2
Performing the Analysis Process

Analysis is, arguably, the most important process underlying how decision makers make sense of their competitive and strategic environment. For analysis to achieve its aims and potential, analysts must be cognizant of this and appreciate how they can best contribute to meeting the organization's needs.

In this chapter, we explore the analysis step that is part of the larger intelligence process, as illustrated in Figure 2.1.

Figure 2.1 Analysis as a function of the larger intelligence project cycle

It is difficult to master the task of performing business and competitive analysis (BCA). Few actually can do it well without substantial development and experience. Even those who declare that they have been trained to do it may not be as skilled as they think they are at producing good output. In light of the many business decision-making failures we witness, the only conclusion that can be reached is that relatively few organizations have a well-developed analytical capability. Fewer still leverage that capability over time into achieving competitive advantage.

This chapter looks at the key success factors of performing BCA well. It is designed to help analysts better understand their tasks, their customers, the impact of their output, and the relationships they must build in order to get the job done. Due to their unique importance as part of the larger sense-making process, we discuss common analytical pitfalls in Chapter 3, "Avoiding Analytical Pitfalls," and how to communicate analysis results in Chapter 4, "Communicating Analysis Results."

Understanding the Customers of Analysis

"Basically, the work that we do for our customers, which are our executive leadership, our sales forces, our product managers, and our strategy people, should help them with their customers . . . our approach is very customer-focused; how are we going to help our customers help their customers?"

—Bret Breeding, former Global Corporate Competitive Intelligence Manager for Compaq Computer

The first questions any analyst must answer are "Who are my analysis customers?" and "What are their critical needs?" These can sometimes be difficult to answer, especially if the customers themselves cannot effectively articulate what they want or there are multiple customers with differing agendas. The answers must at least be attempted because without those, the analyst cannot select the right methods. To be truly effective, analysts must understand how their outputs will eventually be used by the decision makers.¹ These individuals may well be one or more steps removed from the analyst's immediate customer.

An analyst's customers or clients² are those individuals in the enterprise who are in need of advice and guidance in advance of making an identified decision. We make no distinction here between the level at which a customer may be situated in the enterprise, as we know that analysts provide many decision makers with advice, whether they are working on a strategic, tactical, or operational problem.

The analysis process has a clear starting point when either the analyst identifies an issue himself or a customer makes a request. The process also has an end point when the satisfactory product is delivered to the customer. Having said that, good analysts will communicate with their customers throughout the entire process and will engage in many iterations to improve the final product. An open-minded attitude to the task is essential, as is the recognition that improvement can always be achieved. A delicate ego is a distinct hindrance to improving one's analysis.

Defining the Analysis Problem

Customer needs have to be interpreted before they can be acted upon. This is often the foundation to a successful analysis process. Analyst-customer interaction is critical at all stages of the process, but no more so than at the outset; consequently, time spent here will pay dividends later on. A genuine dialog is needed, as experience has shown us that the issuing of instructions in a one-way manner just does not produce effective results.

Most enterprises attempt to identify, relate to, and then satisfy market-place customers' needs and employ customer needs identification processes and (internal) customer relationship management (CRM) techniques. The relationship that an analyst has with his customer and/or decision maker is no less important. Symmetric, two-way communication is needed to identify an enterprise's actual, as opposed to perceived, intelligence needs.

Helping business and competitive analysts are government intelligence models for identifying national-level intelligence requirements, which have been adapted for commercial use. A popular adaptation is the Key Intelligence Topics (KITs) approach advocated by Jan P. Herring or using Key Intelligence Questions (KIQs). Essentially the same activity, this is a semantic terminology change that is preferred by some firms.

Although Herring suggests that KITs are not a simple management tool or a panacea for analysis efforts, the KIT process delivers three essential benefits:

• It facilitates the identification of legitimate intelligence needs and distinguishes between "need-to-know" and "fishing expedition" projects.
• An initial set of KITs provides a proper foundation for an intelligence program, eventually guiding the determination of CI resources, capabilities, and skills required.
• Being a user-driven model, the KIT process provides a foundation for operational planning to meet and understand both organizational and decision makers' dynamic intelligence needs.

