The impact of business intelligence through knowledge management

Abstract

Competition among companies has intensified during the last few decades and hence monitoring the organization’s environment has become a priority. Monitoring the internal and external environments involves collecting, retrieving, managing, and disseminating large volumes of data and information. Companies are able to handle these complex tasks very efficiently through knowledge management (KM). A valuable tool of KM is business intelligence (BI), that is, the set of coordinated actions of research, treatment, and distribution of information that can help support the company’s competitiveness. This study aims to evaluate BI and quantitatively demonstrate its impact on the competitiveness of an organization. It proposes a methodology and applies it to a multinational food processing company to determine the influencing elements in BI and measure their impacts on the organization’s competitiveness. This study identified four variables of BI that are likely to have an impact on the competitiveness of the company: the search for information, the treatment of information, the utility of information, and information security. To collect the required data, this study developed a survey with five categories, namely, research, utility, treatment, security, and competitiveness, and the collected data were analyzed using second-order partial least square-structural equation modeling in SmartPLS 3. This study found that research, utility, treatment, and security have positive correlations with BI, and that the strength of the relationship between BI and each variable is significant. Furthermore, the results show that the BI elements can explain over 38 percent of the variation in the competitiveness of the company.

Keywords

Business intelligence competitive advantage information analysis information management knowledge management

Introduction

Knowledge management (KM) is an important tool for the growth and competitiveness of any organization. Jelenic (2011) mentioned that KM is a vital resource for organizations of any size facing competition in any type of market. The amount of available data and the variety of their sources make it challenging to manage and use the resulting information for the benefit of the organization. Some companies utilize KM systems to manage complex knowledge. KM systems help organizations to identify patterns in data in order to create information and improve internal processes – such as the ones in financial, marketing, operations, and design managements – as well as those processes external to the organization. Nonaka (2007) presented in detail the significance of becoming a ‘knowledge-creating’ company. Figure 1 illustrates the way in which knowledge transformations generate the foundation of business intelligence (BI). Nonaka (2007) strongly argued that knowledge creation is the key to continuous innovation and that knowledge can transform from tacit to tacit, explicit to explicit, tacit to explicit, and explicit to tacit. Figure 1 also shows that the transformation of knowledge from tacit to tacit and tacit to explicit constitutes the foundation of BI.

Figure 1.

Knowledge transformations generate the foundation of BI (Nonaka, 2007).

A knowledge domain encompasses knowledge from various elements of the internal and external environments. Shaikh et al. (2018) provide a list of elements within each category. They list competitors, information and communication technology (ICT), social networks, suppliers, distributors, government policies, and sustainability issues as the most prominent. The key elements of the internal environment are employees, resources, designers, and planners, among others. Competitors are at the top of the list of external elements, indicating their significance. The knowledge surrounding this element is of paramount importance if the organization were to survive in the current business environment. The investment made in acquiring knowledge from competitors is part of an organization’s BI. BI is therefore an inherent element of KM and experts utilize various tools to find valuable information to formulate internal and external business strategies.

Nielsen (2006) listed eight activities as parts of KM: knowledge creation, acquisition, capturing, assembling, sharing, integration, leveraging, and exploitation. He then categorized these activities into three dynamic capabilities such as knowledge development, knowledge (re)combination, and knowledge use. He argued that it is imperative to study BI in detail as well as the link between KM and BI. Figure 2 depicts the interactions between the two concepts.

Figure 2.

Interaction between BI and KM activities. KM: knowledge management; BI: business intelligence.

Food processing is an emerging industry subjected to an important daily flow of information. Companies in this industry process agricultural products for public consumption or for ingredients in further processing. This industry includes the preservation of agricultural products as semi-dried products after initial or intermediate processing or as finished products (Pongpattanasili, 2004: 20). This study examined a food processing company in Algeria in order to understand the significance and usefulness of BI in this industry.

We structured the rest of the paper as follows. The second section presents an overview of BI followed by the link between KM and BI in the third section. The fourth section presents the proposed hypotheses to be tested. The fifth section presents the conceptual model and methodological design. The data analysis of the results of using the methodology described in the fifth section is presented in Appendix 1. The sixth section presents the discussion of results as a conclusion of the article.

