Intellige.docx

W4 Lecture 1 "The Landscape of Intelligence"
Criminal Intelligence Analysis
The Landscape of Intelligence
The Intelligence Community
So far we have addressed the “intelligence community” in fairly
broad terms, making only passing references to its diverse
membership and how they might be understood in relationship
to each other. We can generally understand the intelligence
community as falling into four separate but sometimes
overlapping categories with each category approaching
intelligence from a different aspect.
But before we dive too far into that subject, we need to make a
distinction between the “intelligence community” and the
Intelligence Community (IC). The IC refers to the formal
bureaucracy of organizations under the United States
government. Thanks to the Intelligence Reform and Terrorist
Prevention Act (IRTPA) of 2004, which defined all intelligence
as national intelligence in order to streamline information
sharing (Lowenthal, 2012), it can be difficult to sort out what
we mean by “criminal intelligence” when we speak of criminal
intelligence analysis.
In this lecture we will look at the IC in the broadest sense,
interpreting it as involving four key terrrains on which the
exercise of intelligence analysis takes place often in striking
similar ways: national intelligence, military intelligence,
criminal intelligence, and competitive intelligence.
National Intelligence
When we think of the intelligence community, we usually think

of it in terms of national intelligence. National intelligence
refers to intelligence about national-level requirements
affecting the security of the state. The Federal Bureau of
Investigation (FBI) has jurisdiction over national intelligence
inside the United States. All counterterrorism missions,
regardless of whether they manage threats to federal, state, or
local governments, ultimately fall under the FBI’s purview.
The FBI is a component of the Department of Justice, and as
such it plays a key role in investigating federal crimes, as well.
A close partner to the FBI is the Department of Homeland
Security (DHS), although the DHS also have key disaster
management responsibilities.
The primary proponent of national intelligence in the United
States is the Director of National Intelligence (DNI), which was
established under IRTPA to coordinate intelligence activities
across the various agencies that service the goals of foreign
policy and national security.
If the job of the DNI strikes you as being redundant to the job
of the DCI, then you would be partially correct. Following
investigations into the attacks of September 11, 2001, it was
clear someone in the intelligence community would have to
suffer the consequences for not forewarning the nation against
yet another national security surprise.
As the head of the agency establish explicitly to prevent that
very thing from occurring, the DCI was essentially decapitated
in favor of the DNI, a reasonably sensitive subject for the CIA.
Beyond the CIA and the FBI, the national intelligence
community encompasses a wide berth of agencies, each with its
particular mandate, but all oriented at the national level, usually
in direct service to federal policymakers.
The most famous agency within national intelligence is the

Central Intelligence Agency (CIA), although its mandate it
restricted to operations occurring outside the United States.
Following the attack on Pearl Harbor in 1941, the United States
established the Organization of Strategic Intelligence as an
attempt to ensure we were never again caught unaware of
imminent threats to national security.
After World War II, a debate arose as to whether or not we
needed to maintain a robust national intelligence organization.
Each department at the time argued for the need to retain
separate intelligence capabilities. The Department of Defense
(DoD) in particular was concerned that a centralized
intelligence organization would detract from its ability to
collect on its own requirements.
President Truman was sympathetic to the interests of the DoD
and other agencies and therefore established the Central
Intelligence Group (CIG) as a means of organizing intelligence
analysis in a decentralized fashion. The CIG was envisioned to
serve as a clearinghouse of intelligence information to ensure
requirements at the national level were analyzed and
disseminated according to the President’s priorities.
In a case study for the fact that the President does not always
get his way, the CIG very quickly took on a life of its own until
eventually emerging from the fray as what we think of today as
the CIA. The scope of the CIA’s mission was expanded very
early on to include autonomous collection capabilities, all
brought under the oversight of the Director of Central
Intelligence (DCI). Over time, national intelligence came to
include a variety of organizations responsible for managing
collection and analysis of different types and for different
consumers (Zegart, 1999).
Military Intelligence

Military intelligence refers to intelligence about issues in which
there is a foreign-military nexus. Intelligence activities focused
on indications and warnings of surprise attacks fall primarily in
the realm of military intelligence. In this respect, military
intelligence pays particular attention at the strategic level to
changes in other nation’s military posture – whether they call
up reserve forces, purchase new weapons systems, move naval
resources into or out of an area, etc. (Lowenthal, 2012).
Military intelligence covers wartime intelligence collection in
areas designated for combat operations. The DoD operate a
command in North America that, among its other missions,
assists in counterterrorism missions in the United States, and
during times of emergency it can be called up to assist local law
enforcement agencies. Otherwise, the US Constitution forbids
the military from enforcing civil law, which includes leverage
military intelligence capabilities to assist criminal intelligence
operations where there is no foreign-military nexus (White,
2012).
The primary coordinating organization in the United States for
military intelligence is the Defense Intelligence Agency (DIA).
The DIA serves location where the military services share
information and coordinate their intelligence activities. It also
contains an autonomous collection capability to conduct
intelligence operations on military intelligence interests. Each
branch of the military also has its own intelligence capability
serving its unique needs. The Army’s intelligence activities are
unique in that they specifically do NOT include criminal
activity, which falls under the Criminal Intelligence Division
(CID).
All other branches of service (remember that the Coast Guard
falls under the Department of Homeland Security, NOT the
DoD) house their military intelligence and criminal intelligence
functions within single organizations, although they still require

