Category Archives: Research

What is Business Intelligence

(early draft from the second chapter of my PhD)

According to the Oxford dictionary, the word ‘intelligence’ originates from the Latin verb ‘intelligere’ a synthesis of the words ‘inter’ meaning ‘between’ and ‘legere’ meaning ‘choose’. It has been stated that intelligence “reflects the ability to reason, solve problems, think abstractly, and acquire knowledge” (Snyderman & Rothman, 1988) and that it “is not the amount of information people know, but their ability to recognize, acquire, organize, update, select, and apply it effectively” (Gottfredson, 1997). In other words, intelligence is referred to capabilities that someone has or develops in order to collect data, evaluating them and taking proper decisions based on the right evidence.

Business intelligence (BI) has existed as a term since 1958 but over the years, it has been used in different ways and from different aspects (Pirttimäki, 2007a). When Hans Peter Luhn in 1958 introduced the term, he described an automatic system for information dissemination by utilizing data-processing machines for abstracting, encoding and archiving any kind of documents of an organization (Luhn, 1958). In this early conception of BI by Luhn, the term business was considered as the “collection of activities carried on for whatever purpose” while the notion of intelligence was defined as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal”. The overall objective of the system proposed by Luhn was by and large to speedily and efficiently support organizational actions by providing suitable information.

Gilad and Gilad (1985), referred to BI as a process that produces relevant and reliable information for the company’s strategic goal and objectives. In their work they pinpointed that BI is not just collecting data but it is rather a process for converting raw data into intelligence for decision making. According to them, this process requires five steps namely data collection; storage; evaluation; analysis; and dissemination. The purpose of these steps is to act as information filters; information quality control mechanisms; and information channels for the users.

Ghoshal and Kim (1986) soon recognized BI as an essential competitive tool for the collection and analysis of information on markets, new technologies, customers, competitors and broad social trends. Later, Vedder, et al (1999) suggested a dualistic approach referring to BI as both a process and as a product. According to Vedder et al, BI as a process is “the set of legal and ethical methods a company use to harness information” and as a product, it is the “information about competitors’ activities from public and private sources”.

Pirttimäki (2007b) in her doctoral dissertation examined the different points of view on BI and described it as a multifaceted concept that refers to processes, technologies, methods, information products, and tools to support managing business information and making faster and better decisions. Pirttimäki (2007b, p. 92) stated that BI is “[the] information process that contains a series of systematic activities driven by the specific information needs of decision-makers and the objectives of achieving competitive advantage.”

Williams and Williams (2007) in their book argued that BI is neither a single product, nor a technology, nor a methodology. According to them, all three are combined in BI to organize key information that management needs to improve both performance and profit. They conceived BI as “business information and business analyses within the context of key business processes that lead to decisions and actions”. The improved business performance is the result from the decisions and actions that leverage information assets of the enterprise.

According to the IT glossary of Gartner Group (2013) BI is “an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance”. Prior (2009), in his glossary of terms used in business intelligence and knowledge management, refers to BI as being concerned with information technology solutions dedicated to transform large data collections into intelligence. According to Prior, BI covers activities such as customer relationship management, enterprise resource planning and e-commerce using data mining techniques. Although several definitions for BI have been appeared in scientific and professional literature (Popovič, Turk, & Jaklič, 2010) it has been stated that BI is a poorly defined term (Arnott & Pervan, 2005, p. 71) mostly due to its industry origin in which different software vendors and consulting organizations defined BI to suit their product offerings.

Depending on the scope and the data used for BI, a variety of different types of intelligence have been presented in the literature. Tyson (1986), pointed out a number of different concepts of BI; while Liebowitz (2006, p. 13) claimed that “… wherever you turn, a new type of ‘xyz intelligence’ emerges”. According to (Choo, 2002, p. 86) BI has a significantly larger scope than other intelligence concepts. Generally, the other BI concepts are considered to be subgroup of BI. Almost all of them have the same purpose as BI has, that is to turn data into valuable knowledge and help decision makers. Pirttimäki (2007a) points out that the difference between other intelligence concepts and BI is not clear, as the way intelligence is managed stays the same. Related intelligence concepts discussed in the literature include:

  • competitive or competitor intelligence (Bernhardt, 1994; Fleisher, 2001; Fuld, 1994; Miller, 2001; Zanasi, 1998);
  • customer intelligence (Davis, 2003; Harvey, 2000);
  • market intelligence (Cornish, 1997; Kohli & Jaworski, 1990);
  • product intelligence (McFarlane, Giannikas, Wong, & Harrison, 2013; Rijsdijk, Hultink, & Diamantopoulos, 2007; Wong, McFarlane, Ahmad Zaharudin, & Agarwal, 2002);
  • strategic intelligence (Liebowitz, 2006; McDowell, 2008);
  • technological intelligence (Mortara, Kerr, Probert, & Phaal, 2007; Tacskin, Adali, & Ersin, 2004);
  • counterintelligence(DeGenaro, 2005; Nolan, 1999);
  • and more recently social business intelligence (Dinter & Lorenz, 2012; Heijnen, 2012; Zeng, Chen, Lusch, & Li, 2010).

