To cite this article: Papachristdoulou, E., Koutsaki, M. & Kirkos, E. (2017) 

Business intelligence and SMEs: Bridging the gap. Journal of Intelligence Studies 

in Business. 7 (1) 70-78. 

Article URL: https://ojs.hh.se/index.php/JISIB/article/view/201 

This article is Open Access, in compliance with Strategy 2 of the 2002 Budapest Open Access Initiative, which 

states: 

Scholars need the means to launch a new generation of journals committed to open access, and to help existing journals that 

elect to make the transition to open access. Because journal articles should be disseminated as widely as possible, these new 

journals will no longer invoke copyright to restrict access to and use of the material they publish. Instead they will use 

copyright and other tools to ensure permanent open access to all the articles they publish. Because price is a barrier to access, 

these new journals will not charge subscription or access fees, and will turn to other methods for covering their expenses. 

There are many alternative sources of funds for this purpose, including the foundations and governments that fund research, 

the universities and laboratories that employ researchers, endowments set up by discipline or institution, friends of the cause 

of open access, profits from the sale of add-ons to the basic texts, funds freed up by the demise or cancellation of journals 

charging traditional subscription or access fees, or even contributions from the researchers themselves. There is no need to 

favor one of these solutions over the others for all disciplines or nations, and no need to stop looking for other, creative 

alternatives. 

 

Journal of Intelligence Studies in Business 

Publication details, including instructions for authors and subscription 

https://ojs.hh.se/index.php/JISIB/index information: 

 

Business intelligence and SMEs: Bridging the gap 

Ekavi Papachristodouloua,  Margarita Koutsakia and 

Efstathios Kirkosa,* 

aDepartment of Accounting, ATEI of Thessaloniki, Greece; 

*stkirk@acc.teithe.gr 

 

Journal of Intelligence Studies in Business 

PLEASE SCROLL DOWN FOR ARTICLE 

 

https://ojs.hh.se/index.php/JISIB/index


 

 

    

 

 

 

Business intelligence and SMEs: Bridging the gap 
 

Ekavi Papachristodouloua,  Margarita Koutsakia and Efstathios Kirkosa,* 
 

aDepartment of Accounting, ATEI of Thessaloniki, Greece 

 

*Corresponding author: stkirk@acc.teithe.gr 

 

Received 15 February 2017; accepted 1 March 2017 

ABSTRACT According to research findings, small and medium enterprises (SMEs) are facing 
problems such as an excessively large volume of data, lack of information and lack of 

knowledge. Therefore, in order to make decisions on time, the managers of SMEs use mainly 

their experience, which implies a high risk of failure. Business intelligence (BI) is a useful and 

helpful tool, which brings many advantages and benefits to businesses. However, like any 

technology, it is accompanied by some limitations that must be overcome in order to help 

businesses to develop. This paper summarizes current research findings addressing the issue 

of the development and application of business intelligence systems for SMEs. The issues 

addressed are models for the estimation of the readiness of a SME to establish BI tools, 

alternative BI solutions for SMEs, benefits and challenges of BI in SMEs, implementation 

methods for BI systems in SMEs and finally, BI systems in cloud computing platforms. 

Research papers dealing with these issues are analyzed and the results are presented. This 

paper contributes to the understanding of problems and potentials regarding the development 

and application of BI systems in SMEs. 

KEYWORDS Business intelligence, competitive intelligence, SMEs 

 
1. INTRODUCTION 

Despite the economic size of each company, 

access to relevant and important information 

is very important to ensure the success of the 

acquisition of a market share. Business 

intelligence is considered a very important 

tool to achieve such a goal. According to the 

Gartner’s surveys, business intelligence (BI) 

and analytics systems are ranked as the top 

technological priority of companies in the last 

years worldwide. The main objective of BI 

systems is to facilitate the decision making 

process by providing quality information, 

based on the analysis of large amounts of 

internal and external data. However, BI 

systems are characterized by their difficulty 

and complexity to handle. Also, economic 

factors are the ones that make many SME’s 

administrations fail to proceed to the 

acquisition of a system. Normally, the 

development and maintenance of a BI system 

requires considerable funding. Moreover, the 

majority of SMEs do not have a specialized IT 

department. Many SMEs are run by the 

owners, who might not have advanced 

technological knowledge. It is known that the 

applications of BI are not primarily accessible 

to SMEs. The available systems are 

expensive, difficult to use and require 

excellent technological training of business 

staff. Commonly, these applications meet the 

needs of large enterprises, that have all the 

appropriate resources for their proper 

functioning.  

