OSINT: Its Implications for Business/Competitive Intelligence Analysis and Analysts
| Autor | Craig Fleisher |
| Cargo | Profesor de Gestión, Estrategia y Espíritu Emprendedor. Escuela de Negocios de Odette. Universidad de Windsor. Canadá |
| Páginas | 115-141 |
Page 116
The gathering of data and information from open sources (OS) has been an active focus of both national and business/competitive intelligence organizations for decades (Steele, 2002). Business organizations, in particular, have long been reliant on open sources for intelligence purposes; on the other hand, national intelligence organizations, particularly the large ones that are set up in individual collection silos in the United States, have recently been encouraged to make better use of open sources in their array of intelligence gathering functions (The Commission, 2005). This paper highlights the experiences of business organizations in incorporating and utilizing OS for business/competitive intelligence purposes. It focuses most specifically on the implications OS have for analysts and the analysis process within business/commercial enterprises.
Business/competitive intelligence practitioners have long utilized open sources for gathering data to be used in the intelligence process, evidenced by many articles that have appeared about this process in the B/CI literature (Dishman, Fleisher, & Knip 2003; Fleisher, Knip & Dishman, 2003; Knip, Dishman, & Fleisher, 2003; Fleisher, Wright & Tindale, 2007). One of the key concepts that has guided B/CI practice is the intelligence cycle, which shows how intelligence begins with a client/customer’s need, how data and information are collected as a means of addressing the need, the application of analysis and synthesis methods to «make sense» of the collected items, and then how the resultant «solution» or «actionable insight» is communicated back in the form of an intelligence product or service to the client/customer to resolve their query (Murphy, 2006). This all takes place within a cybernetic loop and can be used to locate what a CI practitioner is doing over an array of different projects (i. e., answering different intelligence needs) that may be present at any point in time during the practitioner’s conduct of their role (Rajaniemi, 2005).
There are many extant definitions of business/competitive intelligence (Fleisher, 2003a). Rather than getting into a long discussion of which ones are ideal, for the purposes of this paper, I define business/competitive intelligence asPage 117 a systematic, targeted, timely and ethical effort to collect, synthesize, and analyze competition and the external environment in order to produce actionable insights for decision-makers. The practical value-adding concept underlying B/CI suggests that effective B/CI should underlie more effective decisions, leading to more insightful (market-based) actions that should eventually result in enhanced economic/financial performance (Fahey, 2007).
The acquisition or collection of data underlying the B/CI process takes some of the same forms it would in national intelligence agencies, most prominently including HUMINT (i. e., human source intelligence) and OSINT, although the systemic use of national gathering methods like COMINT (i. e., communications intelligence), IMINT (i. e., imagery intelligence), MASINT (i. e., measurements and signatures), or SIGINT (i. e., signals intelligence) would be unusual (Clark, 2004). OSINT is the most frequently used form of B/CI intelligence gathering, desirable because it is so easy and produces abundant raw materials for further processing (Vibert, 2003). It is usually engaged as a next step in the project plan after the data collector has scoured the existing base of information within the firm and exhausted the organization’s reservoir of internal knowledge (Blanco, Caron-Fasan, & Lesca, 2003).
For the purposes of this paper, I define OSINT as the finding, gathering, exploitation, validation, analysis and sharing with intelligence-seeking clients of publicly available print and electronic data from unclassified, non-secret (often «gray literature») sources. Traditionally, OSINT was characterized by the searching of publicly available published sources (Burwell, 2004). This included books, journals, magazines, reports and the like, so much so that some people referred to OSINT as literature intelligence or LITINT (Clark, 2004); nevertheless, the growth of digital sources such as those proliferating over the WWW has enlarged the scope of OS activity (Boncella, 2003).
