Don't mistake the message here: KM is not CI. They are ... [9] DeVoe, L., & Neal, K., 2005,"When business intelligence equals business value", 10(3):57â63.
Competitive Intelligence and Knowledge Management's Affinities and Relations: Developing a Model Afrooz Momeni, Mohammad Fathian, Peyman Akhavan Department of Industrial Engineering, Iran university of Science and Technology, Iran
Citation: Momeni, Afrooz, Fathian, Mohammad, and Peyman Akhavan (2012), Competitive Intelligence and Knowledge Management's Affinities and Relations: Developing a Model, Invertis Journal of Science & Technology, Vol. 5, No. 1, pp. 1-7.
Abstract Knowledge management is the process through which corporate knowledge is used to improve organizational performance. Essentially it looks at managing internal knowledge processes, developing the efficient usage of all information required for corporate decisions. Competitive intelligence (CI) is a process for gathering usable knowledge about the external business environment. The aim of this paper is to demonstrate the synergy between Competitive Intelligence, Knowledge Management. As practices; both CI and KM have the same fundamental requirements. Each need a strategic purpose, each depends on having a corporate culture that encourages people to create and share information and knowledge, each requires skills and competencies to carry out the processes involved, and each is facilitated by the same technology tools. But they have some differences. In this paper we show these differences and similarities. Key words: competitive intelligence, knowledge management, small firms
1. Introduction Nowadays, economic organizations are subject to external forces that they must live with and react to: increasing sophistication of competitors, customers and suppliers, globalization of business, international competition. Perhaps the most critical component for success of the modern enterprise is its ability to take advantage of all available information - both internal and external. It’s a real challenge, due to the tremendous flow of information it’s facing every day. Also, the nature of information itself has changed, in terms of volume, availability and importance. The data to be considered becomes more and more complex in both structure and semantics. With the Internet, Intranets, Groupware systems the volume of available data increases each day – customer communications, internal research reports or competitors web sites are just some sources of electronic data. Intellectual property and assets, knowledge are contained within the huge volumes of information and leveraging this value is increasingly important in the competitive market [1]. Making sense of all this information, gaining value and competitive advantage through represents real challenges for the enterprise. The IT solutions designed to address these Challenges have been developed in two different approaches: structured data management and unstructured content management. We can even think at these approaches in a more general perspective as being information management technologies and knowledge management technologies – being aware in the same time that information management it’s a part of knowledge management, as information can be considered a type of knowledge (explicit knowledge).Knowledge management technologies, while less mature than information management technologies, are more and more capable of combining content management systems and the Web with vastly improved searching and text mining capabilities to derive more value from the explosion of textual information. Integrating Competitive intelligence and Knowledge Management in order to respond to the challenges the modern enterprise has to deal with represents not only a "new trend” in IT, but a necessity. Over time, techniques from both technologies blended, Competitive Intelligence Systems are a direct result of such integration [2]. The reason these terms are unknown to most business is because the vast majority of businesses are small enterprises that must concentrate their limited resources on surviving day-to-day in a highly competitive marketplace. Paying the bills and meeting the payroll are what they worry about [3].
2. Knowledge management and competitive intelligence Ironically however, it is this lack of KM and CI that is playing an ever-increasing reason for the failure of both large and small businesses in America. And for that reason alone, it is in the interest of every business owner to become familiar with KM and CI. In a nutshell, KM is the recognition, utilization and protection of the knowledge held within an enterprise. Where KM plays a role is in having that contractor recognize that he has access to all this information. That access comes from own sales staff and Service technicians who are out in the field every day, talking to customers, seeing what equipment is being installed by the competition, socializing with technicians from competitor companies, etc [4]. Utilization of this knowledge in the form of letting employees know this information is important and establishing a method whereby the employees can pass this information to the owner is another aspect of KM.