a foreign-military nexus to conduct operations.
Law Enforcement Intelligence
The category of intelligence of most interest to us in this course
is law enforcement intelligence, also known as criminal
intelligence.
Criminal intelligence operations, unlike national and military
intelligence operations, are subject to legal scrutiny, and this
has profound effects on the parameters of law enforcement
intelligence as an intelligence terrain. Criminal intelligence, for
example, cannot be gathered, analyzed, or even stored unless
there is compelling reason to believe it directly relates to a
crime that has or will take place.
Criminal intelligence supports the law enforcement community
in its mission to protect rights, even the rights of those who
have been arrested. It seeks to anticipate, detect, and reveal
criminal activity with the intent of being that activity to an
orderly conclusion. It is governed by the rules of evidence.
Criminal intelligence operations, through all 6 steps of the
intelligence cycle, must withstand external scrutiny during court
proceedings. Compare criminal intelligence’s key terrain
features with those of the national and military intelligence
terrains: National and military intelligence is about protecting
targets, not rights. Their collection operations are subject to the
scrutiny of intelligence oversight mechanisms, but not the
scrutiny of the public in most circumstances (White, 2012).
As you may recall from other criminal justice courses, evidence
used in court is subject to the exclusionary rule. The
exclusionary rule states that prosecuting authorities cannot use
evidence gathered through illegal or otherwise prohibited means
against a defendant in trial. The exclusionary rule derives from
three lines of thinking within our criminal justice system.

First, in order for the court to exercise its claim as the
legitimate arbiter of the rule of law, it must itself avoid
engaging in or otherwise sponsoring activity that is against the
law.
Second, the law enforcement community has, in the past,
engaged in illegal activity motivated at least in part by the
intention of increasing its chances of prosecution in court. By
not accepting illegally acquired evidence in a trial, the court
discourages the law enforcement community from breaking the
law to secure a prosecution.
Third, if the law enforcement community secures a prosecution
through the use of illegally acquired evidence, then the
convicted defendant is granted standing for damages. So even
though the defendant may be found guilty, he or she can pursue
civil action against the law enforcement organization who
collected the evidence illegally. The Supreme Court determined
that use of civil actions as remedies for such damages would
constitute an onerous burden for the criminal justice system and
waylay the dispensation of justice for the greater benefit
(Neubauer & Meinhold, 2010).
The legal scrutiny to which evidence presented in courts must
be put ensures proper due process. This standard defines the
parameters within which the law enforcement community must
conduct its intelligence activities or risk having trial after trial
dismissed. If a criminal intelligence analyst does not perform
her duties with due diligence to the law, then what, after all, is
the use of her intelligence products?
Law enforcement officers and prosecuting attorneys at all levels
gather criminal intelligence for the purposes of securing
convictions in formal legal proceedings. This end use means
that all criminal intelligence activities must start from a
reasonable suspicion of activity. The law enforcement
intelligence community includes a much looser organization
than national or military intelligence communities, but in every

case it involves intelligence activities at the service of
organizations whose mission is to enforce the laws at various
echelons of our government.
As mentioned earlier, the FBI is responsible for federal law
enforcement. In order to facilitate information sharing and to
increase the efficiency of criminal intelligence activities, Joint
Terrorism Task Forces (JTTFs) were established. JTTFs exist in
most metropolitan cities and control territories that cover the
entire United States. They have so far proven very for bringing
resources to local law enforcement organizations to which they
would not have access otherwise, and somewhat useful for
pushing information up to federal organizations that they would
not have access to otherwise.
One of the key advantages of JTTFs is that all participating
officers have security clearances that allow them access to
information they may find useful for supporting those
organizations they have national defense missions, although
they cannot use this information for criminal prosecutions.
Competitive Intelligence
Competitive intelligence, also sometimes referred to as business
intelligence, is perhaps the newest member of the intelligence
landscape, though that position is debatable. Competitive
intelligence concerns the collection of information about the
activities, plans, and intentions of the business community for
the business community.
Most large companies have competitive intelligence
organizations tasked with providing business executives the
information they need to advance their company’s interests and
secure them a competitive advantage. To be clear, competitive
intelligence is NOT industrial espionage. Industrial espionage
involves stealing information or products from a competitive,

and it is always illegal.
Competitive intelligence professionals routinely analyze
business information in order to predict new trends in markets
and how competitors are attempting to manage them. Much
information about a company is publicly available, either
through tradeshows, financial portfolios, business news media
outlets, and the like. When employees transfer between
companies, a HUMINT collector in the field of competitive
intelligence may debrief them about the projects they were
working on in their last position.
Competitive intelligence often concerns itself with risk analysis
to make recommendations to executives about the dangers or
advantages of alternative courses of action. The supplementary
text available in the Lessons menu for this week is an article
from the International Journal of Information Management that
describes how the competitive intelligence terrain can benefit
from exposure to the other terrains (Powell & Bradford, 2000).
You may want to review it in more detail than we will discuss it
here because it includes an excellent example of using the
target-centric approach to analysis to model an organization’s
behaviors based on information the organization would be
unlikely or unable to hide from public view.
In the article, Bradford and Powell show how a competitive
business unit can construct a model of their company’s
interactions with the operations of a competitor even when the
competitor attempts to conceal sensitive information. They start
by finding information that is publicly available and plugging
that information into a common business model that explains a
certain aspect of an enterprise’s operations that may not be as
publicly available.
By doing this they establish a model adapted for a particular
competitor off of which a competitive intelligence analyst can