Business intelligence

While BI is relatively a recent terminology, the first work highlighting the importance of collecting information for economic purposes dates back to the First World War. Other historical facts also point out that information collection was already carried out in the wharves of ports on newly arrived sailors. ‘Under the reign of Louis XIV, the official envoys of the kingdoms of France, England and Spain already called upon the systematic collection of economic, political, social and strategic information to inform their monarch, not only about the state forces of the enemy but also on the state of its economy’ (Harbulot and Baumard, 1997: 4). It was in 1915 that the German engineer Siegfried Herzog wrote the book The Future of German Industrial Exports in order to anticipate the country’s economic policy after the end of the war by creating a ‘commercio-industrial federation’ for collecting information and using it to preserve the competitive advantage of the German industry (Herzog, 1918: 132). In the mid-19th century, Japan began to collect and process information at the national level, highlighting the importance of information and considering it a ‘collective resource to be fully exploited’ (Jakobiak, 2001: 68). In 1979, Michael Porter emphasized the effect of information technology on competitive advantage. In the book Competitive Strategy: Techniques for Analyzing Industries and Companies, he speaks explicitly of the term ‘competitor intelligence system’ (Porter, 1980: 72). Thus, BI began to emerge in the economic world and made its official appearance in France in February 1994, following the recommendations in a report by the group ‘BI and Business Strategy’, chaired by Henri Martre (Martre, 1994: 3). It was only in 2006 that BI was mentioned in an official speech of the Algerian government (Fekir, 2009).

The global environment of a company is composed of several sectors such as the technological, institutional, political, economic, legal, and sociological. It is evident that competition at the national and international levels is becoming increasingly more intense, and the competitive advantage that some organizations hold is comprised of only a few small differences. In simple words, competitiveness is the ability of an organization to face its competitors. It represents its long-term performance and growth based on three criteria: price, quality, and cost (Okamba, 2005: 18). It is well acknowledged that measuring and managing business performance is a challenging process. Rajnoha et al. (2016) presented BI as a key information and knowledge tool for strategic business performance management. They argued that due to fierce competition and the unpredictable environment, the organization needs to establish a surveillance and monitoring system for information collection to detect threats and seize opportunities. Information is therefore the pivotal element in the functioning of this system.

The link between KM and BI

Several researchers (e.g. Cheng and Cheng, 2011; Weidong et al., 2010) provide comprehensive discussions on the similarities and differences between KM and BI. Cheng and Cheng (2011) concluded that KM and BI have a different system framework. They argued that organizations can benefit if the integration of KM and BI is based on their common characteristics. Walker and Millington (2003) provide a simple way to link KM and BI. One of the core elements of KM is capturing data, which can be accomplished using a variety of different tools. BI is one of the tools used in obtaining critical information that can immediately have an impact on an organization’s strategies as well as its operational plans. Walker and Millington (2003) further confirm that inclusion of BI as part of KM practices has become a daily routine of KM personnel.

Cody et al. (2010) acknowledge that BI and KM are two technologies that have been vital in enhancing the quantitative and qualitative value of knowledge available for decision-makers. BI is about collecting relevant information from internal and external sources. The exponential growth in ICTs has created opportunities to capture and disseminate information on a massive scale. At the same time, the abundance of data has created more challenges to finding the right information; therefore, KM has become crucial. It is important to screen the vital information and identify trends through different techniques such as data mining. Wang and Wang (2008) noted that data collected by a company are connected by unknown relationships and therefore the role of data mining is to find the interesting relationships among the data. Thus, improving knowledge means integrating data mining with KM. Trninic et al. (2011) highlighted that contemporary business operations are based on BI in which data warehousing plays an integral part. They studied the significance and functional application of data warehouses in KM systems. They also argued that data warehousing can be an important basis for creating information, which can subsequently be used for knowledge acquisition.

The identified patterns and trends via data intelligence will be a source for counterintelligence for organizations that can benefit from interventions. These interventions can be through design and development or even sales strategies. Researchers believe that the integration of BI and KM will be immensely helpful to organizations. The integration is done at three levels: presentation, data, and system (Weidong et al., 2010). While the immaturity of text analysis was once a noticeable drawback for integration, that issue has been resolved with the development of new advanced technologies.

