The integration of intelligence analysis into LIS education.
As more and more social media (e.g., Facebook, Twitter) and smart technologies (e.g., iPad, Kindle) are developed, the society in which we live has been facing extraordinary changes and so does LIS education. Many, if not all, LIS schools have been in the process of redefining themselves and we have witnessed the emergence of the iSchools. Traditional library science programs have reviewed and modified their curriculum to remain abreast with the changing demands of the job market. Many LIS graduates choose not to work in a traditional library context but in an environment like what Cronin described the "hinterland" for information professionals in which they do intensive information work utilizing LIS skills but outside a library setting (Cronin et al., 1993; Hohhof & Chitwood, 2000; Shelfer & Goodrum, 2000).
One such area could be intelligence analysis (IA), which includes a spectrum of analytical and judgmental activities required for processing huge amount of information and for transforming it into reports and documents that can be used by decision makers to understand strategic issues (Intelligence.gov, 2010; Johnston, 2005; Montgomery et al., 1979). In 2009, the intelligence analyst was ranked the 9th out of 100 best jobs in America (CNN.com, 2010). By intelligence analyst, we mean those professionals who practice government, military or criminal justice intelligence for national security purposes and who practice competitive or business intelligence for organizational purposes. During the past 20 years, many calls have been made for an integration of IA into LIS education (Cronin & Davenport, 1993; Prescott, 1991; Reid, 2010a). These requests reached their threshold when an IA meeting was organized by the FLICC (Federal Library and Information Center Committee) and the Unites States Department of Justice (DOJ) in June 2010. One of the major intents of that meeting was "to discuss the integration of analysis skills into LIS and I-School Curricula and to highlight intelligence analysis as another information career" (ALISE.org, 2010).
While we try to bring IA and LIS together, two setbacks are bound to happen. First, theoretical guidance for this integration is lacking. Research into the connections between intelligence services and LIS is almost non-existent. There is neither statistical evidence nor empirically supported theory available to illustrate which intelligence competencies are relevant to LIS skills and to what extent. Second, IA per se is still at an early stage of maturity in term of professionalization, and a debate about whether IA is a craft or a profession continues today (Marrin, 2005, 2009).
One central question this article attempts to address is whether IA can be integrated into LIS education. In the next section we will first provide working definitions to various key terms such as intelligence and intelligence work. Then we will review the literature that relates to this possible integration, attempting to comb out some interrelations between IA and LIS. Following that section, we will survey the current status of IA education from multiple perspectives, including LIS. Finally, we will discuss challenges and opportunities entailed in preparations for this likely convergence.
Throughout this text, a series of key notions are defined in an operative manner.
* Intelligence represents a capacity to foreknow emerging realities or to discover the information not publicly available employing systematic approaches. It is often used as the basis for decision making and/or policy development (Jin, 2008; Lowenthal, 2006).
* Intelligence work refers to those activities, covert or overt, to acquire intelligence as defined above. Throughout this text, it is interchangeably used with intelligence practice or intelligence service.
* Intelligence analysis focuses on production of intelligence. It is "the application of individual and collective cognitive methods to weigh data and test hypotheses within a social-cultural context" (Johnston, 2005, p.4). The analytical process itself may not necessarily be involved in covert activities.
* Intelligence analyst refers to the professional who conducts IA at federal, state, or local government agencies or commercial/non-commercial organizations.
* Intelligence competency refers to those knowledge and skill requirements or the qualities to perform intelligence work.
* LIS education refers to those LIS programs accredited by the American Library Association (ALA).
Interconnections Between IA and LIS
If we hope to integrate IA into LIS curricula, we must first understand how these two domains relate to one another. To achieve that purpose, a body of relevant literature was identified. They were then reviewed in two thematic focuses: (1) descriptions of intelligence practices; and (2) intelligence work discussed in the context of LIS.
Descriptions of Intelligence Work
Intelligence work is often discussed in the following subject areas: history, political science, international studies, public administration, and recently business administration (Marrin, 2009; Williams & Lipetz, 2005). Although there are various ways to conceptualize intelligence activities, they can be divided into five broad categories: planning, collection, processing, analysis, and dissemination. They represent holistically an intelligence development continuum or a continuous cycle (see Figure 1), each category feeding into the next one (Bouthillier & Shearer, 2003).
[FIGURE 1 OMITTED]
Intelligence work is also highly context-dependent. The competitive intelligence practice run by a Fortune 500 company would not be the same as what the U.S. Central Intelligence Agency (CIA) does. A government may also, by scope, have different agencies to oversee foreign, domestic, military, and economic intelligence services. Different types of intelligence work will lead to different practice approaches in terms of targets, goals, objectives, information sources, devices, and techniques. Foreign intelligence focuses on gathering information about foreign governments or organizations. Military intelligence puts emphasis on information about capabilities, intentions, activities, weaponry, and deployment of rival military forces (Lefebvre, 2004; Warner, 2005). Law enforcement intelligence may deal with threats like narcotics smuggling, international terrorism, or other types of organized crime (Shulsky & Schmitt, 2002; Williams & Lipetz, 2005). While foreign, military, or law enforcement intelligence may use secrecy and infiltration methods to obtain inside information, competitive or business intelligence allows only using public domains to find and develop insights and strictly prohibits from employing unethical actions such as misrepresentation (Fleisher & Blenkhorn, 2001; Lefebvre, 2004).
Despite the disparities, IA is a vital part of any intelligence work regardless of context. As the process of analyzing available information to make judgments about the future, IA has some universal characteristics that exist across different intelligence disciplines (see Appendix A). The first such characteristic is related to its extrapolative nature: IA seeks to illuminate the unknown based on what is known (Heuer, 1999). The known can be limited, fragmentary, and susceptible to widely divergent interpretations (Shulsky & Schmitt, 2002). The intelligence analyst may have to deal with "new problems at an early stage when the evidence is very fuzzy indeed" (Heuer, 1999, p.15).