establish thresholds where changes in known values should
indicate changes in unknown values inside the competitor firm.
Figure 2 shows a dynamic process model where the values of
different aspects of a company’s operations are known to
increase or decrease based on changes to other values. Such
models help predict how a competitor will react to changes in
the market, or what their future plans and intentions may be.
References
Clark, R.M. (2007). Intelligence analysis: A target-centric
approach (2nd ed.). Washington, DC: CQ Press.
Lowenthal, M.M. (2012). Intelligence: From secrets to policy
(5th ed.). Washington, DC: CQ Press.
Martin, G. (2013). Understanding terrorism: Challenges,
perspectives, and issues (4th ed.). Los Angeles, CA: Sage.
Neubauer, D.W., & Meinhold, S.S. (2010). Judicial process:
Law, courts, and politics in the United States (5th ed.). Boston,
MA: Wadsworth.
Powell, J.H., & Bradford, J.P. (2000). Targeting intelligence
gathering in a dynamic competitive environment. International
Journal of Information Management, 20, 181-95.
White, J.R. (2012). Terrorism & homeland security (7th ed.).
Belmont, CA: Wadsworth.
Zegart, A.B. (1999). Flawed by design: The evolution of the
CIA, JCS, and NSC. Stanford, CA: Stanford University Press.

International Journal of Information Management 20 (2000)
181}195
Targeting intelligence gathering in a dynamic competitive
environment
J. H. Powell!,*, J. P. Bradford"
!School of Management, Bath University, Claverton Down, Bath
BA2 7AY, UK
"Arthur D Little, Berkeley House, Berkeley Square, London,
UK
Abstract
Current management approaches to resource-based strategy and
core competence thinking require
extensive intelligence gathering to ensure that correct
assumptions are being made about the environment
and competitors'capabilities. Without such intelligence any
attempts to develop, maintain and in many cases
even identify the key assets and competences are #awed. Often
the very people who are best placed to carry
out intelligence appraisals are those contributing strongly to
mainline business activities of technology
development and business winning. Because of this potential
con#ict the intelligence process must be
e$ciently targeted. Existing methods of intelligence concentrate
on the process of intelligence gather-
ing, frequently using military analogies. To the extent that a
competitive system model can be created
these existing approaches can be made more directed in their
application and therefore more e$cient in
their use of valuable in-company resources. Moreover, the very
expression of a competitive system model

can improve the appraisal process itself. The applicability of a
general system modelling technique,
used extensively in business process modelling and known as
qualitative systems dynamics (QSD) is
described and its applicability to the intelligence targeting
problem is examined through the use of a case
study. It is shown that the method is accessible and relevant to
competitive intelligence problems. An
example from practice, the competitive analysis of a major
international defence company, is shown. It is seen
that the level of analysis (data points, process understanding,
systemic knowledge) relates closely to the
elements of the system model and this concordance aids
targeting. ( 2000 Elsevier Science Ltd. All rights
reserved.
Keywords: Strategy; Competitor intelligence; Process
modelling; Qualitative methods
* Corresponding author.
E-mail address: [email protected] (J. H. Powell).
0268-4012/00/$ - see front matter ( 2000 Elsevier Science Ltd.
All rights reserved.
PII: S 0 2 6 8 - 4 0 1 2 ( 0 0 ) 0 0 0 0 4 - 9
1. The context and importance of competitive intelligence to the
5rm
1.1. Resource-based strategy
One of the most integrating and almost ubiquitous threads of
current management thinking
emphasises the importance of a resource-based view of the

company (Barney, 1997; Grant, 1998;
Markides & Williamson, 1994). In this approach the
development of the "rm is expected to be built
"rmly upon its existing key assets and distinctive competences.
These assets and competences are
characterised and indeed identi"ed by comparison with the
assets and competences of competitors,
and conventionally are required to possess three attributes
(Prahalad & Hamel, 1990):
f access to a wide variety of markets,
f a signi"cant contribution to the perceived customer bene"ts of
the end product,
f di$cult for competitors to imitate.
With this perception, then, the conventional situational
appraisals of strengths, weaknesses,
opportunities and threat (SWOT) analyses are informed by the
need to maintain these core
competences in the face of the development by competitors of
their own core competences and key
assets (Hax & Majluf, 1996). Such development then forms a
central part not only of the relatively
localised product and market development strategies of the "rm,
but also a central agenda item in
the horizontal strategy of the company, whereby an important
part of the governance of the "rm is
seen to consist of the engendering of an appropriate
environment for the development and
maintenance of these &crown jewels' of key assets and core
competences.
Within such a schema, then, competitive intelligence can be
seen to be of vital strategic impor-
tance in a number of ways:

f If we are to assess our own key assets and core competences in
comparison with those of our
competitors, we are obliged to have some view of what those
competitor competences are.
f The imitability of our own putative core competences can only
be assessed to the extent that we
know the capacity of competitors to imitate.
f We would wish to know, conversely, the vulnerability of
competitors to our imitation of their
core competences.
f Our ability to access new markets can only be satisfactorily
addressed if we have knowledge of
competitors' intentions and capabilities in that respect.
Without such knowledge our capacity to access existing and
new markets and, above all, to identify
and maintain the basis of our competitive advantage will be at
best #awed and, more likely,
severely limited.
1.2. Other bases for requiring competitive intelligence
Resource-based strategy does not represent the sole justi"cation
for a competitive intelligence
programme. Conventional views of the details of the market and
structural struggles of companies
demand knowledge (or at least assumptions) of the positions of
competitors in negotiation
(Thompson, 1998) and of their intents and tactics in market
development campaigns (Rumelt,
1998). Even with the modern emphasis on network approaches
to industrial strategy and the need