Competitive intelligence

Competitive intelligence is the process by which enterprises collect and manage information about their competitor environment and apply it to their decision-making (Fleisher, 2001). According to the Society of Competitive Intelligence Professionals (, competitive intelligence is a systematic and ethical program for gathering, analyzing, and managing external information that can affect a company’s plans, decisions and operations. Similarly, competitor intelligence refers to the information of a single competitor of the enterprise. The focus of competitive intelligence is on the external business environment and more specifically on the competitors of an enterprise. Competitive intelligence must not be confused with espionage which deals with illegal means for gathering information about the competition. Competitive intelligence depends in any kind of publicly available sources about the competitions, such as, magazines, financial reports, TV, advertisement, competitors’ web sites, customer opinions and experiences, benchmarks from various vendors, panel data, etc.

Customer intelligence

Customer intelligence is the process of gathering and analyzing information about the clientele, aiming to build deeper and more effective relationships with the customers, to deliver a better customer experience and to increase customer yield (Davis, 2003). The focus of customer intelligence is to effectively understand customers in terms of who they are, what they need, and how valuable are to the company (Harvey, 2000). The insights of customer intelligence can be used in order to meet and exceed the customers’ expectations. Key resources for customer intelligence include historical data of transactions; survey and questionnaires addressed to customers; communications with customers (e.g. email); purchase patterns; product registration; focus groups; clickstream or online behavior; online communities; etc. These data can be either found online, or can be extracted from internal data repositories and information structures maintained within the enterprise. For example, customer relationship management (CRM) tools are used to handle customers’ interactions with the enterprise and to collect valuable data that can be used for customer intelligence.

Market intelligence

Market intelligence exploits information related to the company’s markets which is used for the decision making process. Specifically, market intelligence is targeted on the dynamics related to consumers and place, price, product and promotion; so it is focused on data from short time periods aiming for determining opportunities and penetration strategies in the market. According to Kohli and Jaworski (1990), market intelligence is focused on understanding the market (both existing and new); determining current and future needs and preferences, attitudes and behavior of the market; and assessing changes in the business environment that may affect the nature of the market in the future. The scope of market intelligence goes beyond the customer needs and preferences while it includes monitoring the competitors’ activities. The main sources for market intelligence are customer surveys; sales reports; interviews with trade partners, formal market research, etc.

Product Intelligence

The notion of product intelligence is referred to the operations in which parts, products or collections of products can monitor and influence their own progress within the supply chain (McFarlane et al., 2013). Taking advantage of Information Technology, products are embedded with microchips, sensors or software and are able to collect, process, produce and transmit information (Wong et al., 2002). Such products have a unique identifier and are able to communicate with their environment. They can store or transmit data, which can be used for decision making. These data contain information on the ways that the object was made, how it was stored or transported and general information about its status.

Strategic intelligence

The term strategic intelligence is referred to the need for information for the high level strategic decision makers. According to Liebowitz (2006, p. 22) strategic intelligence is “the aggregation of the other types of intelligentsia to provide value-added information and knowledge toward making organizational strategic decisions”. Strategic intelligence is concerned with analysis of issues and making forecast to achieve the strategic objectives of the enterprise (McDowell, 2008). The term is used in the contexts of strategic planning and management and its focus is mainly in proactive activities for the long term. It has a broader scope than other intelligence related concepts and takes advantage of any kind of available intelligence.

Technological intelligence

Technological or technology intelligence refers to the processes of collecting information and taking organization decisions based on current or expected technological changes. The term is relevant to the technology scouting and technology forecasting (Coates et al., 2001). The focus is to identify potential opportunities or threads for the enterprise from the advent of new technologies (Mortara et al., 2007). Key resources for technology intelligence comes from many different science and technology dissemination channels (Zhu & Porter, 2002) for R&D information in the form of research publications, patents, white papers, etc.


According to Nolan (1999) counterintelligence includes the active measures undertaken to identify and neutralize the information collection activities of business competition. The concept of counter intelligence is not new. It became more important when executives realized how accurate information they could obtain through their competitive intelligence activities and begun to wonder how vulnerable their companies are. According to DeGenaro (2005) counterintelligence is not a security discipline but the focus is on the deeper understanding of the competitors intentions and capabilities.