A third aspect of KM is perhaps the most important. That aspect is in understanding that as valuable the knowledge is to you that you can mine from your own employees, the knowledge that your employees have about you and your company is of great value to your competition. And that information, you must learn to protect. In a nutshell, CI is spying. Most CI professionals shun the term “spying”. They prefer terms like “data mining”, “data collection” or “case studies”. Anyone else can hire a CI firm today to obtain information on a competitor. Type “competitive intelligence” into an Internet search engine and follow to some of the links. Former CIA and MI agents staff many of the CI firms you will find listed on the Internet. In most cases, they have the ability to find out more about you and your business than you or any employee within your business knows. They can obtain information not only about your business, but also your personal life and the personal life of your key employees [5]. Legitimate CI firms mostly limit their search to public records. Others however, will literally infiltrate your business and employee ranks, sift through your garbage cans, hack into your computer server, tap your phones and bribe your suppliers. All of the information they obtain is put into the hands of the person or company who hired them. And if that person or company is a competitor of yours, and you have not taken steps to protect information about your business, you are in deep trouble [6]. The reason why knowing about and utilizing CI and KM is important is that nearly all businesses, large and small, are subject to rapid technological change. It doesn’t matter if you are a bricklayer or a neurosurgeon, new technology, rapidly introduced into the market, can put you out of business. On the other hand, knowledge of new technology and the ability to use it to your benefit can make you a fortune [7]. There is also the fact that today, the value of most businesses is in the information they amass. Take for example that HVACR contractor. Every service call his employees make is entered into a computer database along with all the information about the customer. The amount he charges, what he pays, who he buys from and for how much - all of this information and more is residing somewhere on a hard disk inside one or more of his computers. The information inside that computer is of greater value than all of the other equipment the contractor owns [8].
3. Two parts of the same whole Prior to the introduction of the term, Knowledge Management into the collective business vernacular, Competitive Intelligence (CI) was defined and positioned somewhere between market research and industrial espionage in the minds of most managers in the modern business enterprise. While this is a tragic misunderstanding, the development of thought leadership in the KM sector in recent years has caused many CI practitioners to reevaluate the continuum along which we operate [9]. CI and KM to be as much parts of one another as separate disciplines on their own and there are a number of lessons that each might learn from the other in contributing value to the firm. The major differences between CI and KM is the much broader scope of KM and the somewhat more developed scale of CI – rather than applying knowledge to the entire firm and all of its myriad objectives, CI is exclusively focused on the goal of successfully defending the firm from competitive threats and proactively transferring market share from competitors to the bottom line. CI professionals define the field as giving meaning and understanding to competitor initiatives and motives, more than simply unifying sources of discovering and monitoring competitive threats in the marketplace. There has been a great deal written in observation of the synergies between the
two areas, although a few theorists have focused on the true value of knowledge driven activities. The goal of both CI and KM should be first to understand that business, like life, is really a continuous series of decisions in which we hope to convey wisdom in evaluating options for organizations in that difficult business of decision-making [10]. Many have misunderstood this fundamental facet of each field, rather than focusing their attention on the unification of sources of knowledge or intelligence. In KM, we describe our sources of knowledge either being Tacit or Explicit; likewise in CI, we call them Primary or Secondary, respectively. The unfortunate consequences of such a blind focus on access rather than understanding means that we continue to add complexity to the decision-making process – essentially making decisions more difficult rather than easier because of the breadth and depth of information inputs available and applied to evaluating options. Not only must we make the right information available to the right people at the right time, we must add to its value through giving it meaning. We might interpret this phenomenon as being in need of a broker for such knowledge – a kind of information professional who understands the meaning behind the business decisions required, and the nature of the sources of enterprise and competitive knowledge available as inputs to the decision-maker. For example, CI professionals are often loath to make recommendations to their internal customers on what to do about the intelligence they’ve gathered. This misunderstanding of the role of the CI practitioner – to give meaning – has led to the demise of many CI units because of their failure to add value to the decision-making process [11]. They are left with little to point towards as return-on-investment during the next budget cycle. Likewise, a failure by KM professionals to deliver a manner of understanding of the applications for the knowledge within an organization is just as likely to lead to their demise. While it’s troublesome to simplify the relationship between CI and KM, instinctively regard them as similar in terms of applying enterprise knowledge of the internal and external environment for long-term competitive advantage. The goals of both disciplines is to evaluate the business decisions at hand, locate and deliver appropriate knowledge from within and without the organization and, in the end, help to give it meaning and help decision makers better understand the options available to them [12].
4. Competitive Intelligence as a Real knowledge management Knowledge management is such a wonderful buzzword. The buzz however, is mainly from the large consulting companies jumping on the wagon and circling their prey. What started as a noble (if not new) idea has turned into an all too familiar game: Big Names’ consulting “practices” selling hype to eager executives? KM started nobly. The objective was to manage knowledge – an intangible yet enormously important resource. Then the software companies took over and the entire noble effort came down to search engines. In other words, KM today centers around ways to retrieve (albeit faster and more cleverly) existing information hidden in corporate mazes. Intranets, portals, and virtual communities- what do all these have to do with knowledge? At best they are information retrieval tools! To-date much of knowledge management (KM) has focused on learning and deep knowledge acquisition, the preservation and availability of essential knowledge, and the promotion and assistance of learning .Peter Senge in his book “The fifth discipline”, describes the KM culture well and coined the term “the learning organization” [13]. The scope of knowledge managements continues to expand. Internet portal developers use the term “content management” to distinguish the particular process that is required to arrange and manage knowledge within internet portals.