These sources were usually identified and scoured first by library and information specialists (special librarians), second by corporate librarians and third by corporate information specialists who had been trained and maintained expertise in quickly identifying required sources of information —many of whom continue to serve this purpose although having greatly expanded the range of open sources examined as well as the nature of querying used (Berkien, 2006). Contemporary corporate information specialists apply a variety of methods for organizing open sources including but not limited to web-link analysis (Reid, 2003), webometrics (Bouthillier & Jin, 2005), scanning methodsPage 118 (Decker, Wagner, & Scholz, 2005), source mapping (Vriens & Achterbergh, 2003), text mining (Leong, Ewing, & Pitt, 2004), blog analysis (Pikas, 2005), and a variety of different patent analysis methods (Dou, Leveille, Manullang, & Dou Jr., 2005; Fleisher & Bensoussan, 2003). See Table 1 for a grid which shows the broad range of open sources utilized for intelligence purposes.
Table 1: Sample Open Source Information Target Grid
| INTERNAL Sources | EXTERNAL Sources | |
| Human | Boundary spanners Customers Employees Managers Networks Sales staff | Academics Consultants/experts Customers Media members Policy officials Suppliers |
| Building diagrams Business plans Databases | Applications (Building) Blogs/Wikis Company Home Pages | |
| Document | Info resource library Intranet (text, A&V) Policies Reports/Statistics | Mapping Media (TV, radio, imagery) Patents/Legal Filings Reports/Statistics |
| Mixed | Observations Meetings Site Visits | Seminars Site Visits Trade Events |
In addition to being a key part of the data collection process, OSINT is often the basis of information utilized in planning and targeting other high value collection activities (Steele, 2002). This is done, at least in part, because it is so convenient to access open sources, as well as the availability of target-rich material (Mah, 2005). In national intelligence, open source data provide a key supplement and archival ability to HUMINT, IMINT, MASINT, SIGINT and other classified collection means. Last but not least, OSINT serves as an effective complement to the other means of data gathering. By combining the data gathered from multiple sources, analysts can better understand the diversity of viewpoints on important issues (Clark, 2004).
Analysts have not been reluctant in using open sources to help them generate business/competitive intelligence insights (Vibert, 2004). Indeed, they have become quite used to integrating data gathered from these sources with other forms of intelligence, and particularly HUMINT (Clark, 2004; Steele, 2002).Page 119 Having stated that, there are some issues that these analysts have encountered that are somewhat unique to OSINT. These are described in greater depth in the section that follows.
Analysts maintain an important role in the overall B/CI process (Fleisher & Bensoussan, 2003, 2007), and can be even more prominent in a heavily OS-based environment. Steele (2002: 171) notes that «analysis is the key enabling skill that is essential to the successful integration of OSINT into an all-around intelligence capability». Even though there is no universal job description for B/CI analysts, there is enough published research done in the area to outline a generic job description as well as to understand the kind of roles they should play, processes they employ, as well as outcomes they produce (Sawka, 2005). The kinds of outcomes that analysts typically seek to achieve are:
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Predict future developments: Analysts explain implications of developments, both current and prospective, to decision makers (Fahey, 2007).
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Help decision and policy-makers to avoid surprises: Analysts seek to provide warnings of major developments, events, trends and assessments (Gilad, 2004).
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Make data more meaningful and sensible: Analysts give guidance to decision-makers, as well as offering alternative means for attaining objectives (Fleisher & Bensoussan, 2007).
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Keep decision makers informed: They offer pieces of current information on specialized topics of concern to decision makers (Service, 2006).
Analysts employ part art, science and craft to their organizational tasks (Fleisher & Bensoussan, 2007). The art facets often centre on the need for creativity, right-brain utilization, and original thinking. This is one reason why there are no software programs that an organization can just plug in that will routinely produce the kind of B/CI analysis products that demanding intelligence clients seek (Bouthillier & Shearer, 2003a; Johnson, 2006). The science component is present in the accepted use of routinized methods, established training programs, and continuing «R&D» that is conducted...
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