182 J. H. Powell, J. P. Bradford / International Journal of
Information Management 20 (2000) 181}195
for partnering approaches to managing these networks of
buyers, suppliers and peer companies, it
can be argued that knowing the capabilities and intents of other
companies in essentially non-
competitive and trusting relationships is an added contribution
to the well being of the grouping in
addition to being a safeguard against any future change in the
nature of the relationship.
Lastly, it is important to retain a dynamic understanding of the
technology trajectories of the
surrounding industrial environment (Nelson, 1997). Without this
perception of the changing
technical capabilities of our own industry and that of adjacent
industries (from whence unexpected
new rivals may emerge) our attempts to generate a viable and
appropriate technology strategy will
be in vain. No matter how capable our company is in generating
technology there is always the
possibility that another company in an adjacent, previously
unobserved industry will innovate in
a direction which we did not expect and in such a way as to
render obsolete our own industry-
centred innovative process.
1.3. Need for ezcient targeting
The opportunity cost of such competitive intelligence gathering
is high, since the best intelligence
appraisals are frequently those carried out by the very people
who are engaged in developing or

exploiting our own core competences. Intelligence derives
always from an appraisal of data
informed by some underlying model of behaviour, a hypothesis
or an assumption against which the
competitors'actions and utterances can be tested. It is frequently
the most knowledgeable members
of our company who are in the best position to develop these
hypotheses and test them against data
gathered often on an opportunistic basis. It is incumbent upon
us, therefore to target the
competitor intelligence appraisal process e$ciently since an
e$cient competitor intelligence
process will provide the least disruption to the main-line
activities of market development, sales or
technology generation in pursuit of our competitive advantage
and its exploitation. If, in addition,
that targeting is carried out on the basis of a model which
contributes to the appraisal process itself,
by representing our best understanding of the dynamics of the
competitive system which we are
investigating, then a double bene"t will accrue, since the
appraisal process itself will be rendered
more e$cient.
2. Existing approaches
2.1. Intelligence cycles
Kahaner (1996) supports the view that a number of bene"ts
derive from engagement in
competitive intelligence activity. In processual terms Kahaner
proposes the use of a military style
intelligence cycle to facilitate the planning, collection, analysis
and dissemination of intelligence
within the organisation. The cycle referred to consists of a
highly interactive process of data

gathering, and conjecture about the enemy's order of battle,
capacity and intent, which in turn
targets further data gathering activities. The justi"cation for this
is the usage of this cycle by
governmental agencies such as the United States Central
Intelligence Agency.
As discussed above, it is highly desirable to seek examples of
best practice in order to improve the
competitive advantage of the corporation vis-à-vis its
competitors and this applies no less to the
J. H. Powell, J. P. Bradford / International Journal of
Information Management 20 (2000) 181}195 183
process of competitive intelligence acquisition as to other
business processes. For those con-
cerned with the methods and process of intelligence operations
possibly the most obvious starting
point for comparison is the intelligence apparatus maintained by
the modern nation-state. In the
UK, for example, intelligence is estimated to cost in the region
of $1.5 billion per annum, equivalent
to approximately 5% of that devoted to defence. In personnel
terms Herman (1997) identi"es the
requirement to be equivalent to 25% of that engaged in Shell
(UK)'s operations.
2.2. Military intelligence methods
The use of individuals to gather intelligence for the bene"t of a
society has a long history going
back to biblical times. The modern intelligence organisation has
its origins in the early twentieth

century with the establishment in Britain of MI-6 which had as
its mandate responsibility for
intelligence gathering overseas (Christopher, 1985). The
American CIA emerged as a result of the
Second World War in order to consolidate the fragmented
intelligence e!orts which were blamed
in part for lapses such as the infamous Japanese attack on the
naval base at Pearl Harbour
(Darling, 1990).
Having become a formal part of the machinery of government
how does the state-sponsored
intelligence business function? The use of the intelligence cycle
as recommended by Kahaner is
actually derived from the military. The rationale is noted by
Herman that, &Contrary to critics+
beliefs, peacetime intelligence sets considerable store by
behaving &properly+ with &proper+ procedures.
Hence the attraction of the military metaphors of requirements
and the cycle' (Herman, 1996).
This last quotation suggests an explanation for the failures of
intelligence to cope with extraord-
inary situations. Intelligence services world wide have been
held responsible for failures which have
had consequences for their governments. The United States
intelligence community were blamed
for failing to predict the end of the Soviet Union and more
recently an enquiry was started as
a result of the failure to advise as to the likelihood of Indian
nuclear testing.
In Israel the reputation of its intelligence machinery was badly
damaged by the failure to give
appropriate warning as to the 1973 attack by the Arab states.
Black & Morris suggested that this