In line with our suggestions in the previous paragraphs, the KIT/KIQ process centers on an interactive dialog between analysts and decision makers. A successful senior intelligence practitioner, Dr. Wayne Rosenkrans, Global Intelligence Director at AstraZeneca Pharmaceuticals, supported this view when he said: "My advice usually is to spend whatever time is necessary on that KIT-KIQ process at the very beginning. That is so important, and if you get off on the wrong foot, or on a wild goose chase, you'll have wasted all kinds of time."

Herring classified decision makers' intelligence needs into one of three, not mutually exclusive, categories:

1. Strategic decisions and actions
2. Early-warning topics
3. Descriptions of key marketplace players

The KIT process requires a high-level understanding of the intelligence needs as well as the various types of operations necessary to address them. Herring noted that when done effectively, analysts' use of the KIT process should result not only in identifying the organization's key intelligence needs, but also in creating the critical communication channel necessary to produce credible insight. It also helps to manage expectations.

Fiora, Kalinowski, and others offer help here by suggesting that the analyst assesses each task by addressing each of the following issues:

• Why is this project being proposed?
• Has anyone attempted it before?
• Are there any barriers I should know about?
• What data or information has already been gathered on this topic?
• What analysis process will be needed?
• Who has a stake in the outcome?
• What decisions will be made based on my work?
• How quickly is an answer needed or wanted?
• What are the customer's expectations of me?
• What does the customer want or not want to hear?
• What resources are available to support me?
• Can I do it?
• Is the potential decision worth more than the effort needed?

By managing expectations, analysts can develop mutual respect and trust with their decision makers, and each can better understand the inherent difficulties in the task. Any disconnect that exists between the intelligence analysis planning process and subsequent decision maker could be disastrous for the enterprise.

Identifying the Scope of the Analysis

In the field of BCA, it is crucial to understand the scope of the analysis effort.³ We suggest that the following four main categories are relevant to the vast majority of analytical efforts conducted within profit-seeking enterprises (see Table 2.1).

Table 2.1
Categories of Analysis

Competitors

Talking of his group's analysis work, Wayne Rosenkrans, Global Intelligence Director at AstraZeneca Pharmaceuticals, said: "It's all about predicting competitors, either competitor behavior or competitive environment behavior." The focus of most BCA projects will be on the type of competition and actual competitors present, which requires both art and science. Many new analysts will choose the easy route of letting others tell them what or who the competition is or will use industry and sector classification codes. Although these methods are convenient, they are not necessarily insightful.

Here we adapt the four areas identified by Donald Lehmann and Russell Winer:

• Product/brand level is the narrowest perspective an analyst can take of competition and focuses only on rivals pursuing the same segment with essentially the same offering. An analyst employed by Coca Cola using only this perspective would only look at competing cola brands such as Pepsi, RC Cola, and Virgin Cola.
• Product category level improves this situation by looking at products/services with similar features and attributes. Using our previous example, this would include not only cola brand drinks but other soft drinks such as cherry-flavored colas (Dr. Pepper, Cheerwine), lemon-lime flavored drinks (7-Up, Sprite, Fresca), diet colas, high-caffeine colas, and other varieties of soft drinks.
• Needs-based/generic-level competitors seek to satisfy the same functional need of a customer. Again, using our previous example, this would be the generic need to quench a thirst. Many beverages would be in this category, ranging from water, juice, tea, and coffee to beer, wine, and spirits.
• Share of wallet level is probably the broadest category of competition and considers any other product that a customer might choose to buy instead of ours.

There are many aspects of a competitor's activity that will attract the attention of a passionate analyst, but it is important to always be mindful of the task in hand and the time scale within which it has to be accomplished. While deconstructing a competitor's quarterly/annual report might be fascinating, if it does not help to answer the KIT/KIQ, then that is valuable time wasted.