Second, most IA follows a simple three-step methodology to handle information: (1) to describe what is known, (2) to highlight the interrelationships that form the basis for the judgments, and (3) to offer a forecast (Lefebvre, 2004). Batty (2005) summarizes the procedures as input (seeking for and receiving raw data), processing (analyzing and synthesizing data into an organized picture), and output (empowering the client to respond to a requirement of his environment). Ghoshal and Westney (1991) reveal that competitive intelligence analysts follow similar steps: (1) synthesizing information about the current situation, (2) creating hypotheses of possible scenarios, and (3) testing hypotheses.
Third, the information environment of IA is always fuzzy. Heuer (1999) describes that intelligence analysts commonly work with incomplete and ambiguous information from multiple sources, such as "newspapers and wire services, observations by embassy officers, reports from controlled agents and casual informants, information exchange with foreign governments, photo reconnaissance, and communications intelligence" (p.115). Recently, open sources on the Internet (e.g., websites, blogs, and podcasts) play an increasingly important role (Thompson, 2006). But because each source has its own limitations, biases, inadequacies, and degree of reliability, information gathered from various sources may be inconsistent, inconclusive or even conflicting with one another (Heuer, 1999; Lefebvre, 2004).
Nevertheless, our knowledge about how intelligence analysts work is still limited. Published scientific research about IA has been rare. The intelligence community is often "shielded from public view" (Johnston, 2005, p.xvi) and intelligence analysts are always wary of being publicized (Ganesh et al., 2003). Warner (2006) even observes that intelligence practices by nature resist scholarship. To date, only a handful of empirical studies about IA have been conducted. Montgomery et al. (1979) completed 117 structured interviews with various intelligence analysts and used the results to develop a model of the underlying cognitive process of IA. The researchers concluded that IA is an internal, conceptually driven process, rather than an external, data-driven one. This research has been one of the most complete, empirical-grounded studies about IA (Lefebvre, 2004).
Ghoshal and Westney (1991) interviewed a cohort of more than 150 competitive intelligence analysts, managers, and users in 1986 in three large multinational companies. They observed, recorded and classified work activities of competitive intelligence analysts, and found that many analysts devoted most of their time to data management activities, such as information acquisition, classification, storage, retrieval, editing, etc., rather than to analytical activities (e.g., synthesis, hypothesis, and assumption building and testing).
Johnston's (2005) ethnographic study aimed to identify variables that affect IA. During a period of two years, he conducted 485 semi-structured interviews with intelligence professionals, academics, and researchers in the intelligence community; observed 325 individual intelligence analysts performing their specific tasks; participated in a series of meetings, trainings, and focus groups about IA. That study identified more than 130 variables grouped into four hierarchical categories: (1) systemic (i.e., organizational structure, culture, resources and incentives, technology, budget, etc), (2) systematic (i.e., user requirements, operations, information archive, analytical methodology, reporting, etc.), (3) idiosyncratic (i.e., affiliation, psychology, education, training, readiness, etc.), and (4) communicative (i.e., formal, informal, etc.).
More recently, using interview and diary methods, Jin (2008) studied how 28 competitive intelligence professionals work in real life. He identified two major working groups in the competitive intelligence context. One group included mostly business professionals represented by MBA graduates. The other group comprised mostly information professionals represented by MLIS graduates. Their working tasks and activities were found to be different from one another. While information professionals focus on information monitoring, acquisition and management, business professionals emphasize information analysis, interpretation and forecasting. There seems to be an inherent tension between the two groups. Business professionals tend to question, and even mistrust, the qualifications of information professionals in handling competitive intelligence work, because they regard it as a specific, business domain, not a general, information domain. In contrast, information professionals are confused about their role in competitive intelligence work. They see intelligence activities embedded in their daily job but at the operational level, they felt estranged from those business professionals. This tension itself reflects deep differences in ideological agendas between the two groups.
Intelligence Work Discussed in the Context of LIS
Two sources were identified where intelligence work is discussed in the context of LIS. One is a book titled Covert and Overt: Recollecting and Connecting Intelligence Service and Information Science. To our best knowledge, it is so far the only monograph exploring the relationship between LIS and intelligence work. The other source is the Annual Review of Information Science and Technology (ARIST), which has published five chapters reviewing various intelligence practices from a LIS perspective. Based on these sources, together with other items in the literature, we compiled Table 1 which offers a systemic comparison of the work domains of IA and LIS using seven criteria: job title, information environment, data form, trustworthiness of data, data processing style, urgency, and typical activities. We will discuss each criterion in detail below.
In the IA domain, analyst is the most commonly used job title. In contrast, given its broadness and definitional ambiguity (Lipetz, 2005), LIS domain has a variety of job titles, depending on their specific duties and the context in which they work. Historically, these two work domains have intersected since WWII. Thereafter LIS has been regarded as a major force that shaped the organization of information in various intelligence agencies (Cronin, 2005). Burke (2005) remarks that the flow of librarians and information scientists into intelligence work is a natural phenomenon because their skills and experience have greatly enhanced the quality of information management--processing and storing data and imposing order to the nebulae of individual facts and evidence--in intelligence services.
For IA, the information environment is often unstable. This unstableness is manifest in three ways. First, there is an unusually diverse set of sources from which data may arrive massively and unpredictably. Second, key information is often missing and the analyst works routinely with incomplete data. Third, the environment is always filled with ambiguity and uncertainty. Contrarily, LIS professionals work in a relatively stable environment where data arrive regularly from reliable sources with a high degree of predictability (Batty, 2005; Heuer, 1999).
While intelligence analysts and LIS professionals are all accustomed to dealing with multiple data forms, the condition of those forms can vary. LIS professionals usually interact with incoming data with steady forms such as books, journals, articles, technical reports, statistics, records and other materials (Batty, 2005). They are static, to some extent, because they have already undergone some organization by their producers. Except some artifacts or items that archivists manipulate, most of the above are well structured, so that some bibliographic control can be developed. By contrast, while intelligence analysts monitor and receive structured documents (e.g., foreign and domestic media and press, reports from embassy or consular, press releases, etc.), they may have to cope with lots of unsteady, real-time data in forms of image, sound, video, or other non-text communications originated from sources like satellite-based platforms, sensory devices, and so forth. Such non-text data can be dynamic and constantly changing. When captured or intercepted by intelligence workers, they may have been distorted for many reasons ranging from technical problems (e.g., equipment malfunction, faulty hardware) to interference from the adversary (e.g., encryption, intentional deception) (Shulsky & Schmitt, 2002). Even with textual forms, intelligence data can be unstructured and elusive to outsiders.