BI combines data gathering, data storage, and data management with analytical tools to present complex internal and external information to planners and decision-makers (Negash, 2004). Negash (2004) argued that BI is a set of coordinated actions of research, treatment, and distribution of information that can help economic factors but it is neither a product nor a system. It is an architecture and a collection of integrated operations – as well as decision-support applications and databases – that provide the organization easy access to business data. The road map of BI specifically addresses decision-support applications and databases (Moss and Atre, 2003: 4).

Shehzad and Khan (2013) identified a number of critical success factors related to both BI and KM technologies from the literature and assessed their effectiveness with similar research studies. They proposed a KM model that is comprised of the operational layer, the BI and KM layer, and the output layer. They demonstrated that the KM and BI components interact with each other to provide users with a comprehensive output. The concept of real-time BI is also studied in the literature and several models have been proposed. Alsuwaidan and Zemirli (2015) posited that KM is much needed in real-time BI applications in order to facilitate decisions during critical times. They proposed a model for integrating KM capabilities into a real-time BI process.

As noted by Herschel (2005), KM deals with both tacit and explicit knowledge while BI normally focuses on explicit knowledge. He studied the importance of integrating KM and BI, distinguished between these two elements, and provided the argument that BI improves knowledge. He also argued that KM and BI both contribute in building the intellectual capital of an organization. The obvious benefits of enhanced knowledge are enhanced decision-making and organizational performance (Herschel, 2005; McHenry, 2005; Weidong et al., 2010). Vinekar et al. (2009) also confirmed that the combination of BI and KM provides better support for decision-making. They elaborated that, while BI identifies the potential weaknesses and opportunities, KM supports the design, implementation, and process monitoring. Measuring and managing business performance is a challenging process. Based on the findings of this study, the key tool for increasing the overall economic performance of a company is to employ a strategic performance management tool supported by a knowledge-based BI.

Proposed hypotheses

According to the literature, BI can be defined in different ways. One simple way to define it is as a process that includes two primary activities: getting data in and getting data out (Watson and Wixom, 2007). The BI process includes several phases: identification of information needs, information acquisition, information analysis, storage, and information utilization (Lönnqvist and Pirttimäki, 2006). As such, BI receives untreated information that must be classified according to established criteria and processed through human analysis in order to provide useful information (Negash, 2004). BI can also be considered as a moving process that must be adapted to the expectations of an organization (Olszak, 2016). BI is rapidly gaining popularity as organizational leaders are recognizing the importance of its contribution to accomplish strategic advantage (Watson and Wixom, 2007). BI is not only an effective tool in decision-making in firms, but it is considered more efficient than material factors (AL-Shubiri, 2012). Thus, this study formulates the following hypotheses:

H1: There is a positive relation between the search of information and BI.

H2: There is a positive relation between the utility of information and BI.

H3: There is a positive relation between the treatment of information and BI.

H4: There is a positive relation between information security and BI.

Business analytics is one of the four major technology trends since the 2010s. Leading organizations acknowledge the significance of BI in business analytics and report that BI has gained attention in both the professional and academic fields (Chen et al., 2012). Technological assets are the foundational capabilities necessary for achieving success in BI (Işik et al., 2013). The two objectives in the implementation of BI are consistency and transformation. Organizations adopting BI for data consistency use a comprehensive data collection strategy, whereas organizations adopting BI for transformation use a problem-driven data collection strategy (Ramakrishnan et al., 2012). Trieu (2017) presented a literature review on BI and concluded that researchers normally focused on the conditions necessary for the success of BI while they ignored the probabilistic process that links the conditions. The most effective way to prove the importance of BI is to quantify and to measure it (Lönnqvist and Pirttimäki, 2006). Thus, this study hypothesizes the following:

H5: BI has a positive impact on the competitiveness of a company.

Conceptual model and methodological design

Very few articles can be found about BI and the food processing industry. One case study was conducted at the National Foods Industry in Pakistan, where BI was developed to process raw data to give an overall representation of performance (Asif et al., 2017). The process significantly reduced the time spent in processing data usage. The BI process was composed of three main phases: extraction of the data set, transformation to appropriate data structures, and loading the data warehouse and the workflows (Asif et al., 2017). This study aims at filling the gap in the literature by measuring the impact of BI in the competitiveness (COM) of an organization in the food processing industry, and by demonstrating a positive relation between BI and its four elements: the search of information (SEA), the utility of information (UTI), the treatment of information (TRE), and information security (SEC). This study adopted the structural model shown in Figure 3 to analyze the impact of BI on the competitiveness of the organization. The second-order model in Figure 3 represents the assumption that the common underlying second-order formative construct BI can account for the seemingly distinct but related first-order constructs: SEA, UTI, TRE, and SEC. The latent variables, SEA, UTI, TRE, SEC, and COM, constitute reflective measurement models (for simplicity not shown in Figure 3).