Social business intelligence

Social business intelligence is a rather new concept referring to a) the general ability of companies to utilize social media data for decision making; and b) the use of social media and other web-based application in the cloud in order to enhance the collaboration and the dissemination of the products of BI across the enterprise. It is common that social business intelligence to be used interchangeably with other terms such as “social media analytics”, “social media intelligence”, “social intelligence” and “business intelligence 2.0” (Dinter & Lorenz, 2012). In this thesis, social business intelligence is considered as the actions to utilize social media data.

Taking a closer look at the aforementioned concepts of BI, it stands to argue that the terms used mostly refer to the specific type of decisions required in particular situations, the source of the data facilitating such decisions and the technological infrastructure needed to support the appropriate level of analysis. In all cases, capabilities of BI appear to be inextricably intertwined with technical capacities and the intrinsic properties of the information infrastructure in place for acquiring and analyzing data.

BI is a rather ambiguous concept referring as much, in the managerial process dealing with making decisions, as in the technology-based solutions transforming data into information and then to actionable knowledge which leads in increased business performance.

To this end, BI can be considered as a broad concept referring to the set of activities (e.g. technological, methodological, procedural; architectural) carried on by organizations to understand (i.e. transform from raw data, to information and then to knowledge) their internal and external environment and use that knowledge in decision making.


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Organizational knowledge and knowing – paper reviews

Organizational learning and communities-of-practice: Toward a unified view of working, learning, and innovation

Brown, J.S., and Duguid P.


This paper builds on ethnographic studies and reports in order to draw attention on the differences existing between the way people actually work and the way organizations describe that work. Manuals, training programs, job descriptions and organizational charts describe canonical work practices but as the ethnographic studies have shown people more than often face circumstances that these practices cannot be applied. Storytelling, collaboration and social construction are prescribed by non-canonical practices that people mobilize for confronting everyday situations in their work. All these being social mediated facilitate the formation of groups of people that according to researchers foster a non formal transfer of knowledge between participants. Within these small, self-constituting communities the non-canonical practices are continually developing new interpretations of the working environment leading to innovation in the form of altering the practice. Authors conclude that there is a gap between the assumptions and the beliefs of what working, learning and innovating seem to be and what actually are. In order to close that gap organization must look beyond canonical abstractions of practice and identify itself as community-of-communities. Also must legitimize and support the enacting activities perpetrated by its members. Authors overall objective was to show where constrains and resources lie in this research developing the argument that there must be a more closer, realistic and reflective linking of working, learning and innovating that was at the moment that this paper was published.


Bridging epistemologies: The generative dance between organizational knowledge and organizational knowing

Cook S.D.N., Brown J.S.


Authors’ main objective in this chapter was to broaden the existing understanding of what and how people know. They identify four categories of knowledge inherent in the explicit-tacit and individual-group distinctions arguing by the use of illustrative examples that each form of knowledge does work the others cannot; can often be used as an aid in acquiring the others; and no one of them can be derived from or changed into one of the others. They make clear the distinction between knowledge and knowing defining knowledge as something that is possessed such as rules, procedures, equations, etc. and the knowing as the epistemic work (i.e. the work that people must do in order to acquire what they need to know for doing what they do) that is done as part of action or of practice. The research related to knowing is enriched by these distinct kinds of knowledge, productive inquiry, dynamic affordance and the generative character of knowledge; notions that has been provided and described in the lines of this chapter by the authors. Using three cases (i.e. bread-making machine design, flute-making companies and Xerox blank paper handling) they make clearer some of the implications of their perspective. Concluding, authors suggest the need for further theoretical and empirical work in the form of cases studies of knowledge-creating organization, knowledge work and knowledge management.


A dynamic theory of organizational knowledge creation

Nonaka I.


The objective of this paper is to develop a theory dealing with the dynamic aspects of the organizational knowledge creation process. Basic concepts and models of organizational knowledge creation indicate that a continual dialog between tacit and explicit knowledge drives the creation of new ideas and concepts. Also, although information and knowledge are distinct notions the first is a necessary medium for initiating and formalizing the latter. To this end, information is conceptualized as a flow of messages while knowledge is created and organized by the very flow of information. While the prime movers in the knowledge creation are the individual members; there are three basic factors that induce the individual commitment in an organizational setting: (a) intention, which has to do with the fact that individuals form their approach to the world and try to make sense of their environment, (b) autonomy, as every individual within an organization may have different intentions and (c) fluctuation, as knowledge creation at the individual level involves continuous interaction with the external world. Author forms a “spiral” model bridging the epistemological and ontological dimensions of knowledge creation, identifying the existence of four patterns of interaction that represent ways in which existing knowledge can be converted in new knowledge. The knowledge is creating through these four conversion processes that is, from tacit to tacit (i.e. socialization), from tacit to explicit (i.e. externalization), from explicit to explicit (i.e. combination) and from explicit to tacit (i.e. internalization). Enlarging individual knowledge, sharing tacit knowledge, crystallization, justification and networking knowledge are the five organizational knowledge creation processes. Author proposed two management models namely, middle-up-down management and hypertext organization. Middle-up-down management is suitable for promoting the efficient creation of knowledge in business organization while the hypertext organization links related concepts and areas of knowledge allowing a problem to be viewed from many angles.