Trustworthiness of Data
LIS professionals usually work with public information sources. They rarely encounter intentional deception, and the information that they use is usually of a high degree of trustworthiness. In contrast, intelligence analysts use a much wider set of information sources including public and private, human and technological channels where information can be vulnerable to manipulation and deception (Warner, 2005). They may cope with disinformation and misinformation sent by adversaries or competitors almost every day (Lefebvre, 2004). Johnston (2005) remarks that in any case of IA, "deception is the rule; the validity of the data is always in doubt" (p. 35). Given the low trustworthiness, one of major routine jobs for intelligence analysts is to filter and evaluate data by means of extensive clarifications, verifications, and validations. Another source of untrustworthiness can be misjudgment. Chartrand (2005), an intelligence officer, once was called upon to verify a suspicious submarine spotted by a reconnaissance plane. Eventually the target was proved to be a submerged rock. He twits that "this may sound ridiculous ... but this is the kind of thing that does happen in intelligence work" (p. 34).
Data Processing Style
Intelligence analysts often work with ambiguous and fuzzy situations. The way they select and organize data relies much on their "conscious and subconscious assumptions and preconceptions" (Heuer, 1999, p. 41). This intuitive approach seems to be natural when the factor of time pressure is considered. When key information is missing but a judgment must be made, the analyst has no choice but to lean heavily on, and be biased by, his prior experiences, subject knowledge, imagination, and creativity (Marshall, 2005). Additionally, because intelligence analysts are accustomed to taking intentional deception into consideration, when processing data, they are particularly interested in "anomalies'" and "outliers" instead of "central tendencies of distribution" (Johnston, 2005, p. 35). By comparison, LIS professionals may have less time pressure than intelligence analysts. They have been accustomed to a conventional approach by which data is processed formally with objective standards such as various cataloguing tools and statistical models (Batty, 2005).
Time is always a key dimension for human activity. Davies (2002) observes that the demand for timeliness is an essential challenge imposed upon intelligence analysts. Garst (1989) argues that intelligence could be a life and death profession and it is more time sensitive than in other domains. Lefebvre (2004) echoes that intelligence analysts usually work under considerable time constraints set by their intelligence consumers. One example can be found in Montgomery et al (1980), where user requirements for intelligence products are categorized into three groups by urgency: (1) immediately, (2) twenty-four-hour, and (3) basic. LIS professionals, in contrast, may also have deadlines to meet, particularly for those who work in corporate settings. Nevertheless, the time constraints that they face may be less hectic than that of intelligence analysts. Batty (2005) surveys that "[librarians] and information scientists, given the advantage of time and less intransigent demands, have developed general methods and structures by which they can potentially organize many assemblies of ideas, facts, and suppositions in such a way as to respond with appropriate information to almost any question" (p. 29). In short, LIS professionals may have more discretion in scheduling their tasks than intelligence analysts.
The most frequently mentioned action verbs for IA and LIS domains were identified and extracted from Williams & Lipetz (2005). They are listed in an alphabetical order in Table 3. When synonyms are involved, they are clustered together but separated by slashes. Most of these verbs can speak for themselves. But while the activity of "organizing" appears in both columns, it is evident that these two work domains may entail activities with different orientations. For example, IA emphasizes assessing and synthesizing the data to deduce "so what," while LIS focuses on imposing order to this chaotic world through the lens of information and creating maximal access for the general public.
The above comparison is rather an early presentation of a way of thinking about those two domains. It is descriptive by nature, not prescriptive. Beyond that comparison, considering that the horizons of LIS have expanded dramatically since the appearance of the Internet, intelligence work has attracted lots of interest from LIS researchers and educators, particularly after the attack of 9/11. Cronin and Davenport (1993) systematically review the concept of social intelligence. They argue that intelligence study should be treated as a new lens or an extension of traditional LIS because as social interactions have become increasingly popular, core intelligence skills, such as sourcing, validating, collating, analyzing, and synthesizing, would be essential for future information professionals. Bergeron and Hiller (2002) thoroughly revisit the concept of competitive intelligence from the LIS perspective. They use Taylor's Value-added Spectrum as a model to explore the role of information professionals in the context of competitive intelligence. Beyond social and business circles, Davies (2002) focuses on the concepts of intelligence, information technology, and information warfare. He points out that traditional LIS research and education have chronically emphasized overt, public information phenomena but have often neglected covert, secret information phenomena. Similarly, Cronin (2005) criticizes this kind of short vision and calls for a closer connection between the communities of intelligence and information science, so that information professionals can play more actively than before in intelligence sharing and netwar situations. Indeed, when open source information and grey literature proliferate both online and offline (Marling, 2005), many LIS skills such as signal indexing and word/character identification may be invaluable for the intelligence community (Williams & Lipetz, 2005). For example, Chen and Xu (2006) thoroughly examine the topic of intelligence and security informatics (ISI). From an information science and technology perspective, they list what ISI can contribute to the intelligence community through techniques such as knowledge discovery. At least two studies (Chen et al., 2008; Qin et al., 2007) have been found using the same rationale to study terrorism activities on the Web.
Interconnections Between IA and LIS
A literature that specifically addresses the nexus between IA and LIS is hard to acquire. Williams and Lipetz (2005) made the first effort to explore this area, but most of the articles in their book are historically based, portraying how LIS has played an important role in shaping intelligence practice since WWII. Almost none of the articles provide a formal, coherent description of relations between these two work domains, except Batty (2005) who uses the analogy of "two men in a boat" (p. 25) to describe the interrelation.