Despite these limitations, better 

information provision, facilitated by a BI 

system, may lead to better decisions and 

become a consistent competitive advantage. A 

prerequisite is the successful confrontation of 

problems, stemming from the specific 

characteristics of SMEs. With the evolution of 

technology, BI suppliers have designed and 

Journal of Intelligence Studies in Business 

Vol. 7, No. 1 (2017) pp. 70-78 

Open Access: Freely available at: https://ojs.hh.se/ 

 

 



 71 

developed applications and tools to meet real 

small businesses needs. Τhere are BI systems 

that are available online. These systems are 

affordable, easy and they belong to the 

category of cloud systems. Such solutions are 

suitable for SMEs, as they do not incur 

additional installation and maintenance cost. 

Tools and IT system applications are not 

considered a privilege of large companies, as 

the services offered are designed for the needs 

and requirements of SMEs, which can be just 

as competitive and successful. The present 

paper addresses a wide spectrum of issues 

related to the application of BI systems in 

SMEs. BI practitioners and SME managers 

might find this brief but concise 

summarization useful in their attempts to 

apply this cutting-edge technology in this 

specific business sector. 

2. READINESS OF AN SME FOR BI 

Hidayanto et al. (2012) conducted research to 

assess the readiness of a SME to establish a 

BI tool. For the development of the 

framework, the researchers used as their tools 

the Critical Success Factors and the 

Analytical Hierarchy Process. They focused on 

three categories of functions found in an SME. 

The framework formed by researchers 

primarily focuses on three main categories 

that are identified, which were developed and 

evaluated. These categories are organization 

where nine relevant factors were explored, 

process where four factors have been analyzed 

and technology where five factors were 

explored.  

In this study the researchers initially 

make a theoretical presentation on the 

development of the model and then they 

proceeded to a more detailed description. 

Finally, the proposed framework is applied to 

a real case of a SME. Through this research, 

they explored and evaluated the critical 

success factors, namely the elements that are 

necessary to ensure the success of such a 

venture in the evaluation and acquisition of a 

BI system. 

 We chose the Analytical Hierarchy 

Process method for the development of the 

proposed framework, because this method 

allows the analysis of a complex problem in a 

more simple structure and selects the most 

effective solutions that lead the 

administration to better decisions (Taylor 

2005, Cheng 1997). The researchers define the 

three steps of this method. 

The first step is the decomposition of the 

model into three levels (objective, criteria and 

alternatives). In the second step, the 

comparisons between pairs of criteria and 

alternatives were created. The comparison 

was made with a rating scale of 1-9. The third 

and final step is the weight of each pair. This 

method was used to give the weight of each 

factor based on specific criteria and the better 

alternative was the one with the higher 

weight.  

To be valid comparisons, the researchers 

chose values  less than 0.1 (consistency ratio 

<0.1). Then, they began to develop a 

framework that would apply in a real and not 

virtual enterprise. The target frame raised the 

level of readiness of BI in an SME (level 1). 

Criteria joined the function categories of 

business (level 2), while the critical success 

factors were considered alternatives (level 3). 

For the purpose of the study, Hidayanto et al. 

(2012) used 18 factors based on the scientific 

literature references by Atre (2003), Williams 

and Williams (2004) and Yeoh and Koronios 

(2010). 

For the category of organization, the 

critical success factors selected were 

committed management support  and 

sponsorship, clear vision  and well-established 

business case, strategic alignment, effective 

business/IT partnership for BI, BI portfolio 

management, continuous process 

improvement culture, culture surrounding the 

use of information and analytical applications, 

cross-organizational collaboration and 

decision process engineering culture. 