Figure 3.

BI is a second-order construct while SEA, UTI, TRE, and SEC are first-order constructs. BI: business intelligence; SEA: search of information; UTI: utility of information; TRE: treatment of information; SEC: information security.

The questionnaire given in Table 1 was adopted for this study and it consists of 20 items grouped under five variables. The first four constitute the BI elements while the fifth variable is the competitiveness. By filling the questionnaire, the respondents expressed their choice of disagreement or agreement according to a five-level Likert-type scale. The answer ‘strongly disagree’ was coded as 1, the answer ‘rarely agree’ coded as 2, the answer ‘neutral’ coded as 3, the answer ‘somewhat agree’ coded as 4, and the answer ‘strongly agree’ coded as 5.

Table 1.

Questionnaire composed of five variables: SEA, UTI, TRE, SEC, and COM.

Experience: □ 0–5 years □ 5–15 years □ 15–25 years □ 25 years and over
Educational level: □ Basic □ Secondary □ University
Hierarchical level: □ Executive Officer □ Senior Executive □ Executive Agent
Variables	Items
SEA	SEA1. Collecting data in the business environment, such as customer suppliers and competitors, is essential
	SEA2. The layout of the tools (Internet watch, subscription database, newsletter, etc.) facilitates the data collection
	SEA3. The organization of the collected information improves their use
	SEA4. Sharing of information between staff is important
UTI	UTI1. The information collected is used to improve the positioning of the company
	UTI2. The information collected is used to improve customer expectations
	UTI3. Keeping watch over its environment serves to detect the threats and opportunities
TRE	TRE1. The Information and Communication Cell periodically analyzes the information collected
	TRE2. Internal databases, the intranet and the display, organize the dissemination of information within the company
	TRE3. The company can influence authorities and organizations to preserve these interests
	TRE4. The information collected allows innovation in the distribution, production, ‘servuction’ and management of the company
SEC	SEC1. Sensitive information is exposed to risks (theft, destruction, counterfeiting, etc.) – reversed scale
	SEC2. The company has an information protection policy
	SEC3. The financial and forecast data are safe
	SEC4. It is essential to control the sensitivity of the information before communicating it
	SEC5. Your servers and computer workstations are sufficiently protected by software and security materials
COM	COM1. The market share of your company is great
	COM2. You offer the best prices on the market
	COM3. You have a good corporate image
	COM4. Your business has the ability to face the competition

Note: SEA: search of information; UTI: utility of information; TRE: treatment of information; SEC: information security; COM: competitiveness.

The subjects of the study were 30 upper level management personnel of the Western Regional Commercial Direction of Cevital Food Company (Cevital Group). Cevital Group is the first Algerian private company with 18,000 employees spread over four continents: Africa, Europe, Asia, and South America (Cevital, 2016). It represents the flagship of the Algerian economy. The company has crossed important historical stages to reach its current size and a favorable reputation in the food, electronics, steel, automotive, and other industries. The Group has world-class production units equipped with the most advanced technologies and its strategy is based on strong competitiveness regarding price, quality, volumes, logistics, robotization, and colocation. Research and development, innovation, and the talent of its contributors are always the company’s top priorities. These competitive advantages form the basis of a dynamic and attractive industry that creates jobs for Algerian youth.

The detailed data analysis of the partial least square-structural equation modeling results is presented in Appendix 1.

Conclusion

Overall, the data analysis conducted above confirmed H1 (0.329, p = 0.000), H2 (0.392, p = 0.000), H3 (0.332, p = 0.000), and H4 (0.172, p = 0.017). That is, the search of information, the utility of information, the treatment of information, and information security have a positive and significant relationship with BI, that is, these are four different elements ‘forming’ BI. The data analysis also confirmed H5 (0.618, p = 0.000), that is, BI – as a whole – has a positive, important, and significant influence on the competitiveness of the company. Furthermore, the second-order model was able to explain 38.2 percent of the variation of the competitiveness of the company. Thus, the model and the instrument seem to be appropriate for conducting this type of study.