over-sight was in large caused by, &a concept2 that the Arabs
had no intention and were incapable2
of renewing the war against Israel (Black & Morris, 1991).
Likewise, in the UK the intelligence function was held
accountable for errors in detecting the
invasion of the Falkland Islands by Argentina. An enquiry after
the end of hostilities considered as
evidence a paper prepared for the Joint Intelligence Committee,
a senior co-ordination body for the
British intelligence community, on the possible risk from
Argentina (Franks, 1983). A later analysis
described the report as, &a very routine item, containing
nothing to start ringing alarm bells' (West, 1997).
2.3. Shortcomings
In all these cases it could be suggested that the organisation in
following the cycle has established
standard operating and monitoring procedures which have led to
a certain myopia in divining from
the information available knowledge about the changing
environment. Furthermore the cycle,
whilst a useful tool for visualising a static situation, appears
inappropriate when considering an
environment such as a subversive organisation or market
competitor over time in a changing
environment.
Indeed in the cases highlighted previously the quality of
intelligence regarding the opponent, be

they the Soviet Union, Arab states, or Argentineans was often
good. The problem seemed to
revolve around its situation within an environment which was
itself dynamic. For example in the
Israeli intelligence services the victories over the Arab states in
previous years helped to form
a mind-set which degraded intelligence about the Arab armed
forces on the basis that they had lost
previous engagements with the Israelis and were of poor
quality.
Allied to this is the problem of maintaining the subjectivity of
the analyst. A classic problem for
the government intelligence agencies has been to select and
maintain analysts who can view the
world as their opponents or competitors might. This is in
contrast to the assumption that, &all
foreign persons are educated at Oxford or Harvard' for example.
The implication of this statement
is the risk of viewing information and assuming that
competitors strategic culture is the same as the
analyst's. Furthermore, the intelligence analyst in government
often joins their organisation and
stays with it for the duration of their career. This means that the
intelligence collectors and analysts
do not get to spend time in posts where their customers operate,
and therefore quite understand-
ably they lack sensitivity to the customers needs. Allied to this
situation intra-organisational
mobility means that desks devoted to particular specialities
have a turn over of sta! every 2}4
years, the implication being that it is hard to build up a
knowledge bank within the organisation
and too easy to dismantle.
In summary the quest for e!ective competitive intelligence

acquisition and analysis within the
management information structure can derive bene"t from
examination of best practice of the
nation-state. However, such consideration must be balanced by
a concern with utilising methods
for collection which go beyond the formal intelligence cycle to
incorporate an ability to consider
dynamic environments where the organisation engages in
competition with other actors. The next
section of this paper illustrates such a technique.
3. A system interpretation of intelligence product
In order to make clearer the use of system models to facilitate
intelligence analysis and gathering,
a distinction is made between three levels of intelligence
product, namely
f point data,
f process-based information,
f system-based knowledge
with an implication of an increasing contextualisation within a
declared system understanding.
At the lowest level of system understanding business
intelligence is concerned with data points.
These are essentially isolated pieces of data, often very
important to the success of our endeavour,
but viewed as being essentially unconnected to the surrounding
activities of the competitor. For
example, we might wish to know the cost of production of a
competitor's product. While we are
fully aware that the cost of production is a result of a number of
underlying factors in our
competitors' operation, such as wages levels, production

e$ciency, etcetera, it is the end e!ect
which motivates our data gathering rather than the underlying
factors.
There are, however, certain advantages in viewing the
competitor's cost of production as part of
a linear process of cost/value accretion. Firstly, it may not be
possible to acquire the cost of
production directly. Such information is closely guarded and
attempts to obtain it directly may be
neither successful nor ethical. If we view the cost as the end of
a process, however, we can enquire
what the components of that cost might be. Our accountants
may o!er the suggestion that the end
cost is dominated by only a small number of factors, say wages
bills, cost of capital expenditure and
raw materials. We can easily estimate the "rst; advertisements
in the trade press for various levels of
sta! present a ready source of data. Similarly, the cost of capital
expenditure can be estimated by
knowledge of the cost of money and estimations of the cost of
set-up of the production system. The
third component, the cost of raw materials, is a piece of data
which can be acquired by the simple
expedient of inviting quotes from suppliers. Thus, by
considering process-based information we can
make estimates of directly inaccessible data and, moreover
establish con"dence levels by cross
referencing incomplete data directly acquired with other
estimates derived from process under-

standing.
So far, however, we have still had the aim of establishing single
data points, albeit underwritten
by a process understanding. A further step, leading to system-
based knowledge, is to take into
account the complex commercial system of which the data
points and process-based information
are only parts. The essential characteristic of their system-based
knowledge is that it is concerned
with knowledge which derives from an understanding of the
commercial system as a whole. For
example, while it is appropriate at the operational level to be
concerned about the costs of
a competitor in order to estimate, say, the entry price of a &me-
too' product, at the strategic level
companies are concerned with wider issues, such as the product
strategy of competitors. Here an
understanding of the relative balance of a competitor's
expenditure of resources on innovation (in
order to establish di!erentiated products) on market process
innovation (in order to achieve a low
cost base) on advertising (to achieve, for example, brand loyalty
or perceived quality) is essential to
make an appreciation of the long term strategic posture of the
competitor. If we are concerned with
a level of strategic intelligence gathering above the product
strategy level * at the market strategy
or corporate level, for example * the need for a broad-based
system context is even stronger. Such
an appreciation can only be achieved by establishing a rich and
extensive model which spans both
the internal processes of the protagonists and the essential
market mechanisms.
4. System structures