Environment

Although we provided a detailed chapter in our last book on macro-environmental (STEEP) analysis,⁴ David Montgomery and Charles Weinberg suggest that competitive analysis systems should ideally focus on the following main environmental sectors:

• Competitive—Both current and prospective competitors and the means by which they compete
• Customer—The firm's current customers, potential customers, and competitor's customers
• Economic—Issues such as GNP, inflation, financial markets, interest rates, price regulations, raw material sourcing, fiscal and monetary policy, and exchange rate volatility
• Political, legal, and regulatory—Institutions, governments, pressure group, and stakeholders that influence the "rules of the game"
• Social—Demographics, wealth distribution, attitudes, and social and cultural characteristics that determine the firm's purchasers
• Technological—Current and emerging technologies, product and process innovations, and basic and applied R&D efforts

Analysts often segment the competitive environment into two layers. The macro-environment embraces the largely uncontrollable STEEP factors, and the operational/internal or micro-environment includes the individual, sometimes unique, strengths and weaknesses of the enterprise.

Technology

Technology analysis is principally concerned with the technological base of new or emerging technological capacity.⁵ Much technological analysis focuses on evolution of science and scientific activity, such as basic and applied research conducted within government laboratories, hospitals, innovation parks, and universities. One specialized area of competitive analysis falls under the rubric of Competitive Technology Intelligence (CTI). Brad Ashton and Gary Stacey identified three focal areas for CTI:

• Innovation—Identifying innovation and in particular, disruptive innovation. Decision makers in technologically driven industries experience rapid change. Consequently, new or different technologies will be needed within a short- to medium-term time period to compete.
• Product/process—Attempts to understand the nature and potential results of process improvements. For companies with technology intensive products and/or processes, technology is an important differentiating factor in product features, production steps, or pricing strategy. Those industries that are characterized by frequent product introductions must keep ahead of relevant emerging technologies.
• R&D—Many industries have a high proportion of companies with high R&D intensity. As such, they display higher-than-average ratios of R&D expenditures to sales. They are also firms whose R&D portfolio may contain a high proportion of large, long-range products and that are most active in developing innovation, as well as product and process improvements.

The intelligence needs of decision makers in technology environments will vary by industry and position. Ashton and Stacey also noted that scientists and engineers require detailed technical intelligence on technical objectives, manufacturing methods, R&D approaches, and technical contacts. Technical managers often need analysis related to competitors' program funding plans, IP portfolios, partnership approaches and arrangements, R&D strategies, and technology acquisition or transfer strategies. Senior decision makers are frequently concerned with the nature of emerging or potential business alliances, new product introductions, and technical breakthroughs. Marketing decision makers care about competitive product features, product sales, product benefits, and cost/performance/price insights. Last but certainly not least, public policy makers and regulators require analysis to help them establish reasonable policy and regulatory requirements. A flexible approach is therefore needed to respond to the needs of each and every customer.

Decision Location and Decision Maker

Management decisions differ depending on the level of responsibility on which they are made and who makes them. A brief overview is helpful here to put this into context:

• Strategic decisions have significant resource allocation impact, set the precedents or tone for decisions further down the organization, and have a potentially material effect on the organization's competitiveness within its marketplace. They are made by top managers and affect the business direction of an organization.
• Tactical decisions are less pervasive than strategic ones and involve formulating and implementing policies for the organization. They are usually made by mid-level managers and often materially affect functions such as marketing, accounting, production, a business unit, or product, as opposed to the entire organization. Tactical decisions generally have lower resource implications than strategic decisions and are typically semi-structured.
• Operational decisions support the day-to-day decisions needed to operate the organization and take effect for a few days or weeks. Typically made by a lower-level manager, operational decisions are distinct from tactical and strategic decisions in that they are made frequently and often "on the fly." Operational decisions tend to be highly structured, often with well-defined procedure manuals.

Analysts must remain focused on the critical intelligence needs (CINs) of the decision maker, whatever their hierarchical level. Whether they are senior executives at the business unit or corporate level, middle managers from functional areas, or front-line personnel, each has different needs for the outputs the analyst provides.