Similarly, the term “intelligence practice “is used here to distinguish and emphasize the unique character and immediacy of deliberate surveillance and foresight processing. By their nature Intelligence practice and competitive Intelligence in particular are "real time or near real time knowledge management". Whichever terminology you prefer mainstream intelligence processing has arrived [14]. The contempt of CI professionals to the hype around KM is simple. CI professionals have already been managing all external knowledge for their corporations for the past decade. For many years they’ve done it without any software toys, with a phone and an (large) index cardholder. Today they have their software and still, a single brilliant CI officer and an effective company-wide CI program - one that is structured correctly and is ingrained in the culture - in a Fortune 500 unit, costs a few hundred thousands dollars per year. Compare that to a typical Big Name’s “KM project in search of KM problems” and you immediately understand why CI professionals actually add value to shareholders and why boards are beginning to demand them. They understand Management of Knowledge. First step: One has to create knowledge. That can only happen if one knows how to look outside one’s cubicle – the old profession of human intelligence collection. Second step: One has to convince people to Use it. That will only happen if one provides real insights about real opportunities and threats in the competitive arena, which is the core of the intelligence synthesis. Third step: One has to guard the strategic future of large organizations managed by insular, and sometime overconfident, executives. That can happen only if one embraces intelligence as basic “decision insurance” culture, tracking blind spots and operating as a risk manager. That’s Competitive Intelligence. That’s the way it is practiced in several global market leaders, and that’s a Real knowledge management. As Peter Drucker observed, companies that can’t look beyond their navels are doomed. No virtual communities gazing collectively into their navels will change that [15]. No amount of Internet searches and surfing will change that, either. Without real competitive intelligence, CEOs that spend millions on big KM projects led by Big Names’ consultants will find themselves, like Eckhard Pfeiffer, formerly of Compaq, when the environment changes [16].
5. KM in the intelligence Process The intelligence process must deal with larg volumes of source data, converting a wide range of text, imagery, video, and other media types into organized information, and then performing the analysis-synthesis process to deliver knowledge in the form of intelligence products. IT is providing increased automation of the information indexing, discovery, and retrieval (IIDR) functions for intelligence, especially the exponentially increasing volumes of global open-source data [17]. The functional information flow in an automated or semiautomated facility (depicted in figure1) requires digital archiving and analysis to ingest continuous streams of data and manage large volumes of analyzed data. The flow can be broken into three parts: 1. Capture and compile 2. Preanalysis 3. Exploitation (analysis-synthesis) Capture and compile includes the acquisition of volumes of multimedia data and the conversion to digital form for storage and analysis. Electronic data (network sources) are directly formatted, while audio, video, and paper documents must be converted to digital form. Foreign sources may be translated by natural-language analysis to convert to a common language base. The
Capture and compile
Pre analysis (organize)
Exploitation (analysis)
Translate data to digital form
Organize and analysis incoming data
Understand the meaning of information
News radio Messages TV
Convert audio video signals
Maps images pictures
Image-text extraction Natural language processing
Foreign language data
Foreign language translation
Data indexing
Clustering and linking of related data
Statistical analysis
Interactive search and retrieval
Lexicon and thesaurus extraction
Structured judgment analysis
Data base populate
Trend and change analysis
Current trends
Threshold alerts
Identify higher level patterns and trends
Sort, collate list and cluster
Modeling and simulation Topic 1 Topic 2
Collaborative analysis
Topic 3 Data visualization
Analyst perceptio n
Figure 1- Intelligence processing and analysis flow includes three distinct phases to develop the production intelligence base
Production base
Convert paper documents to digital
Analysis base
Boots literature papers
preanalysis phase indexes each data item (e.g., article, message, news segment, image, book or chapter) by assigning a reference for storage; generating an abstract that summarizes the content of the item and metadata with a description of the source, time, reliability-confidence, and relationship to other items (abstracting); and extracting critical descriptors of content that characterize the contents (e.g., keywords) or meaning (deep indexing) of the item for subsequent analysis. Spatial data (e.g., maps, static imagery, or video imagery) must be indexed by spatial context (spatial location) and content (imagery content).The indexing process applies standard subjects and relationships, maintained in a lexicon and thesaurus that is extracted from the analysis information base. Following indexing, data items are clustered and linked before entry into the analysis base. As new items are entered, statistical analyses are performed to monitor trends or events against predefined templates that may alert analysts or cue their focus of attention in the next phase of processing. For example, if analysts are interested in relationships between nations A and B, all reports may be scored for a tension factor between those nations, and alerts may be generated on the basis of