An appropriate structure for such a model can be seen in Fig. 1.
The hatched areas represent
internal systems models for ourselves and the competitors (only
one competitor being shown here).
The centre section represents a system model of the dynamics of
the market mechanisms,
distinguishable from the internal processes by the common
nature of the variables. For example,
a variable called Company A's proxt would clearly fall within
the internal model of company A (in
spite of the inherent knowledge that a pro"t won by A in a "xed
market implies a loss of pro"t by
other companies). Conversely, a variable called total market
size would naturally fall within the
central market model, since it is speci"c to neither company
alone.
Note that there are certain linkages between the internal
processes of the protagonists and the
central model. For example, if a company expands its core
competences by internal investment in
techniques to reduce costs (process innovation) or to enhance
the di!erentiation of its products
(investment in research) the e!ect will not simply be felt in
terms of the company's internal state.
Fig. 1. System modelling structure for competitive intelligence
targeting.
Certainly, the internal costs will be reduced or the salary bill

increased (internal e!ects) but
additionally the total market size may increase as new
customers are attracted into the market by
the reduced costs or increased features of the product. These
links are shown conceptually in Fig. 1.
Fig. 1 shows no links directly between the two companies. In
the case where two companies are
in a direct competitive stance one against the other this will be
representative but to the extent that
the companies are in a non-competitive relationship (such as
technology sharing) there will exist
links between the companies which do not pass through the
market mechanisms and which will
therefore bypass the market model in the system modelling
structure exempli"ed by Fig. 1. It may
be argued that no relationship between companies is entirely
without market consequences, and to
the extent that this is true, it will be possible to eliminate
linkages which bypass the market model.
5. A system modelling example
The system models exempli"ed by Fig. 1 can be implemented in
a number of ways and it is not
within the scope of this paper to argue in detail the respective
merits of wholly quantitative models
vis-à-vis those which take into account the less clearly de"ned
contributions to be made to system
behaviour by their constitutive human elements. In the context
of competitive intelligence,
however, it is observed that any system modelling approach
which fails to re#ect the e!ects of
human behaviour upon the business system is unlikely to be
adequately rich in its representation
(Checkland, 1984).

A modelling approach, known as qualitative systems dynamics
(QSD) has proved successful in
practice (Powell & Bradford, 1998). The method is based on the
well-known systems dynamics
method (Coyle, 1996) of in#uence diagrams. The building block
of a QSD in#uence diagram is the
linkage of two descriptive variables by an arrow which indicates
causality and consequent
correlation between the variables. Causally related variables
with negative correlation (for
example, the well-known e!ect of economy of scale in
production, where increased volume results
in a reduced cost of unit production) are connected by an arrow
with a negative sign attached.
Positively related variables (for example cost of production
a!ecting cost of goods sold) have
a positive sign attached.
It must be stressed that the signs attached to the heads of the
arrows indicate only the causality
and correlation between the variables; if cost of production goes
up it pushes cost of goods sold up
Fig. 2. A potentially unstable loop.
with it (# sign). There is no implication that a particular
variable is rising or falling at any
particular time. If the cost of production falls, it will cause the
cost of goods sold to fall (still a positive
correlation, hence a # sign).

By building chains of such causal connections, diagrams which
capture the characteristics of
systems can be built, and in particular the e!ects of loops of
variables can be examined. Fig. 2
shows a relevant example.
Here we see that if we increase the funds available in a
company we are more able to invest in the
development of new markets. This in turn increases the size of
the available market which increases
the sales volume, the sales income and the proxt, with the result
that more funds become available
thus increasing the capacity for investment in new markets. It
should be noted that the QSD method
does not claim that this investment cycle will increase without
constraint. Firstly, there will be
compensating loops in the rest of the system which will act as
limiting mechanisms to the isolated
behaviour of Fig. 2. Secondly, the very purpose of expressing
system behaviour in this fashion is to
identify management agendas and action plans which will
control the system, and to the extent that
management is e!ective in this respect the e!ect of loops such as
that of Fig. 2 will be provoked or
attenuated. Identi"cation of potentially unstable loops such as
that of Fig. 2 is a useful indicator to
managers that, left alone, such mechanisms can build strongly
either in our favour or against our
interests, and that a management agenda can be derived which
promote the linkages in the loop
when such linkages are in our interests and can be attenuated
when they act against our interests.
Powell and Bradford (1998) report a case study in management
of product safety which illustrates
this agenda setting.

Other loops, described as goal-seeking, behave in a potentially
stable fashion. Fig. 3 shows a loop
(related to that shown in Fig. 2) where the funds made available
to develop new markets is seen to
increase the cost of goods sold, which in turn causes the proxt
to fall, which in turn causes a fall in the
funds available for market development. Thus, there is an
inherent tendency for such a loop to seek
a stable level. The extent to which it will stabilise is a function
of the strength of the linkages
between the variables, but qualitatively the loop presents the
possibility that management action
can strengthen the tendency of the underlying system dynamic
either to run away or to stabilise.
Fig. 3. A goal-seeking loop.
Fig. 4 shows the full diagram from which Figs. 2 and 3 are
extracts. It derives from an
actual consultancy concerned with establishing the nature of the
competitive environment
for a major international defense company. While it is a
simpli"cation of the actual overall
model produced it captures many of the important illustrative
points for competitive
intelligence. Comparing it with Fig. 1 we see that the central
section has a market model
showing, for example, that increased switching costs will tend
to raise the height of entry
barriers to a new entrant ( predation). If new entrants cross that