The entire issue of geographic complexities can also be added here. In the past, the key focus of analysis would be constrained within national or nation-state boundaries. Today's enterprises increasingly compete in environments that require the analyst to consider all forms of geographical levels of competition, including national, multi-national, and global formats.

Multi-point competition, where a diversified company will compete across a variety of market sectors, is increasingly commonplace. Consequently, the analyst will need to examine how a business can best prepare to simultaneously compete against dozens of other businesses, across multiple segments, and in multiple countries. If a firm competes in fifteen different countries, there may be fifteen separate sets of competitive contexts and rules to which it must conform. When considering all these perspectives, it is perhaps not surprising that the analysis process is seen as a highly skilled and highly complex undertaking.

Intelligence Analysis at Differing Organizational Levels

Intelligence analysis takes place at multiple levels within an organization. The three most common are strategic, tactical, and operational. These match the decision location and decision-maker components discussed previously and as such, Table 2.2 gives guidance on the typical techniques used for intelligence analysis at each level.

Table 2.2
Levels of Intelligence Analysis

Strategic Intelligence Analysis (SIA)

SIA is arguably the most vital form of intelligence because it provides a framework within which other forms of intelligence collection and analysis take place. It helps to discern and make sense of important trends, to identify and extract patterns that would otherwise not be visible, and to provide an overall picture of the evolving opportunity and threat environment. SIA also provides guidance for tactical and operational assessments, and work done at these levels in turn helps to shape the strategic intelligence focus. As strategic analytic methodologies mature, they will also offer the basis for predictive or anticipatory assessments that can serve to provide warning of potential high-impact activities.

Treatments on the kind of specific techniques and tools that the business analyst might use exist,⁶ but generic analytical initiatives that would fall under the rubric of strategic intelligence analysis include the following:

• Opportunity and Threat (O&T) Assessments—Used to assess the levels of dependence and vulnerabilities of critical issues, competitive changes that could cause significant impact, and the likelihood of such activities taking place.
• Sector/Competitor Assessments—Focus on emerging or threatening competitors that provide strong potential for impacting the competitive terrain.
• Trend Analyses—Baseline assessments to better recognize departures from current practice, especially those that shape the industry's future.
• Anomaly Detection—Requires systematic "environmental scanning," as well as the coalescing of tactical and operational intelligence reports that identify and highlight specific deviations from the norm.
• Impact Assessments—The macro-level view taken in SIA offers a good approach for assessing probable cascade effects of threatening competitive action and activity.

Tactical Intelligence Analysis (TIA)

TIA is a necessary and important complement to work done at the strategic level. It is the natural link between macro- and micro-level analysis. Although SIA provides the framework for TIA, these assessments in turn feed SIA. With a dynamic symbiotic relationship between the two, mutual strength is derived.

Typical techniques used in TIA are the following:

• Cluster and Pattern Analysis—Identifies the use of particular marketplace attack methods, commonalities of targets, and attempts to build profiles of competitors.
• Stimulus-Response Analysis—Identifies actions that could be taken by competitors in response to specific events. This analysis could be used both proactively to develop warnings or reactively to design future tactics.
• Value Constellation Analysis—Identifies the key stakeholders, important partners, allies, joint venture prospects, outsourcing potential, and agents that a company could utilize.

Operational Intelligence Analysis (OIA)

Unlike its two more broadly based IA cousins, OIA is often event-centric and single-case-oriented. It provides more immediate but lesser-lasting benefits and typically involves technological assessments of methods used for marketplace battles or investigations of competitive threats. It is frequently focused on helping the analyst understand in real-time a particular event; such as a competitor who is attempting to perform competitive intelligence efforts of your enterprise. This can be especially helpful for counter-intelligence and can keep your company's efforts from being prematurely disclosed.