[FIGURE 2 OMITTED]
We postulate that IA and LIS have a complementary relation in the context of intelligence practice. While LIS cares about how to retrieve, organize and simplify massive datasets and make them accessible, IA tends to focus on how to collate, connect, and interpret the gathered data. If we blend those typical verbs listed in Table 1 into the intelligence development continuum (Figure 1), then we will obtain Figure 2. Specifically, LIS-oriented activities almost dominate collection and processing steps, while IA-oriented activities lead in the step of analysis. In other words, LIS activities may play a vigorous role in collecting intelligence data, and further help organize the gathered information to facilitate its assemblage. IA activities focus on connecting the dots and drawing the bigger picture. Hence, we may say that LIS can serve as a prelude to IA, and that IA completes LIS in a way of information use. In addition, whilst LIS focuses on the format and properties of information, IA concentrates on the content and meaning of information. When LIS mostly handles information from stable sources and with structured forms and high trustworthiness, IA copes with information from diverse sources and fuzzy environments with both structured and unstructured forms. While LIS pays more attention to overt, public information domains, IA supplements LIS with responsiveness to the covert, secret information domains. Due to the complementary relation, LIS has played an active role in shaping modern intelligence practice, and IA may potentially help LIS expand into this nontraditional area without losing its unique character. Therefore, IA and LIS could end up with having a synergistic relationship if they converge well.
Specific to intelligence education, we examined the literature and found that formal educational offerings in IA are lacking. Marrin (2009) dichotomizes learning opportunities for IA in the United States into two sections: governmental training programs, which are designed by intelligence agencies such as the CIA to provide specific instructions for their internal staff mostly over a short term; and academic education programs, which intend to provide theoretical and practical knowledge about IA over a long term. Based on the information provided by Marrin (2009) and by official websites of those intelligence agencies and educational institutions, Table 2 and Table 3 list the names and characteristics of these programs. Additionally, by providing grants or holding conferences and workshops, some non-profit organizations, such as the Intelligence Community Centers of Academic Excellence (IC/ CAE) and the International Association for Intelligence Education (IAFIE), provide valuable contributions to the enhancement of intelligence education.
Although the above lists may not be exhaustive, they at least establish a base line for follow-up monitoring. Several observations can be drawn from these tables. First, governmental training programs are agency-based, domain-specific, and in a decentralized, fractured structure. Each major intelligence agency has created at least one organizational entity engaged in internal training. While DoD and DIA programs focus on the military sector, CIA programs concentrate on the civilian sector (i.e., political, economic, social, and technological sides of foreign countries), and FBI programs emphasize law enforcement intelligence. There is, however, no centralized mechanism to identify, coordinate, produce and update a set of coherent learning contents and standards.
Second, among those governmental training offerings, skill-enhancement-oriented programs outnumber knowledge-advancement-oriented programs. Except the NDIC who provides solid degree programs in intelligence studies, others listed in Table 2 are mostly programs for orientation or performance improvement purposes. These short-term events may easily and adequately satisfy institutional training needs. In the long term, however, they may be limited to IA professionalization (Marrin, 2005).
Third, in the academic category, there are a small number of educational programs related to IA, particularly when you compare that number against the total number of universities and colleges in the United States. While lamenting the scarcity of formal educational opportunities, we must take one step further to identify reasons why this is happening. Fleisher (2004) observes that scope ambiguity and lack of an agreed-upon body of knowledge (BOK) could be two good reasons. In like fashion, Marrin (2009) remarks that the shortage of BOK means the lack of standardization and assurance mechanisms to ensure the quality and reliability of intelligence production. To remedy this, continuous research and cumulative knowledge building may be the only solutions.
Fourth, IA has attracted attention among higher education institutions, ranging from undergraduate colleges to comprehensive research universities and from traditional institutions to virtual schools which offer classes strictly online. Many IA programs are housed in an independent entity, such as Department of Intelligence Studies at Mercyhurst College, Global Security and Intelligence Studies Program at Embry-Riddle Aeronautical University, and the School of Security and Global Studies at American Military University. Other hosting areas include international affairs, public policy, arts and sciences, and business administrations. These academic programs recruit students from the general public, offering bachelor and master degrees and other qualifications such as undergraduate and graduate certificates in intelligence studies.
Finally, among those listed programs in Tables 2 and 3, there is almost none linkable to LIS. It may provide a piece of counterevidence to the claim that IA and LIS are interconnected. Strickland (2005) observes that LIS schools have rarely invested their attention in intelligence work. He attributes this phenomenon to the conflict between the openness of academia and the covertness of intelligence. Nevertheless, we identified two pioneers who have pushed the boundaries of the intelligence education in the LIS context.
[FIGURE 4 OMITTED]
Shelfer and Goodrum (2000) report how Drexel University designed and implemented a competitive intelligence program housed in their iSchool. That program involved three modules (i.e., business information tools, information services to organizations, and competitive intelligence), one online certification option, and was aimed toward both undergraduate and graduate students. Fleisher (2004) identifies a competitive intelligence degree program set up at the Graduate School of Library and Information Science at Simmons College. Boasted as the first of this kind in the United States, the program offered courses that could lead to a master's degree or a post-master's certificate in competitive intelligence. However, these two programs have now appeared to be discontinued and downsized into a concentration (Drexel University, 2010) or specialization (Simmons College, 2010). One of legacies that they have left may be an interest in competitive intelligence education for the LIS area.
An analysis of course information provided by 58 ALA-accredited LIS schools websites has identified a list of courses that are relevant to intelligence service. To conduct that analysis, first we visited the official website of each school, and then found their webpage(s) containing course information (e.g., listing of course titles and descriptions). On that specific webpage, we used the search function provided by our browser, Microsoft Internet Explorer, to retrieve any text strings including the word "intelligence." Then these text strings were analyzed to determine in what context the word "intelligence" was mentioned. The analysis would then tell us in which courses intelligence is taught, the type of intelligence work, and the extent of coverage. This approach was limited by several drawbacks. First, it is uncertain if the information provided is up-to-date. Second, it was likely we would miss courses in which intelligence work content is taught but whose title and description have not mentioned the word "intelligence." Third, the course information would not tell how often such courses are offered and how many students enroll. Despite those defects, the data were collected in November 2010.