entry barrier ( predation up) the
market share of companies A and B (the incumbents) will fall
(A's market share and B's market
share).
The internal models of this illustrative in#uence diagram show
the e!ects of increased investment
in research on designed quality and features, both of which in
turn a!ect the perceived quality. This
then increases the extent to which a company can claim a
relative premium in the market which then
increases the price.
While the simpli"ed Fig. 4 does not purport to be an adequate
representation of a business
system, it shows many of the characteristics of the more
complicated in#uence diagrams appropri-
ate for actual consultancy work. Its use for competitive
intelligence targeting and analysis will now
be illustrated and certain conclusions drawn regarding the
nature of the system origins of
competitive intelligence of various types.
6. Use of the QSD diagram to target and analyse competitive
intelligence
Within the context of in#uence diagrams such as Fig. 4 the
tripartite distinction between
point data, process information and system-based knowledge
can now be seen as relating,

Fig. 4. Competitive system model.
respectively, to
f the individual variables within the diagram,
f chains of causality represented by sequences of arrows linking
variables and,
f loops and connected loops.
6.1. Point data
Individual variables within the diagram represent items of
intelligence which we might seek to
measure essentially in isolation from the surrounding system
connections. An important piece of
point data which might allow us to assess the ability of a
competitor to compete against us in terms
of speed of innovation might well be the rate of research
investment. The point data approach would
then lead us to attempt the acquisition of that data directly.
Such an enquiry is extremely di$cult;
companies guard the details of their research expenditure
carefully, and as it does not form part of
any statutorily required public declaration, it is unlikely that
any company for which research
expenditure was important would willingly release such data.
Resort might then have to be made to
less direct methods. Sta! recruited from the competitor might
bring information with them;
existing sta! of the competitor might be indiscreet;
benchmarking standards may give insight of the
expenditure to be expected. Even so the amount of intelligence
available if the competitor's
research expenditure is treated merely as an dissociated point
variable is limited. Since the

Fig. 5. A process view of research investment (extracted from
Fig. 4).
measurement of such a variable is dissociated from the
surrounding system dynamics the point
data is essentially an item of intelligence assessed in a static
fashion.
6.2. Process-based information
A more successful analysis approach which contributes to the
targeting task is to consider the
linear processes of which research investment forms one
element. See Fig. 5 below.
Such a perspective puts the research investment in the "nancial
context of the company.
Investment in research is seen as competing with investment in
process (eventually to reduce
production costs) and with investment in market development.
The analyst is now in a better
position to estimate the research investment of the competitor.
Bounds can be placed on the
investment by making estimates of the other two competing
variables. For example, our accountants
may inform us that the investment in process is dominated by
the cost of capital investment. Since we
can observe the building of capital equipment and because we
can determine the likely costs of
speci"c purchases of large plant (by making legitimate enquiries
of suppliers) we can estimate the

likely "nancial expenditure on process. More re"ned estimates
would result from a more detailed
process model, and might involve the visible e!ect of
recruitment and the expenditure on
production sta! by observation of the recruitment activity
(through public advertisements for
sta! ) and the going rates for salaries at various levels. The
essential characteristics of the
process-based information view is that pieces of intelligence
can be built up by considering
contributory associated factors upon which more data may be
available. Such a processual
approach already falls within the readily available techniques of
competent intelligence analysts.
6.3. System-based knowledge
There is more to be gained from taking a whole system view,
however. Fig. 6 shows a di!erent
extract from Fig. 4.
It will be seen that investment in research forms part of a closed
loop which passes through cost of
goods sold, price, sales income, proxt, available funds and back
to investment in research. There are
three other loops in the diagram, two passing through
investment in process and another passing
through investment in markets.
Fig. 6. Loop analysis of research investment.

An understanding of the place of the competitor's investment in
research in a dynamic loop
which encompasses price and pro"t allows us to make dynamic
estimate of the research investment
over time. By targeting our competitive intelligence resources
upon the key variables of the model
constituted by Fig. 6, we can mobilise a whole series of pieces
of information such as the
year-on-year pro"t of the company, the year-on-year prices, the
relative investments on process
and in new markets. The analyst thus has the basis for
bypassing the protection measures which the
competitor can put in place to protect against data-point
acquisition and certain process-based
inferences on our part. It is signi"cantly more di$cult to protect
against the dynamic e!ects of
variables which of necessity are publicly available, either
because of regulatory requirements or by
virtue of their publication being part of the o!ering process by
which trade is carried out (prices, to
take an obvious example).
7. Strategic intelligence and the market model
Intelligence product such as the research investment rate
discussed above operate at essentially
at the tactical or operational level. At the strategic level
companies require insight into the policies
which are being pursued by their competitors. We might
enquire, for example, whether the overall
policy of the competitor is changing on balance between cost
competition and di!erentiation. We
might wish to know whether the competitor's strategy has
changed from exploitation of an existing
market to the development of new markets. The QSD in#uence
diagrams give insights into the