For the process category, the factors 

chosen were balance team composition, 

availability of skilled team members, business 

driven development approach and iterative 

development approach and user oriented 

change management. 

For the technology category business 

driven scalable and flexible technical 

framework, sustainable data quality and 

integrity, importance of metadata, BI and DW 

technical readiness and the silver bullet 

syndrome were selected. 

Once the problem decomposition process 

was completed, the researchers proceeded to 

create pairs of criteria and alternatives, with 

the help of four specialists in BI. Experts, 

using the Delphi technique, gave values to 

results which arose from four comparisons: i) 

the inter-category pairwise comparison, ii) the 

pairwise comparison for organizational 

category, iii) the pairwise comparison for 



 72 

process category and iv) the pairwise 

comparison for technology category. Finally, 

the validity of comparisons of each class of the 

consistency ratio was calculated (consistency 

ratio < 0.1) and the weight of each factor was 

calculated. 

To give a more accurate and fair decision 

about the value of each factor in business, the 

researchers used the e-GP model (electronic 

government procurement) Readiness Self-

Assessment. Thus, they evaluated the level of 

readiness of each factor using a scale (0-3) 

measuring each factor’s readiness level. 

The results of this research reaffirm the 

findings of previous research, mainly 

conducted by Williams and Williams (2004) 

and Yeoh and Koronios (2010). According to 

the BI experts the most crucial factors in 

developing BI systems are the following: 

 

 Strategic alignment between business 

and IT. Consistency is required between 

business strategy, organization and 

processes and IT strategy, 

infrastructure, organization and 

processes. 

 Managements support and sponsorship. 

The determination of the management 

to support the project secures the 

availability of resources such as funding 

and human skills. 

 Clear vision and well established 

business. A clear strategic business 

vision is required. Such a strategic 

vision is needed for the establishment of 

a solid business case. Misunderstanding 

of the long-term vision and objectives 

may derail the BI project. 

 

Other important issues are the 

composition of the BI team and the quality of 

the data. It is critical to include business 

experts who understand the strategic vision in 

the BI team so they can foresee the 

organizational challenges. 

After the comprehensive development of 

the model, the researchers applied it to a real 

SME. They randomly chose an SME in 

Indonesia, which did not use a BI system. 

Through semi-structured interviews they 

assessed the level of preparedness of each 

factor separately and then multiplied it by the 

weight factor of the level of preparedness. 

After, they added all the results to give the 

final grade. The company managed to collect 

58.05%. The result showed that although the 

company understood the importance of the 

factors for the implementation of BI, it had to 

face some obstacles and then proceed to the 

implementation of BI. 

By applying a similar model, businesses 

will be able to analytically evaluate their 

readiness and then they can decide whether 

they will be able to deploy BI software, as they 

may be confronted with unexpected situations 

that may arise during the project. 

Management should be aware of the real 

needs of the enterprise and adopt 

corresponding services to manage and support 

them.  

3. BI SOLUTIONS FOR SMES 

Tutunea and Rus (2012) study alternative BI 

solutions for SMEs. In their research, they 

tested and evaluated the available commercial 

BI solutions, open source solutions and IT 

systems tools offered for small and medium 

businesses. The software tested was available 

on the websites of companies that provide BI 

solutions. For the evaluation of commercial 

solutions, they set two criteria. The first 

criterion set was the complexity that 

characterized the provided solutions. The 

variables set for this criterion were the 

functionality, maintenance and system 

support, accessibility and user interface and 

the final purchase price. The second criterion 

was characterized as the reputation of the 

company that was on the market. 

By conducting this research, they have 

concluded that, depending on company size, 

the management and some specific internal 

factors, there are three types of BI solutions 

that allow companies to choose the one that 

best fits their requirements and needs. 