frequency, score intensity and sources of incoming data items. The third phase of processing, exploitation, presents data to the Human Intelligence analyst for examination using visualization tools to bring to focus the most meaningful and relevant data items and their inter-relationships. The categories of automated tools that are applied to the analysis information base include the following tools [18]: • Interactive search and retrieval tools permit analysts to search by content, topic, or related topics using the lexicon and thesaurus subjects. • Structured judgment analysis tools provide visual methods to link data, synthesize deductive logic structures, and visualize complex relationships between data sets. These tools enable the analyst to hypothesize, explore, and discover subtle patterns and relationships in large data volumes— knowledge that can be discerned only when all sources are viewed in a common context. • Modeling and simulation tools model hypothetical activities, allowing modeled (expected) behavior to be compared to evidence for validation or projection of operations under scrutiny. • Collaborative analysis tools permit multiple analysts in related subject areas, for example, to collaborate on the analysis of a common subject. • Data visualization tools present synthetic views of data and information to the analyst to permit patterns to be examined and discovered.
6. Troubling weaknesses that can cripple knowledge management – and competitive intelligence There are times when knowledge management (KM) only confuses and over-complicates an organization’s need to learn from itself. You have to wonder why such complicated constructs are ever needed. Are organizations invoking this new so-called KM panacea, making more out of less? In the end, are corporations actually spending untold amounts of money on undefined, unclear knowledge management goals? It is somewhere around this point in the discussion of KM’s value, when others summon its so-called cousin, competitive intelligence. CI, as it’s otherwise nicknamed, aims to fulfill specific needs: analysis of privately-held rivals, forecasting new product developments among competitors, determining acquisition prospects or pricing strategies. These are all very practical, high-return objectives. Unlike knowledge management, CI’s goals are clear, necessary and nearly a threedimensional product you can wrap your hands around. Yet, CI also has its weak underbelly.
KM’s soft underbelly is its very bigness. Big words, big concepts and large systems – with large budgets – often follow. Many managers find it difficult to describe KM in just a few words. They use big words and sociological-like phrases, such as “codification strategy” or “decision architecture” to describe how a company has used a groupware or Intranet-based network to share information. The software industry has naturally fed into this desire by creating or refashioning existing packages to meet the demand for knowledge management. It is the software programming and consulting business that has taken long-standing data mining and data warehousing packages and given them a KM makeover. Often the same packages claim to perform both knowledge management and competitive intelligence tasks. These are great claims, but they can fall short. Software is one arena where both CI and KM have experienced mutual weaknesses. Some small firms have spent the some years examining software packages offered to both KM and CI audiences and find they do indeed fail to deliver on many of their promises. Such failures – often coming at a very high cost in personnel and out-of-pocket – can create a downward pressure on both movements, potentially burying the benefits gained [19]. The complete intelligence cycle, which shares many characteristics with the KM process, involves five steps: • Defining requirements • Collect and organize • Analyze • Report and inform • Evaluate None of the packages we reviewed perform more than two or three of the five tasks well. None actually analyze. So, while these packages claim to provide knowledge management solutions, these solutions are partial at best – and severe overstatements at worst. If KM’s reach may be too broad, then CI’s is too specific. In many corporations, CI has been assigned to an elite, almost cloistered group of professionals. Competitive intelligence in corporations that have used it well encourages a broad use by all ends of the organization, from sales, R&D, and purchasing to the executive suite. These successful CI-driven corporations generally do not rely on software solutions and arguably have become excellent users of internal, competitive, and market knowledge. They are the minority. For most, the CI message has become entangled with libraries, information systems or market research and has not successfully transferred true intelligence skills to the organization at large – this despite the very concrete benefits of CI, including improved win-loss sales records, better-targeted new products, and overall improvement to the bottom line [20]. Don’t mistake the message here: KM is not CI. They are cousins, but with different objectives. KM’s goals are very lofty – that of harnessing the entire corporation’s skills, market knowledge and overall business smarts. Day-to-day business does not often reward such dream-like goals that do not return distinct profits, or show specific returns. Add a high price tag to KM’s goals and create lots of friction – friction that bring movements, such as KM, down in defeat. CI, on the other hand, has proven successful time and time again, mostly in very focused, tactical arenas. Although the price tag is much less than its KM cousin, it has not yet broken through this elitist corner in many
corporations. CI is not likely to fade away; at the same time, it desperately needs to grow and be used by more than just the marketing set.