An important component of OIA is vulnerability analysis and recommending how these can be minimized or eliminated. Vulnerability analysis can be used to look both at the enterprise's marketplace vulnerabilities as well as, more tactically, the competitive intelligence process being employed.

Evaluating the Inputs to Analysis

It is critical that analysts can credibly evaluate their data and information. In weighing the credibility of inputs, they have to consider the nature of their sources and reliability. In the intelligence community, this is often described as the process of determining "bona fides."

There are a series of questions that should be kept in mind when examining sources, as follows:

• Reliability—Can the source of the information be trusted to deliver reliable information to the analyst? Does the source have a "track record" for delivering credible inputs? The U.S. military uses the following information evaluation scale to assess reliability:⁷
  • "A" Completely reliable
  • "B" Usually reliable
  • "C" Fairly reliable
  • "D" Not usually reliable
  • "E" Unreliable
  • "F" Reliability unknown
• Accuracy—Have the data inputs been captured "first hand," or have they been filtered? Is there any reason to think that there might be any deception involved? Is the source able to communicate the data precisely? Is the source truly competent and knowledgeable about the information they provide? Do they have a known vested interest, hidden agenda, or other bias that might impact the information's accuracy? Can the source's data be verified by other sources or otherwise triangulated?
• Availability—Unavailable data is not going to help the analyst do his or her work. It is critical that the analyst does not rely solely on one source. Are there credible substitutes that can be used? Can sources be accessed quickly?
• Ease of access—What is the financial opportunity and time cost to access the source? Is this the best use of limited resources, or can equivalent data be gained with lesser expenditure? Does the source speak the right language, or will translation be needed? If so, what are the dangers of misunderstanding and/or incorrect reporting?

Once analysts have addressed their sources, they need to make sense of what has been gathered. The next section talks about the preliminary stage in sense-making—that of qualifying the raw data and information before it is subjected to analytical processing.

Making Sense of the Analysis

Analysts ultimately respond to decision makers' needs for knowledge. This brings up the key question of what we mean by the word "knowledge." In this book, we conceptualize knowledge as that which people in the enterprise either know they know or perceive that they know. Knowledge is definitely an asset of the enterprise and is referred to by analysts as evidence, the basis upon which they can perform further assessment.

Knowledge can be further broken down into five interrelated elements, all of which are important for analysts to understand in carrying out their responsibilities.⁸

Facts

Verified information, something known to exist or to have occurred. These are unambiguously true statements and are known to be so. Facts come in any form and will be found among virtually any source of data that enters an employee's awareness, or the enterprise's communication and information systems. It is surprising how few enterprises subject their collected data and information to fact checking and verification processes. This becomes even more important for strategy decision-making purposes because many of the facts about competitors and competition are time-sensitive. What may be accurate today may be dangerously incorrect tomorrow.

Perceptions

Perceptions are impressions or opinions that fall short of being facts, but which are supported to some extent by underlying data or logic. These are often expressed as thoughts or opinions in language such as: "I think that . . ." or "My view is . . ." It is important for the analyst to subject these thoughts and opinions to tests to establish which can be converted into facts and which have to remain as perceptions for the time being. There is nothing wrong in factoring perceptions into the analysis process, just as long as everybody knows that this is what they are. The error comes when perceptions are mistakenly regarded and treated as facts when they are not. The use of perceptions is perhaps the most exciting element to subject to subsequent analysis, especially when using scenario analysis, war-gaming, what-if analysis, and other such future-oriented techniques.

Beliefs

Beliefs are often drawn from a mix of facts and perceptions and commonly describe cause-effect relationships. They can be either explicit or implicit, but they too need to be subjected to verification and justification. Beliefs often color the way individuals understand their world and the way in which they think about the future. Therefore, it becomes critical in the analysis process for beliefs to be aired and made transparent to those individuals who are key parts of the process, whether these individuals are data gatherers, analysts, or decision makers.