As a first choice, they ranked the solutions 

developed within the company and did not 

involve specialized BI providers. These 

solutions mainly focus on static or dynamic 

analyses of the data with the help of Excel 

spreadsheets, Open Office Calc, Lotus 1-2-3, 

computer graphics, and what-if type analysis. 

Such BI solutions are part of SaaS (System-

as-a-Software) and have gained ground in 

their acquisition by SMEs. This is because the 

final purchase cost is low, it is easy to use and 

the installation time is very fast. Also, there is 

no further staff training. The products are 

hosted in a secure online platform where the 

company has access without leaking data. 

As a second option, the researchers ranked 

the commercial BI solutions. Of the software 

that was tried, they found that there are two 

types of providers. 



 73 

The first category includes specialized 

software companies that provide exclusive BI 

tools. Businesses rely on a specialized team to 

design the software according to their needs 

and requirements. Such providers are 

Information Builders, MicroStrategy, and 

QlickTeck. 

In the second category, they identified 

companies that have a greater variety of 

interests. In this category are the BI solutions 

that are aimed at a particular sector such as 

education, banking or insurance systems. 

Such providers include Oracle, Microsoft, 

SAP, and SAS. 

As a third option, they ranked BI solutions 

and open source solutions. The motivation 

that drives companies to proceed to the 

acquisition of such solutions is the low cost. 

Therefore, the architecture, the functionalities 

and their environment are considered to be 

the main criteria on which SMEs choose a BI 

tool. Providers of open source software are 

Actuate, Jaspersoft, Pentaho and SpagoBI. 

Enterprises can choose a suitable solution, 

taking into account the quality of the 

information provided, data analysis tools and 

visualization, cost, accessibility and 

effectiveness of the decisions. Thus, companies 

depending on resources choose the best 

solution that will bring advantages 

4. BENEFITS AND CHALLENGES OF 

BI IN SMES 

In this section two surveys aiming at 

identifying the benefits and challenges of BI 

adoption in SMEs are presented. Also, 

through the research, they identified the 

sections of their application. For this purpose 

were used two research studies by Scholz et 

al. (2010) and Nenzhelele (2014). 

Scholz et al. (2010) were able to identify 

the beneficial factors, challenges and types of 

SMEs that adopt BI tools. The authors study 

the adoption of BI by German SMEs by 

examining 214 firms in Saxony. 

The method applied was based on 

references of other authors and researchers. 

The study was based on Exploratory Factor 

Analysis (EFA), which identifies the perceived 

benefits and challenges of implementing BI. 

Initially, to verify the suitability of the sample 

they used the KMO measure as proposed by 

Kaiser and Rice (1974). Then, they applied the 

MSA measure to validate the sample and then 

applied the PCA measure to extract relevant 

information. A number of factors have an 

impact on businesses, including applied 

graphics and an Eigenvalue with EV>1 

according to Thompson and Daniel (1996, 

200). 

After identifying the strengths and 

challenges, they focused on identifying the 

type of businesses applying BI. For this, they 

used a cluster analysis, namely the k-means 

algorithm and the proximity measure ED. The 

numbers of clusters were defined by the use of 

a FC measure (Fusion Coefficient) (Toms et al. 

2001). 

 To collect the necessary data, the 

researchers assessed 4960 Saxon firms, where 

the operators responded to an on-line 

questionnaire via e-mail, which covered a 

wide range of issues focusing on BI. The 

questionnaire was validated in two ways 

(Fowler 2001): it was originally created and 

written by experts in the field of information 

technology and then evaluated by conducting 

a preliminary test. In this way, they managed 

to ensure that respondent companies fully 

understood the terms and the importance of 

the questions in the questionnaire.  

In total they collected 452 questionnaire 

responses. Of these 452 companies, 214 

already had a BI tool. In these companies, 

they applied the technique of cluster analysis, 

to find the kind of companies that implement 

BI.  