In particular, by analyzing the path coefficients in Figure 4, this study found that UTI is the most important element of BI followed very closely by TRE and SEA. This study also found that SEC has the least influence on BI. These findings were somewhat expected since in the opinion of the company’s personnel, the usefulness (utility) of the information is the BI element that can potentially create the most value for the company, that is, increase its competitiveness. The company could also benefit from an increase in the perception of the security of information, perhaps by improving its practices in this area. More interesting, though, is the examination of the total effects on the competitiveness of the company. Table 4 shows the total effects of the four predecessor constructs, UTI (0.242, p = 0.000), TRE (0.205, p = 0.000), SEA (0.203, p = 0.000), and SEC (0.106, p = 0.024) on COM via the second-order construct, BI. The total effects of UTI, TRE, SEA, and SEC follow the same pattern of importance as their effects on BI discussed above. That is, UTI seems to be the BI element that influences COM the most while SEC influences it the least. By inspecting the outer weights (not shown) it can be identified that scale items SEC4 ‘It is essential to control the sensitivity of the information before communicating it’ and SEC5 ‘Your servers and computer workstations are sufficiently protected by software and security materials’ have the lowest outer weights. The company could benefit from addressing these two aspects of information security.

Figure 4.

Structural model results.

In summary, the competitive advantage of information cannot be derived from untreated raw information, and the fact of collecting and organizing information does not systematically generate competitive advantage. All data must pass through the process of BI within the KM framework and only then useful and exploitable information, considered as intelligent, offers this advantage to a company. It has to be noted that BI is an embedded element in the big picture of KM. The use of BI as a tool is fundamental in KM for the creation of new knowledge and combining it with the existing knowledge. The value provided by BI through KM is promising. Recall that KM is the process of managing knowledge while BI is the process that transforms raw information into intelligent and useful information for decision-making. This empirical study demonstrated a positive relationship between the elements; search of information, utility of information, treatment of information, information security, and BI; as well as the impact of BI on the competitiveness of a company.

Footnotes

Appendix 1 Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

AL-Shubiri

(2012) Measuring the impact of business intelligence on performance: an empirical study. Polish Journal of Management Studies 6: 162–173.

Alsuwaidan

Zemirli

(2015) Toward a knowledge-based model for real-time business intelligence. In: Proceedings of the 2015 Science and Information Conference, SAI 2015, America Square Conference Centre, London, UK, 28–30 July 2015.

Asif

Hina

Mushtaq

(2017) Business intelligence solution for food industry. Journal of Basic & Applied Sciences 13: 442–447.

Cevital (2016) Cevital. Available at: https://www.cevital.com/en/ (accessed 12 September 2018).

Chen

Chiang

RHL

Storey

(2012) Business intelligence and analytics: from big data to big impact. MIS Quarterly 36: 1165–1188.

Cheng

(2011) Integration: knowledge management and business intelligence. In: 2011 fourth international conference on business intelligence and financial engineering, Wuhan, China, 17–18 October 2011, pp. 307–310. Piscataway, New Jersey: IEEE.

Cody

Kreulen

Krishna

, et al. (2010) The integration of business intelligence and knowledge management. IBM Systems Journal 41: 697–713.

Fekir

(2009) Intelligence Économique et Stratégie D’entreprise, État de la Question et Pratique en Algerie. Ben Aknoun, Alger: Institut National de Commerce.

Hair

Hult

Ringle

, et al. (2017) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks: SAGE.

10.

Harbulot

Baumard

(1997) Perspective historique de l´intelligence economique. Intelligence Économique 1: 1–17.

11.

Herschel

(2005) Knowledge management and business intelligence: the importance of integration. In: Proceedings of the European conference on knowledge management, ECKM (ed Remenyi

Dan

), Ireland, 8–9 September 2005. Academic Conferences Limited.

12.

Herzog

(1918) The Future of German Industrial Exports. New York: Doubleday Page and Company.

13.

Işik

Jones

Sidorova

(2013) Business intelligence success: the roles of BI capabilities and decision environments. Information and Management 50: 13–23.