Assumptions

This is the knowledge that individuals take for granted. These can come in the form of any of the previously described categories and may refer to things that have occurred in the past, present, or can be fairly safely predicted as going to happen in the future. Explicit assumptions are those that are consciously adopted by the analyst, are well understood, and are shared. Implicit assumptions are those that individuals in the analysis process do not consciously elicit, share, or articulate and may not even be aware of. Valuable as they are, as with perceptions and beliefs, assumptions need to be consistently and constantly challenged to reflect changing situations and a shifting competitive landscape.

Projections

Projections are composed of a mixture of facts, perceptions, beliefs, and assumptions. They are justified or substantiated judgments about the future. It is again important that the analyst be able to powerfully defend or justify their projections as they become a critical part of the knowledge base underlying the decisions made.

Synthesis

Having identified the type of knowledge in place, the analyst can proceed with greater confidence toward a high-quality output. Qualified inputs are then subjected to the real heart of analysis—the thinking processes, sifting, synthesis, induction, deduction, abduction, experimentation, mathematical conceptualization, experimentation, research, application of methods, techniques, and a vast array of other activities all designed to generate unique and actionable insights.

An important element to the analysis process is that of infrastructure support, or those technological parts of the analysis process that are complementary to the analysis and analysis process itself. The following section provides an overview of the key facets of analysis infrastructure that can encourage and permit the analyst to produce their best work.

Infrastructure to Support the Analysis Process

One facet of analysis infrastructure that has become more prominent in recent years is the growth of information systems support. When we refer to information systems in this book, we are specifically referring to combinations of software and hardware that are utilized to support information gathering, classification, synthesis, and dissemination. These are often referred to under alternative rubrics such as management information systems (MIS), decision support systems (DSS), enterprise information systems (EIS), enterprise resource planning systems (ERP), executive information systems (ExIS), business intelligence systems (BI), marketing information systems (MkIS), and knowledge management systems (KM), among others. Firms have also developed information support systems to which they refer by their own unique names. The function of each remains the same—providing support for the analysis process and enhancing sense-making.

The entire subject of system support would require book-length treatments to properly describe what we know and their ability to support the business and competitive analysis process. This book focuses on carrying out business and competitive analysis, and as such, we highlight what we deem to be the key elements that analysts should consider as they use these systems to support them in their work.

Intelligence Solutions

Unfortunately for most of the readers of this book, competitive analysts generally have not been well supported by the information systems introduced by organizations. Most were not purpose-designed to support BCA tasks, and as such, they have not and maybe cannot replace or substitute for the human cognitive and mental processes that analysts uniquely develop and employ.⁹

Successful analysts require the support of dedicated information systems, both formal, informal, human, and technical. These systems can, among other things, allow the manipulation of data for multi-dimensional visualization of phenomena and for mapping relationships. Effective systems also operate in real-time and have filters to make sure that the data and information the analyst works with is traceable, to provide the ability to assess validity and reliability.

Analysts are ordinarily uninvolved in the selection of management information systems and usually have to work with what is already there in the larger enterprise or what can be afforded. Fortunately, there has been an upswing in the nature and number of intelligence software solutions. Intelligence solutions have become one of the hotter topics in today's boardrooms. Recognizing their needs in this area, corporations have energized a burgeoning market for intelligence consulting, software, and services solutions.

Limitations of Intelligence Solutions

Essentially, decision makers want concise information they can act on. Typically, they get bits and pieces of data or stacks of undigested reports, leaving them to fill in the blanks. Effective intelligence solutions are designed not to create more information, but to create better information. Used properly, intelligence solutions can reduce the amount of information being transmitted; nevertheless, it is wise to be aware of their limitations.

Some commercial intelligence solutions have attempted to provide the "holy grail" of intelligence with artificial intelligence, knowledge trees, or executive decision support systems, which take the inputted information and perform a number of "tests" on it to alert the analyst to when certain data parameters have been triggered. Virtually all intelligence-related software provides the basic means for organizing and categorizing information, but precious few take it to the next step to where inferences can be drawn or insight achieved.