Customer relationship management – paper reviews

The CRM imperative – Practice vs theory in the telecommunications industry

Wright L.T., Stone M., and Abbot J.

The main objective of this paper is to examine how businesses were enabled to focus on the customer taking advantage of the Information Technology. Using three case studies based on three different telecommunication companies, authors made an attempt to understand what affects Customer Relationship Management (CRM) when it is actually applied. Two traditionally telephony companies and a new-entrant cable firm applied CRM technologies in order to retain and win costumers in the telephony market but each company dealt with the introduction of its CRM in a different way. Without intentions of providing prescriptions for building CRM, authors identify some key points showing areas where the adoption of CRM could enhance organizational practices such as: market orientation, improvement of collaboration, people management and commitment, knowledge acquisition and management, management and proliferation of data, efficiency and effectiveness, and finally, speedy solutions and profitability. What the case studies have shown was that when companies faced with the dilemma of investing in new technologies, new products etc they could also invest simultaneously in improving their customer relationship skill.

CRM systems: Necessary, but not sufficient. REAP the benefits of customer management

Starkey M., Woodcock N.

Spending on Customer Relationship Management not always benefits the expected for a company. Furthermore investing in CRM sometimes can waste opportunities for a company and destroy economic value. The lack of senior executive ownership and leadership, the unnecessarily complexity in designing CRM, the dysfunctional approaches from the perspective of customer, the lack in knowledge of good customer management techniques, the poor implementations of CRM systems, etc are some of the reasons that authors identified as what makes customer management performance disappointing. Of course, there are examples of effective practices in companies. Authors believe that it is of great importance the investment to be in the ‘right things’ so customer management must include planning for Retention, Efficiency, Acquisition and Penetration. According to measurements in the case studies of this paper such a planning can provide four to one return on investment for well-managed programmes.

 Corridors of Influence in the Dissemination of Customer-Oriented Strategy to Customer Contact Service Employees

Hartline D.M., Maxham G.J., and McKee O.D.

Customer contact employees are responsible for carrying out the customer-oriented strategy of a company to end customers in the form of quality services. In this paper a model has been proposed explaining how a company can disseminate a customer-oriented strategy in order to increase commitment and shared values of its customer contact employees. The proposed model supports that important role in such dissemination plays the organizational structure, the empowerment, the behavior-based employee evaluation, the socialization and the organizational commitment. Authors validate their hypotheses conducting a questionnaire-based research including in their samplings nine hotel chains. Authors’ findings reveal the existence of three “corridors of influence” between customer-oriented strategy and the shared employee value. The first corridor emphasizes the importance of work group socialization and organizational commitment, the second corridor focuses on formalization and behavior-based evaluation whilst the last corridor focuses on the empowerment of costumer contact employees.

 Understanding why marketing does not use the corporate data warehouse for CRM applications

Payton C.F., Zahay D.

This paper tries to answer why marketing does not use the corporate data warehouse for customer relationship management implementations. Conducting a series of focus group interviews with functional marketing and information systems managers in one large regional health care payer, authors identify factors that prevent the adoption of corporate data warehouse in the organization. Marketing needs, data quality issues and training were the most important factors according to the findings of their analysis. Authors found that marketing managers have special data needs and these additional data cannot always be found in the data warehouse. Furthermore, the quality of data kept in the data warehouse may not be acceptable by the marketing managers and finally, the data analysis tools of data warehouse may be difficult to use.

A short synthesis

Customer Relationship Management is a process that allows organizations to gather and analyze customer data in an efficient way improving the customer retention. Therefore, it is very important for organizations to invest simultaneously in improving their customer relationship skill while investing in new products or technologies. Unfortunately, a great number of CRM applications are failing and this is due to the fact that the investment made by the companies for such applications is not enough or even worse, is not well targeted and designed. Organizations can use data that can be found in the corporate data warehouses in order to create and sustain their customer relationship management but to have such ability several issues must be resolved. From the other hand, customer-contact employees – which are responsible for translating a customer-oriented strategy into quality service – must be managed in a way seeking in achieving the employees’ desired performance. To this end, organizations need to invest in the ‘right things’ in order to establish a successful customer relationship management.