appropriate targeting of intelligence resources at this strategic
level.
Let us take the case of a competitor who may be changing
policy from existing market exploitation
to new market development. Fig. 7 (an extract from Fig. 4)
shows some of the key e!ects.
Fig. 7. Model of market entry policy of company B and its e!ect
on company A.
In a similar way to the analysis of research investment above,
the justifying logic of the
competitor can be traced in the right hand side of Fig. 7.
Investment in new market penetration will
drive up the overall size of the market which in turn will drive
up the sales volume of our competitor,
company B. This will drive down B's cost of production
(because of economies of learning, scale and
volume). This will drive down the cost of goods sold which
pushes up B's available pro"t.
8. System grounding of intelligence product and its relevance to
the business task
It will be clear from the above that system understanding is
vital for the targeting of intelligence
resources and for the analysis of the raw product. To the extent
that we can contextualise lower
level intelligence product within a higher system level of
understanding (point data within

a process-based understanding or process-based information
within a system-based understand-
ing) then we can contribute to intelligence product at that
higher level. The advantages of this
contextualisation are not limited to the analysis process alone,
however, since a sensitivity to the
Table 1
Concordance of intelligence product level with system elements
and mechanisms of acquisition
Level of intelligence product Corresponding system
element
Nature of intelligence
product
Mechanism of acquisition
Point data Variable Static Direct observation
Process-based information Chains and trees
of causal links
Causal Building up information
from associated data
System-based knowledge Loops Dynamic Observation of time-
varying data
Connected loops Policy Induction of underlying

policy
indicators and warnings which cue our awareness of a
competitor's change of policy also allow us
to target our limited acquisition resource more e!ectively. Even
the simpli"ed system model above
shows the extent to which the codi"cation of a system
understanding allows greater awareness of
these indications than does any attempt to acquire isolated point
data.
Intelligence product informs strategic understanding and
decision making as much as it does
operational decision making, particularly when the core
competence or resource-based strategic
frameworks are taken into account. These approaches rely on
measures of our competence relative
to the competitors, and this comparison is in essence an issue of
intelligence. We can be as accurate
and self-observant of our competences as we wish; without an
equally accurate view of the
competitors' competences our developmental strategy will be
ill-founded. Naturally, the more
strategic the use to which our intelligence product is to be put,
the higher the level of the system
model which underwrites it.
Whatever the purpose of the intelligence product, however, the
tripartite distinction observed
above between point-data, process-based information and
system-based knowledge informs the
targeting and analysis process. The connection between the
system modelling and this tri-partite
distinction can be summed up in Table 1. Here we see that there
is a direct concordance between
the level of intelligence product desired, the applicability of

that product and the level of system
contextualisation which is necessary to produce that intelligence
product.
The practical implications of the system-based approach are
wide ranging. Firstly, the QSD
in#uence diagrams provide a convenient lingua franca between
the intelligence acquisition profes-
sionals and the policy levels of the "rm. Secondly, the
generation of a system diagram produces
convergence between the perspectives of the policy-makers and
the intelligence acquirers, and this
of itself is much to be desired. Thirdly, the contextualisation
process addressed above provides
a tangible increase in e$ciency in raw intelligence acquisition in
that marketers and others engaged
previously in data-point acquisition become sensitised to the
intelligence implications of data
encountered by chance. By making them aware of the process
implications the relevance of the data
can often be seen more clearly. Fourthly, and most signi"cantly,
the direct e!ect on the strategic
decision making processes can be tangible. The contribution of
intelligence product at the policy
process level can often be indirect and under valued, but with a
system-based approach the direct
applicability of the intelligence product to the detection of
policy changes on the part of the
competitors, for example, is clearly seen by the senior
executives. The e!ect of this is not only to

enhance the status of the competitive intelligence function of
the "rm, but also to improve the
policy generation process. This is clearly of signi"cance to the
e!ectiveness and survivability of the
company in today's complex and dynamic business environment.
References
Barney, J. (1997). Strategic factor markets: Expectations, luck
and business strategy. In: NJ Foss, Resources "rms and
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history of Israeli intelligence. London: Hamish Hamilton.
Checkland, P. (1984). Systems thinking in management: The
development of soft systems methodology and the
implications for social science. In U. Probs, Self-organisation
and management of social systems (pp. 94}104). Berlin:
Springer Verlag.
Christopher, A. (1985). Secret service: The making of the
British intelligence community. London: Heinemann.
Coyle, R. (1996). Systems dynamics modelling: A practical
approach. London: Chapman & Hall.
Darling, A. (1990). The central intelligence agency: An
instrument of government to 1950. Pennsylvania: Pennsylvania
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University Press.
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and Company.
Before taking up his present post, teaching Strategy at Bath
University School of Management, John Powell pursued
a career in the defence industry, operating at the board level and
representing the UK in international projects and in
NATO study groups. Much of his industrial experience is in
building and managing inter-company groups on major
international projects, where competitive intelligence is of
critical importance. He consults regularly on the interface
between strategy and security in the "rm. His publications cover
inter-company con#ict, security management and
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Medal for Academic Excellence and the OR Society's
President's Medal.
Je!rey Bradford is a management consultant with Arthur D.
Little. He recently completed his doctorate in Strategic
Decision Making with Cran"eld University, Royal Military
College of Science Shrivenham. Je!rey has published on
a range of subjects including the impact of operational research,
intelligence, knowledge and security concerns at the
strategic policy making level and contributed the UK Strategic
Defence Review in 1997.

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