Table 4 indicates that almost half of ALA-accredited LIS schools (27 out of 58, 46.6%) include at least one course in their curriculum covering intelligence work content. In total, 30 courses were identified. Among them, two courses (approximately 6.7%) are at the undergraduate level (i.e., University of South Carolina and Syracuse University), while all others (28, 93.3%) are offered at the graduate level. Breaking down the text strings containing the word "intelligence," as illustrated in Figure 3, we see "competitive intelligence" takes a majority (20 out of 30, 66.7%), followed by "strategic intelligence," "business intelligence," and others (i.e., "business/competitive intelligence," "corporate intelligence," and "intelligence gathering"). Figure 4 shows the title variety among the 30 courses. Almost half of them are fully committed to competitive, business, or strategic intelligence. The other half teach intelligence work content with other topics such as business information resources, knowledge management, collection development, system management, data mining, and online searching. In sum, one salient finding of Table 4 is that competitive or business intelligence has been partly integrated into LIS education. Possible reasons for this integration may include advancement of information and communication technology, proliferation of accessible open source information, and popularity of knowledge management topics toward special or corporate librarianship. To understand what is taught in these courses, further study of their syllabuses is necessary.
Challenges and Opportunities for a Converged Approach
Open source information has become of great value for government, military, law enforcement, and competitive/business intelligence. As the use of social media grows steadily, more LIS professionals have started tagging unstructured data (e.g., email, blog, video, image, voice recording, etc.) (Reid, 2010a, 2010b). Intelligence work is receiving growing attention from LIS educators (ALISE.org, 2010; Cronin, 2005; Williams & Lipetz, 2005), particularly under the circumstances where calls for revising the traditional roles and responsibilities of information professionals are prevailing (Shelfer & Goodrum, 2000). Yet, current interests in a convergence of LIS education and IA are still weak and even pervasively repressed. Based on the literature that we reviewed, this section describes some challenges and research opportunities in developing an effective convergence strategy.
The very first challenge that we perceive is that scientific research about how intelligence analysts work in reality is seriously lacking. This has hindered the process of building the IA knowledge base, from which trainable elements can be identified. Although in the field of intelligence there are various publications (such as journal articles, books, and so on), most of them are historically or conceptually based. Intelligence practice itself is far ahead of empirical research. If that situation continues, it would be difficult to convince LIS educators who usually appreciate scientific methods and empirical evidence that there are some natural connections between IA and LIS.
The second challenge is relayed from the first one. Because of the paucity of such scientific research, there is a lack of competency-based models in intelligence education. For any occupational training, a competency-based model is essential when developing learning objectives, instructional materials and procedures, education strategies, and evaluation methods (Postlethwaite, 1973). Without knowing the adaptive and technical competencies for a qualified intelligence analyst, it would be difficult to design an efficient and adequate program to train future analysts. Furthermore, in our search of the above literature, no previous study has been found to demonstrate how LIS skills can be relevant to IA. That would pose a huge challenge to LIS educators to incorporate IA education initiatives.
The third challenge can be related to ethical issues. LIS schools traditionally train people how to be librarians. This profession greatly values and adheres to ethical principles on information seeking, gathering, organizing, and disseminating. For example, in the ALA Code of Ethics (American Library Association, 2008), it is articulated that information professionals protect each information user's "right to privacy and confidentiality with respect to information sought or received and resources consulted, borrowed, acquired or transmitted." Another example can be taken from the ASIS&T Professional Guidelines (American Society for Information Science and Technology, 2005), which clearly state that information professionals must uphold the principle of open inquiry and oppose any misrepresentation and/or falsification. However, intelligence practitioners (such as analysts from the FBI, CIA, and so on) sometimes may have to disguise their real identity to access, collect and analyze a great deal of personal and private information (Shulsky & Schmitt, 2002). Although in a broader intelligence community, some codes of ethics have been developed in specific domains, such as SCIP (Strategic and Competitive Intelligence Professionals, a trade association previously called the Society of Competitive Intelligence Professionals), other intelligence domains may not have similar codes. In particular, for those who usually conduct covert and secret intelligence practices, ethical issues are often downplayed. Thus, potentially, when values are in conflict, those aforementioned ethical dilemmas may entail huge moral challenges and even resistance from some LIS educators towards the convergence.
These challenges, however, can also be translated into research opportunities. If we hope to achieve an effective convergence of IA and LIS education, a research agenda, which may include the following research questions, is needed:
1. How do intelligence analysts work in reality?
2. What is the impact of intelligence on decision making?
3. What competencies are needed for a qualified intelligence analyst?
4. Among the competencies, which are relevant to LIS education?
5. To what extent are these competencies relevant to LIS education?
6. When IA meets the LIS professions, what are possible ethical dilemmas?
7. How can we address those ethical dilemmas?
8. How many LIS schools would like to integrate IA education? And to what extent?
9. What will be the best structures and governance models to ensure proper design, implementation, and assessment of such convergence?
To answer these questions, various methodological approaches can be used. Andrew Abbott, one of the most widely cited authors in the literature about professions and professionalization, observes that social scientists usually take the following four approaches to conduct their research: ethnography, surveys (questionnaire and/or interview), record-based (e.g., census) analysis, and history (Abbott, 2004). In the previous studies on intelligence work discussed earlier in this paper, surveys (e.g., Ghoshal and Westney, 1991; Jin, 2008; Montgomery et al., 1979) and ethnography (e.g., Johnston, 2005) are the primary methods adopted by the researchers. These two approaches may be suitable to the majority of the above-listed research questions, and we propose that they could be hybridized with other specific techniques. For example, if we group Questions 3, 4, and 5 together to study the competency issues of intelligence analysts, researchers might want to consider first using a Q-methodology-based literature review technique (Dziopa and Ahern, 2011; Johnston, 2005; Stephenson, 1953) to identify a prototype of the competencies list, then a Delphi method (Linstone & Turoff, 1975) to verify and refine the prototype, and finally administering a questionnaire survey on LIS educators to measure the relevance of each identified competence. In summary, "science is a conversation between rigor and imagination" (Abbott, 2004, p.3), and likewise, those research questions must be answered through proper and rigorous methods. Meaningful research findings should also lead to new questions and proposals, and so the research agenda grows by itself and demonstrates its value to both domains of LIS and IA.