Nearly all commercial intelligence solutions fall short in the qualitative arena, which is what intelligence analysis has traditionally been about. There is little evidence of intelligence software that provides the kind of discovery through spatial, timeline, and relationship analysis that trained analysts routinely perform.¹⁰ Analysis of soft data means seeing just around the corner, appreciating why a rival made certain visits to different competitors or provincial officials, or the competitor's CEO has voiced certain views. Few intelligence solutions are able to look around the corner, especially when that corner is on a different street or in a country where the written and spoken language differs markedly from the analyst's enterprise.

What the software industry generally has not grasped is that competitive intelligence is traditionally defined as information that has been analyzed to the point where a person can make a decision. Software generally does not analyze. It can perform rudimentary and even sophisticated statistical analysis on convenient data, but still requires interpretation. Reality dictates that most analysis is done on the less convenient data that employees encounter but mostly do not recognize to be an important piece of the intelligence puzzle. This highlights the importance of combining human/expert knowledge and organizational data with a sophisticated, purpose written, software product, to form an effective intelligence solution. These things can rarely be bought "off-the-shelf."

Organizations also use groupware or intranet-based technologies to organize and categorize the internal expertise of a company. These packages generally do an unsatisfactory job of relating one source to another and providing additional leads to the analyst who needs to locate another bit of data. Most packages fail to organize the data and simply generate long lists, crudely ranking the inputs by relevancy.

Managing the Internal Network

For the analysis process to succeed, analysts must be part of established networks aimed at facilitating intelligence sharing throughout the organization. This allows the analyst to access individuals who can provide bits of data or information that can often be the "missing piece" in their emergent analytic puzzles. Even if individuals in the internal network do not have the critical information, they can often point out those individuals who may have it. Other functions, such as human resources management, public relations, business development, and marketing and sales, also obtain important pieces of intelligence that needs to be shared.

For analysts in smaller enterprises or organizations that are lacking the financial resources to set up a dedicated analysis function, networks can be the most powerful way to accomplish their roles. Many smaller companies have established their intelligence networks with the purpose of providing a source for best practices, a repository of particular forms or types of data, or for helping to identify other key sources.¹¹ Figure 2.2 illustrates how an internal network can begin to take shape and the functions from where network participants are frequently drawn.

Figure 2.2 Participants of internal networks

Analysts use their network partners to bounce their ideas, to test their insights, or to communicate initial findings. This helps to provide preliminary feedback on how the decision maker may react to the news. It is critical that these contacts take place in a mutually beneficial fashion, as the creation of "two-way" communication among members of a network is one of the best ways for ensuring that the network can be used for maximal benefit.¹²

When internal networks are created electronically and exist primarily in a digital format, it is critical that participation and contributions can be made conveniently and quickly. If they are not, then members will not use it.¹³

Managing the External Network

External networks are also a key part of the analyst's contact universe. It is vital that analysts establish, maintain, and constantly qualify and update a set of contacts outside the firm, as certain types of research cannot be accomplished without them. Analysts must be able to access industry experts, industry associations, industry commentators, stock analysts, government experts, government departments, civil servants, respected journalists, subject specialists, and broadcasters to obtain needed data or access to other important stakeholders. Analysts that network with professionals from other non-competing or consulting firms can gain insight into effective practices.¹⁴ It is important to note here that networks may not be viewed as providing immediate and obvious benefits. John Shumadine, Director of Competitive Intelligence for Deloitte and Touche, states it clearly when he says: "But it sometimes takes years to develop a Rolodex within your own industry, a repository of information and a collection of individuals that you can network with. There's not an immediate payback." Nevertheless, these networks are typically essential to long-term business and competitive analysis success.

Proactivity, Efficiency, and Perpetual Learning

Analysts need to be highly proactive, not only delivering the needed intelligence for their decision makers today, but also figuring out ways by which they can augment their organization's analysis capabilities for the future.