14.

Jakobiak

(2001) L’intelligence Économique en Pratique avec L’apport d’Internet et des NTIC. Comment Bâtir son Propre Système D’intelligence Économique, 2nd éd. Paris: Editions d’Organisation.

15.

Jelenic

(2011) The importance of knowledge management in organizations: with emphasis on the balanced scorecard learning and growth perspective. In: Management, knowledge and learning international conference, Celje, Slovenia, 20–22 June 2011, pp. 33–43. International School for Social and Business Studies.

16.

Lönnqvist

Pirttimäki

(2006) The measurement of business intelligence. Information Systems Management 23: 32–40.

17.

McHenry

(2005) Linking decision artifacts: a means for integrating business intelligence and knowledge management. Electronic Journal of Knowledge Management 14: 91–102.

18.

Martre

(1994) Intelligence Économique et Stratégie des Entreprises. Paris: Rapport du commissariat général au Plan.

19.

Moss

Atre

(2003) Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications. Boston, MA: Addison-Wesley Professional.

20.

Negash

(2004) Business intelligence. Communications of the Association for Information Systems 13: 177–195.

21.

Nielsen

(2006) Understanding dynamic capabilities through knowledge management. Journal of Knowledge Management 10: 59–71.

22.

Nonaka

(2007) The Knowledge-Creating Company. Brighton: Harvard Business Review.

23.

Okamba

(2005) Mondialisation, Concurrence et Compétitivité les Enjeux Stratégiques Pour les Entreprises de la Caraiübe. Paris: Publibook.

24.

Olszak

(2016) Toward better understanding and use of business intelligence in organizations. Information Systems Management 33: 105–123.

25.

Pongpattanasili

(2004) An appropriate manufacturing strategy model for the Thai food processing industry. University of Wollongong Thesis Collection 1954–2016. Available at: https://ro.uow.edu.au/theses/576 (accessed 13 September 2018).

26.

Porter

(1980) Competitive Strategy: Techniques for Analyzing Industries and Companies. New York: Free Press.

27.

Rajnoha

Štefko

Merková

, et al. (2016) Business intelligence as a key information and knowledge tool for strategic business performance management. E a M: Ekonomie a Management 1(19): 183–202.

28.

Ramakrishnan

Jones

Sidorova

(2012) Factors influencing business intelligence (BI) data collection strategies: an empirical investigation. Decision Support Systems 52: 486–496.

29.

Rigdon

(2012) Rethinking partial least squares path modeling: in praise of simple methods. Long Range Planning 46: 341–358.

30.

Shaikh

Bashar

Rafiq

(2018) Emerging trends in ICTs and its impact on organizational innovation through knowledge management. International Journal of Business and Management 2(3): 1–8.

31.

Shehzad

Khan

MNA

(2013) Integrating knowledge management with business intelligence processes for enhanced organizational learning. International Journal of Software Engineering and its Applications 7: 83–92.

32.

Trieu

(2017) Getting value from business intelligence systems: a review and research agenda. Decision Support Systems 93: 111–124.

33.

Trninic

Durkovic

Rakovic

(2011) Business intelligence as support to knowledge management. Perspectives of Innovations, Economics and Business (PIEB). Prague Development Center (PRADEC) 08(2): 1–6. Available at: https://ideas.repec.org/a/ags/jrpieb/128645.html (accessed 21 February 2019).

34.

Vinekar

Teng

Chennamaneni

(2009) The interaction of business intelligence and knowledge management in organizational decision-making. Journal of International Technology and Information Management 18(2): 143–159.

35.

Walker

Millington

(2003) Business intelligence and knowledge management. Information Outlook 7(8): 38–40.

36.

Wang

(2008) A knowledge management approach to data mining process for business intelligence. Industrial Management and Data Systems 108: 622–634.

37.

Watson

Wixom

(2007) The current state of business intelligence. IEEE Computer Society 40: 96–99.

38.

Weidong

Weihui

Kunlong

(2010) The relationship of business intelligence and knowledge management. Available at: https://www.semanticscholar.org/paper/The-relationship-of-business-intelligence-and-Weidong-Weihui/a18cc68c13df9f75cdea3b2702d903175e07ce3a (accessed 20 February 2019).