Traditionally, LIS education has focused mostly on librarianship. But the management and use of information are becoming more critical in various social areas. LIS education has to evolve accordingly and address the needs of other information professions. Since the 9/11 terrorists attack, the intelligence community is under critical reforms and the value of open source information has been significantly upgraded (Dessy, 2010; Powell, 2010). According to Reid (2010a, 2010b), in both public and private sectors, and particularly in federal government agencies, there is a huge shortage of intelligence analysts of various kinds: regional analysts, cyber analysts, business analysts, all source analysts, forensic analysts, management analysts, open source analysts, security analysts, and so forth. Knowledge and skills of LIS professionals on text mining and database design may play a very active role in the IA field. The literature indicates that LIS and IA have some complementary linkages. Potentially they can even have a synergistic relationship to achieve mutual enhancement. This constitutes the basis for a positive assumption that IA can be integrated into LIS education. We have seen some encouraging evidence that these two domains intersect at competitive intelligence, and nearly 47% of ALA-accredited LIS schools have integrated some intelligence work content into their course offerings. This integration, however, is still at a very initial stage, and there may be many technical and ethical challenges. For example, LIS schools may be ambiguous about the needs for an IA program, the standards of teaching, and the level of engagement. To address these challenges and turn the assumption into reality requires addressing the series of research questions listed earlier in this paper. The literature about this topic is limited. Our intention here was to provide a review of the literature and of the current offerings of IA education to give a reference point for educators and researchers who share the same interests regarding the integration of IA and LIS. This article hopefully will generate further discussion and research about this topic.
Appendix A: Specialized Intelligence Disciplines Discipline Description BI Business intelligence refers to those activities operated by an organization to seize and analyze information generated from both internal and external sources, in order to minimize hazards and uncertainties of the business environment and to develop proper, far-sighted business strategies. Those activities may include text mining, data mining, market analysis, sales analysis, analysis of customer and supplier records and behavioral patterns, etc. CI Competitive intelligence refers to the process by which an organization legally and systematically collects, organizes, analyzes, and distributes information about its competitors and competitive environment, in order to gain or maintain its competitive edge. This concept is often used synonymously with business intelligence, market intelligence, or competitor intelligence. HUMINT Human intelligence collects information from human sources. These sources usually have access to important information and they are hired to pass the information to the intelligence agency. MASINT Measurements and signature intelligence is "technically derived intelligence that detects, locates, tracks, identifies, and describes the unique characteristics of fixed and moving targets" (Lefebvre, 2004, p.259) MEDINT Medical intelligence results "from the collection, evaluation, analysis, and interpretation of foreign medical, bio-scientific, and environmental information which is of interest to strategic planning and to military medical planning and operations for the conservation of the fighting force and formation of assessments of foreign medical capabilities in both military and civilian sectors" (Kaufman, 2001, p.1). MI Market intelligence mainly involves the analysis of company's current and potential customers and its sale patterns, in order to cope with short-term and tactical problems. TECHINT Technical intelligence uses advanced technology such as long-range photography and the interception of electromagnetic waves to collect information. It can further be broken down into IMINT and SIGINT. IMINT Imagery intelligence, or photographic intelligence, refers to collection, processing and analysis of photos or images, obtained from such sources as aerial surveillance or reconnaissance, of places or things to which direct access may not be possible. SIGINT Signals intelligence refers to "the process of deriving intelligence from the interception of electromagnetic (radio) waves" (Shulsky and Schmitt, 2002, p.28-29). It can further be broken down into COMINT, TELINT, and ELINT. COMINT Communication intelligence refers to "the interception of and derivation of information from foreign communications signals (radio messages) by other than the intended recipients" (Shulsky and Schmitt, 2002, p.29). TELINT Telemetry intelligence refers to "the interception, processing, and analysis of foreign telemetry" (Shulsky and Schmitt, 2002, p.29). ELINT Electronic intelligence refers to "the interception, processing and analysis of noncommunications electromagnetic radiations coming from a piece of military equipment (e.g., radar) while it operates" (Shulsky and Schmitt, 2002, p.29). OSINT Open-source intelligence derives from non-covert collection via generally available sources of information such mass media (i.e., newspapers, broadcasts, the Internet, etc.), or via diplomatic contacts. SI Strategic intelligence refers to the intelligence necessary to create and implement a grand macro strategy (Heidenrich, 2007).
Abbott, A. (2004). Methods of discovery: Heuristics for the social sciences. New York, NY: W.W. Norton.
ALISE.org. (2010). At ALA--intelligence analysis. Retrieved from http://www.alise.org/mc/page. do?sitePageId=112733&orgId=ali
American Library Association (2008). Code of ethics of the American Library Association. Retrieved from http://www.ala.org/advocacy/proethics/codeofethics/codeethics
American Society for Information Science and Technology (2005). Retrieved from http://www. asis.org/AboutASIS/professional-guidelines. html
Batty, D. (2005). Intelligence work and information science: Two men in a boat. In R. V. Williams & B-A. Lipetz (Eds.), Covert and overt: Recollecting and connecting intelligence service and information science (pp. 25-32). Medford, NJ: Information Today.
Bergeron, P., & Hiller, C. (2002). Competitive intelligence. In B. Cronin (Ed.), Annual Review of Information Science and Technology, 36, 353-390.
Bouthillier, F., & Shearer, K. (2003). Assessing competitive intelligence software: A guide to evaluating CI technology. Medford, NJ: Information Today.
Burke, C. (2005). Intelligence agencies, librarians, and information scientists. In R. V. Williams & B-A. Lipetz (Eds.), Covert and overt: Recollecting and connecting intelligence service and information science (pp. 107-114). Medford, NJ: Information Today.
Chartrand, R. L. (2005). The intelligence game: Seeing is believing? In R. V. Williams & B-A. Lipetz (Eds.), Covert and overt: Recollecting and connecting intelligence service and information science (pp. 33-40). Medford, NJ: Information Today.
Chen, H., Chung, W., Qin, J., Reid, E., Sageman, M., & Weimann, G. (2008). Uncovering the dark web: a case study of jihad on the web. Journal of the American Society for Information Science and Technology, 59(8), 1347-1359.
Chen, H., & Xu, J. (2006). Intelligence and security informatics. In B. Cronin (Ed.), Annual Review of Information Science and Technology, 40, 229-289.
CNN.com. (2010). Best jobs in America 2009--Top 50: Intelligence analyst. Retrieved from http:// money.cnn.com/magazines/moneymag/best-jobs/2009/snapshots/9.html
Cronin, B. (2005). Intelligence, terrorism, and national security. In B. Cronin (Ed.), Annual Review of Information Science and Technology, 39, 395-432.