Developing increased expertise at searching, classifying, qualifying, and organizing data can be a very beneficial time saver for analysts. They also need to develop the ability to cut through the "noise" and get to the heart of an issue. They need to be able to remove the unnecessary trappings and keep only the essential bits that make the difference. The Pareto principle, whereby 20% of the information will provide 80% of the insight, nearly always rings true in analysis work. Better analysts also hone their project and time management skills over time. We know of few long-term analysts who are poor at this, and, in reality, it is one of the key competencies that effective analysts must eventually display.

Another key task for the analyst is to learn how to quickly deal with the flood of data and information that comes in on a daily basis. An analogy is considering how a quick keyword entry using any one of the many search engines available on the World Wide Web generates thousands of "hits" and how only a few of these will actually be relevant to the searcher's goals. By systematically capturing learning from prior projects, integrating their efforts across projects, building the capabilities of their supporting information systems, leveraging their networks more effectively, and last but not least, by educating their customers in ways that improves their mutual relationship, significant advances in business and competitive analysis can be made.

Summary

L.M. Fuld noted that three elements were part of the successful competitive intelligence operations that he had examined:

• Constancy—Information is gathered and analyzed constantly, not just during the traditional strategic planning cycle or on a just-in-time basis.
• Longevity—Intelligence program investments were for the longer term. Six months, one year, or even two years may not be enough to prove the worth of a program. The most successful and enduring intelligence systems had taken three to five years to mature.
• Involvement—The more people saw the development and use of intelligence as a key component of their jobs, the more readily available the intelligence was and the more it was used.

These three facets of success are not commonly experienced by competitive analysts. Most claim that they are not properly supported in terms of information systems, access to key decision makers, access to data or information, or most importantly, the time needed to effectively perform their tasks. Properly allocating resources is critical if the analysis function and individuals doing it are to be effective.

In examining analysis practiced in a number of organizations in the last twenty years, we have identified a set of characteristics that are present in those organizations that have maintained the greatest longevity. These are reflected in Fleisher and Bensoussan's 10 Commandments for Business and Competitive Analysis, and they provide a beneficial summary to the lessons that should be captured by analysts and their superiors from reading this chapter.

Fleisher and Bensoussan's 10 Commandments for Business and Competitive Analysis

1. Analysis should underlie and be an integral part underlying every one of your organization's important competitive and strategic decisions.
2. Decision-making customers shall use only analyzed data to direct competitive decision-making, planning, and subsequent actions.
3. Analytic processes should be performed in a timely manner and products delivered to clients well in advance of their need to use them in decision-making.
4. Analytic products must contain conclusions and recommendations effectively presented in the optimal format to customers for their consideration.
5. Analysts shall not confuse data compilations, digests, newsletters, static portals, or summaries with analysis.
6. Analysis must be FAROUT© and able to be relied upon to strike the best balance among these elements.
7. Analysis outputs (products, advice, services, etc.) will be negotiated based on the client's specification to ensure that the KIT is achievable.
8. Analysis should reflect all relevant data available, from all legitimate/legal means and sources.
9. Analysis should utilize the best and most current methods, tools, and techniques available (like the ones included in this book and beyond).
10. Analysis must be regularly evaluated by both its producers as well as consumers for its contribution to your organization's mission and goals.

References

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Endnotes

¹ McGonagle and Vella, 2003.

² Similar to actual practice, we will use these terms interchangeably in this book, recognizing that there are subtle differences between the two terms.

³ Clark, 2004.

⁴ See Fleisher and Bensoussan, 2003, Chapter 17.

⁵ See, in particular, Chapters 21 through 25 of this book, which focus on technological phenomena.

⁶ Fleisher and Bensoussan, 2003.

⁷ Page, 1996.

⁸ Belkine, 1996; Fahey, 1999; O'Connor, 2003; Senge, 1990.

⁹ Fuld, 2003.

¹⁰ Bouthillier and Shearer, 2003; Fuld and Sawka, 2000; Nikkel, 2002; Ringdahl, 2001.

¹¹ Skryzowski, 2003.

¹² Skryzowski, 2003.

¹³ Chender, 2006.

¹⁴ McGonagle and Vella, 2003.