7. Conclusion Nowadays all businesses, large and small, are under the influence of rapid change. Therefore they should vary their policies to not to lose ground in market. One of the ways that businesses can pass these changes is competitive intelligence. The value of most businesses is in the collected information. The most efficient way for collecting information is utilize knowledge management. CI professionals have been got external knowledge of firms for many years. As we saw above, some processes of knowledge management and competitive intelligence are same. Therefore some that aren't same make some problems. We shouldn't use Knowledge management as competitive intelligence and vice versa. If KM’s reach may be too broad, then CI’s is too specific. In many corporations, CI has been assigned to an elite, almost cloistered group of professionals. Competitive intelligence in corporations that have used it well encourages a broad use by all ends of the organization. To date, both KM and CI have failed to show most organizations how to cope with. KM has made it too complicated and expensive, and CI has only taught it to a few.
References [1] Tyson, K., "The Complete Guide to Competitive Intelligence", Prentice-Hall, Chicago, IL, 1998. [2] Hall, H., 2000,"Online information sources: Tools for business intelligence?", Journal of information science, 26(3), 139–143. [3] Gartner, 2007,"Business intelligence & information management", http://www.gartner.com/ap/bi, Sydney, Australia. [4] Duffy, J., 2000, "Knowledge management: What every information professional should know", Information Management Journal, 34 (3), 10-18. [5] Porter, M. and Millar, V., 1991, "How information gives you competitive advantage". Harvard Business Review. [6] Kotler, P., 2002, "Marketing management: Analysis, planning, implementation and control", (11th Ed.). Englewood Cliffs, New Jersey: Prentice Hall. [7] Canongia C., 2006, "Synergy between competitive intelligence (CI), Knowledge management (KM) and technological Foresight (TF) as a strategic model of prospecting- The use of biotechnology in the development of drugs against breast cancer, National institute for metrology, standardization and industrial Quality", Brazil. [8] Hall, J., 2002, "A different platform for recognition, (views and opinions), Air conditioning, Heating & refrigeration News, September. [9] DeVoe, L., & Neal, K., 2005,"When business intelligence equals business value", 10(3):57–63. [10] Asakawa K., & Lehrer M., 2003, "Managing local knowledge assets globally: the role of regional innovation relays". Journal of World Business, 38(1):31–42. [11] Carney, M., 2008 "Minority family business in emerging markets: Organization forms and competitive advantage". Family Business Review, 20(4):289–300.
[12] Gupta, A., & Govindarajan, V., 2000, "Knowledge flows within multinational Corporations". Strategic Management Journal, 21: 473–496. [13] Senge, P., 1990, "The fifth discipline: the art and practice of the learning organization", 236294. [14] Huseman, R., & Goodman, J., 1999, "Leading With Knowledge: The Nature of Competition in the 21st Century", Sage Publications. [15] Drucker, P. , 1998, "The Coming of the New Organization (in) Harvard Business Review on Knowledge Management".HBS Press. [16] http://money.cnn.com/magazines/fortune/fortune_archive/1996/04/01/210990/index.htm [17] Definition from "Glossary of Competitive Intelligence Terms," Competitive Intelligence Review, Vol. 9, No. 2, April–June 1998, p. 66. [18] Hedley, Jr., J. H., 1996, "Checklist for the Future of Intelligence," Institute for the Study of Diplomacy, Georgetown University, Washington D.C., 1995. See also “IC21—The intelligence Community in the 21st Century, U.S. House of Representatives, Permanent Select Committee on Intelligence, March 4. [19] Hadjimanolis. (2002), "An investigation of innovation antecedents in small firms in the context of a small developing country", R&D Management Journal 30 ,235–245 [20] Sanna-Randaccio, F., & Veugelers, R., 2007," Multinational knowledge spillovers with centralized versus decentralized R&D: "A game theoretic approach". Journal of International Business Strategy, 38: 47–63.