Cronin, B., & Davenport. E. (1993). Social intelligence. In B. Cronin (Ed.), Annual Review of Information Science and Technology, 28, 3-44.
Cronin, B., Stiffler, M., & Day, D. (1993). The emergent market for information professionals: Educational opportunities and implications. Library Trends, 42(2), 257-276.
Davies, P. (2002). Intelligence, information technology, and information warfare. In B. Cronin (Ed.), Annual Review of Information Science and Technology, 36, 313-352.
Dessy, B. (2010, June 25). Analytical challenges in federal libraries. Power-point slides presented at the Intelligence analysis meeting: A discussion of convergent skills and the future, Washington DC.
Drexel University. (2010). The iSchool at Drexel University: Competitive intelligence and knowledge management concentration. Retrieved from http://www.ischool.drexel.edu/CS/GraduatePro grams/MS/CIKM
Dziopa, F., & Ahern, K. (2011). A systematic literature review of the applications of Q-technique and its methodology. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 7(2), 39-55.
Fleisher, C. S. (2004). Competitive intelligence education: Competencies, sources, and trends. The Information Management Journal, 38(2), 56-62.
Fleisher, C. S., & Blenkhorn, D. L. (2001). Managing frontiers in competitive intelligence. Westport, CT: Quorum Books.
Ganesh, U., Miree, C. E., & Prescott, J. E. (2003). Competitive intelligence field research: Moving the field forward by setting a research agenda. Journal of Competitive Intelligence and Management, 1(1), 1-12.
Garst, R. (1989). A handbook of intelligence analysis (2nd ed.), Washington, DC: Defense Intelligence College.
Ghoshal, S., & Westney D. E. (1991). Organizing competitor analysis systems. Strategic Management Journal, 12(1), 17-31.
Heidenrich, J. G. (2007). The state of strategic intelligence: The intelligence community's neglect of strategic intelligence. Studies of Intelligence, 51(2), Retrieved from http://www.cia.gov/library/center-for-the-study-of-intelligence/csi- publications/csi-studies/studies/vol51no2/thestate-of-strategic- intelligence.html
Heuer, R. J. (1999). Psychology of intelligence analysis. Washington, DC: Center for the Study of Intelligence, Central Intelligence Agency.
Hohhof, B., & Chitwood, L. (2000). At a crossroad: Information professionals to intelligence analyst. Information Outlook, 4(2), 22-25.
Intelligence.gov. (2010). Intelligence.gov--Analysis. Retrieved from http://www.intelligence.gov/ careers-in-intelligence/types-of-opportunities/ analysis.html
Jin, T. (2008). An exploratory study on information work activities of competitive intelligence professionals (Unpublished doctoral dissertation). McGill University, Montreal, Canada.
Johnson, L. K. (2007). Handbook of intelligence studies. London, UK: Routledge.
Johnston, R. (2005). Analytical culture in the U.S. intelligence community: An ethnographic study. Washington, DC: Center for the Study of Intelligence, Central Intelligence Agency.
Kaufman, D. C. (2001). Medical intelligence: A theater engagement tool, strategy research project. Carlisle Barracks, PA: U.S. Army War College.
Lefebvre, S. J. (2004). A look at intelligence analysis. International Journal of Intelligence and counterintelligence, 17(2), 231-264.
Lipetz, B-A. (2005). Defining what information science is or should be: A survey and review of a half-century of published pronouncements In R.V. Williams & B-A. Lipetz (Eds.), Covert and overt: Recollecting and connecting intelligence service and information science (pp.187-198). Medford, NJ: Information Today.
Linestone, H. A., & Turoff, M. (1975). The Delphi method: Techniques and applications. Reading, MA: Addison-Wesley.
Lowenthal, M. (2006). Intelligence: From secrets to policy. Washington, DC: CQ Press.
Marling, G. L. (2005). Technology for open source government information and business intelligence. In R. V. Williams & B-A. Lipetz (Eds.), (pp. 129-146). Covert and overt: Recollecting and connecting intelligence service and information science. Medford, NJ: Information Today.
Marrin, S. (2005). Intelligence analysis: turning a craft into a profession. Proceedings of the 2005 International Conference on Intelligence Analysis, McLean, VA. Retrieved from http://analysis. mitre.org/proceedings/Final_Papers_Files/97_Camera_Ready_Paper.pdf
Marrin, S. (2009). Training and educating U.S. intelligence analysts. International Journal of Intelligence and Counterintelligence, 22 (1), 131-146.
Marshall, M. G. (2005). Teaching intelligence research. Defense Intelligence Journal, 14(1), 89-113.
Montgomery, C. A., Thompson, J. R., & Katter, R. V. (1980). Cognitive processes in intelligence analysis: A descriptive model and review of the literature (Report No. 445). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.
Montgomery, C. A., Thompson, J. R., and Katter, R. V. (1979). Human processes in intelligence analysis: Phase I overview (Report No. 1237). Alexandria, VA: U.S. Army Research Institute for the Behavioral and Social Sciences.
Postlethwaite, T. (1973). Principles of curriculum development, Paris, France: UNESCO.
Powell, A. D. (2010, June 25). Overview of intelligence analysis. Power-point slides presented at the Intelligence analysis meeting: A discussion of convergent skills and the future, Washington DC.
Prescott, J. (1991). A competitive assessment of SCIP. Competitive Intelligence Review, 2(1), 1-2.
Qin, J., Zhou, Y., Reid, E., Lai, G., & Chen, H. (2007). Analyzing terror campaigns on the internet: technical sophistication, content richness, and Web interactivity. International Journal of Human-Computer Studies, 65(1), 71-84.
Raisinghani, M. S. (2004). Business intelligence in the digital economy: Opportunities, limitations and risks. Hershey, PA: Idea Group.
Reid, E. (2010a). Information professionals as intelligence analysts: Making the transition. Retrieved from http://www.governmentinfopro.com/federal_info_pro/2010/01/information- professionals-as-intelligence-analysts-making-the-transition. html
Reid, E. (2010b, June 25). BI, CI, and intelligence analysis. Power-point slides presented at the Intelligence analysis meeting: A discussion of convergent skills and the future, Washington DC.
Shelfer, K., & Goodrum, A. (2000). Competitive intelligence as an extension of library education. Journal of Education for Library and Information Science, 41(4), 352-361.
Shulsky, A. N., & Schmitt, G. J. (2002). Silent warfare: Understanding the world of intelligence. Washington, DC: Brassey's.
Simmons College. (2010). Master's Degree: Graduate School of Library and Information Science, Simmons College. Retrieved from http://www. simmons.edu/gslis/academics/programs/ms/
Stephenson, W. (1953). The study of behavior: Q-technique and its methodology. Chicago: University of Chicago Press.
Strickland, L. S. (2005). Knowledge transfer: Information science shapes intelligence in cold war era. In R. V. Williams & B-A. Lipetz (Eds.), Covert and overt: Recollecting and connecting intelligence service and information science (pp. 147-168). Medford, NJ: Information Today.
Thompson, C. (2006). Open-source spying. The New York Times Magazine. Retrieved from http://www.nytimes.com/2006/12/03/magazine/ 03intelligence.html
Warner, M. (2005). Wanted: A definition of intelligence. In R. V. Williams & B-A. Lipetz (Eds.), Covert and overt: Recollecting and connecting intelligence service and information science (pp. pp.199-210). Medford, NJ: Information Today.
Warner, M. (2006). Sources and methods for the study of intelligence. In L.K. Johnson (Ed.), Handbook of intelligence studies, London, UK: Routledge.
Williams, R. V., & Lipetz, B-A. (2005). Covert and overt: Recollecting and connecting intelligence services and information science, Medford, NJ: Information Today.
School of Library and Information Science, 267 Coates Hall, Louisiana State University, Baton Rouge, LA 70803. Email: email@example.com
School of Information Studies, 3661 Peel St., McGill University, Montreal, Quebec, Canada, H3A 1X1. Email: firstname.lastname@example.org
Table 1: Comparison of IA and LIS Work Domain Characteristics. IA LIS Job Title Intelligence analyst Librarian, archivist, documentalist, information specialist, cataloguer, information scientist, researcher, etc. Information Unstable Relatively stable Environment Data Form Unsteady and steady Mostly steady Dynamic and static Mostly static Unstructured and Mostly structured structured Trustworthiness Low High of Data Data Processing Intuitive, subjective Conventional, Styles objective Urgency High Low Typical Activities analyzing abstracting associating classifying assembling/ data handling and synthesizing management assessing/evaluating/ data mining grading collating disseminating inference making indexing hypothesis testing organizing organizing querying selecting retrieving sourcing translating and validating/ machine translation clarifying/verifying word/character identifying Table 2: Formal Governmental Training Programs in Intelligence in the United States. Intelligence Program Parent Organization and Description What to Offer/Teach Department of National Defense B.S. in Intelligence; Defense (DoD) Intelligence College M.A. in Strategic (NDIC), created in Intelligence 1962, previously known as Defense Intelligence School (DIS), Defense Intelligence College (DIC), or Joint Military Intelligence College (JMIC) Defense Intelligence Joint Military Not specified Agency (DIA) Intelligence Training Center (JMITC), created in 1993 Central Intelligence Sherman Kent School Intermediate and Agency (CIA) for Intelligence, advanced level created in 2000, training on later in 2002, it analytical became a part of CIA methodologies, University substantive issues, and leadership skills Central Intelligence Career Analysts Training on basic Agency (CIA) Program (CAP) thinking, writing, and briefing skills, as well as basic analytical tools Federal Bureau of College of Analytical Tools and techniques Investigations (FBI) Studies, created in for both strategic and 2001 after the 9/11 technical analysis Terrorist Attack Office of the National Intelligence Not specified Director of National University, created Intelligence (ODNI) in 2006 with a virtual status, working to coordinate training across the whole Intelligence Community. Parent Organization Student Body Department of Members of the U.S. Defense (DoD) Armed Forces or federal government employees Defense Intelligence Not specified Agency (DIA) Central Intelligence Managers and Agency (CIA) analysts in CIA Central Intelligence New hires in CIA Agency (CIA) Federal Bureau of New and in-service Investigations (FBI) analysts in FBI Office of the Not specified Director of National Intelligence (ODNI) Table 3: Formal Academic Educational Programs in Intelligence in the United States. Host of Intelligence Parent Organization Program What to Offer/Teach American Military School of Security * Undergraduate University (Online) and Global Studies Certificate in Intelligence Analysis * Graduate Certificate in Intelligence Analysis * B.A. in Intelligence Studies * M.A. in Intelligence Studies * Graduate Certificate in Intelligence Studies Embry-Riddle Global Security and * Bachelor's degree Aeronautical Intelligence Studies University (Arizona) Program Henley-Putnam n/a * Bachelor's, University Master's, Doctoral Degrees and Certificate in Strategic Security James Madison College of Integrated * Bachelor's degree University (Virginia) Science & Technology in Information Analysis Johns Hopkins Carey Business * Competitive University (Maryland) School, School of Intelligence Arts and Sciences, Graduate School of Advanced Certificate International * Certificate in Affaires School of National Security Education Studies * Various seminars, workshops, courses in intelligence * Master of Science in Intelligence Analysis Mercyhurst College Department of * B.A. in (Pennsylvania) Intelligence Studies Intelligence Studies * M.S. in Applied Intelligence * Graduate Certificate in Applied Intelligence Notre Dame College Center for * Certificate in (Ohio) Intelligence Competitive Studies (Business) Intelligence University of School of Public * Certificate Maryland at College Policy Program in Park Intelligence Analysis Figure 3. Distribution of the Contexts Containing the Word "Intelligence." Competitive Intelligence 67% Strategic intelligence 13% Business Intelligence 10% Others 10% Note: Table made from pie chart.
|Printer friendly Cite/link Email Feedback|
|Title Annotation:||Library and Information Science|
|Author:||Jin, Tao; Bouthillier, France|
|Publication:||Journal of Education for Library and Information Science|
|Date:||Mar 22, 2012|
|Previous Article:||Physiological access as a social justice type in LIS curricula.|
|Next Article:||Building rapport between LIS and museum studies.|