From the research conducted, they 

managed to identify three main beneficial 

factors including improvements to data 

support. In this factor the main benefits are 

reduced effort of data analysis and reporting, 

reports are available faster and with better 

quality, easy access to information and 

flexible reactions to new information. The 

second beneficial factor was improvements to 

the decision process where the main benefits 

are that business decisions are being eased by 

more precise and current data analyses, risks 

and chances are supported in a higher level 

and the company’s  results are improved. The 

last beneficial factor is savings and it’s 

characterized by savings on personnel in 

different departments that can be achieved, 

competitive advantages can be achieved and 

cost savings in IT that can be achieved. 

 On the other hand, they were able to 

identify the main challenging factors. These 

were the challenges depending on usage. The 

main challenges are that the handling of the 

solution is too complicated and reports are to 

complex, data is poorly structured, 

capabilities do not cover business needs and 

BI staff are not qualified enough. 



 74 

 Challenges related to data such as 

software errors, inadequate security function, 

range of BI tools and functions don’t match 

with the business needs.  

The last challenging factor is the interface 

challenges. In this factor the main challenges 

are limited data export and also that the data 

are not usually enough 

  Finally through the cluster analysis, they 

were able to identify four categories of 

companies using BI (Rapidly growing B2C, 

lightly regulated companies with a focus on 

collaboration, service-oriented B2B 

companies, and high-regulated product-

oriented companies). 

The research carried out by Scholz et al. 

(2010) showed that companies and 

organizations that do not have a BI tool 

should not only focus on the positive effects 

that could generate from its use. They should 

study and all those challenges and constraints 

that may arise, e.g. software errors, reduced 

resources, and unnecessary costs. Through 

cluster analysis, they concluded that product-

oriented companies have better prospects in 

the application of BI. Also, through cluster 

analysis BI providers can identify the real 

needs of SMEs. 

In the second examined paper, Nenzhelele 

and Pellissier (2014) identify which business 

areas mainly applied business or competitive 

intelligence and whether they understand the 

concept. According to Bernstein (2009) 

competitive or business intelligence is formed 

by processing the data, which produce 

information, processed information which 

produces knowledge and processed knowledge 

which leads to intelligence.  

 The data collection was done by using a 

questionnaire sent to a hundred SMEs in the 

greater region of South Africa. Their original 

purpose was to discover whether SMEs are 

aware of BI and then to identify the main 

challenges they face. Also, they tried to find 

the sections where companies apply BI. From 

the research, the researchers concluded that 

although companies understood the 

importance of BI, they did not apply an 

equivalent tool. Businesses using a BI tool 

asked about the main challenges and 

discovered that three restrictions are common 

to all businesses. The lack of time working 

with the system shows that small businesses 

do not have the needed time to manage a 

competitive intelligence system, the lack of 

human resources and economic factors were 

the main problems they face. The application 

area is not located in a particular part, but 

somewhere independently. This is because 

SMEs have no formal organizational 

structure, but one very important role is the 

application of competitive intelligence in 

market research and marketing department. 

Apart from the various challenges and 

benefits identified, SMEs are trying to be 

more competitive to be able to achieve higher 

profits and more sales. In this case, it is stated 

that SMEs choose to spend more money and 

establish BI software in market research and 

marketing departments. 

5. IMPLEMENTATION METHODS 

FOR BI SYSTEMS IN SMES 

Frion and Yzquierdo-Hombrecher (2009) 

present a new competitive intelligence model 

for the management of large amounts of data 

and information entering business. Initially 

they conducted a literature reference which 

focused mainly on the concept of BI. The 

second method was based on their long 

experience in competitive intelligence systems 

and their application mainly in small 

businesses. Also, through their experience, 

they managed to develop and present a new 

information management model: the Acrie 

Model. 

From the literature, research is found that 

studies and research are carried out based on 

large companies. For this reason, the authors 

noticed that there are many different ways 

and methods to apply competitive intelligence. 

The literature survey was completed with the 

presentation of the new method and the new 

competitive intelligence information 

management model was developed. The model 

was called the Method Acrie.  

The basic principles of this model are less 

data, more inductive reasoning tests and 

analysis and less information, more curiosity 

about the problem, focuses on human behavior 

and on information approach through 

questions.  

The method takes place in three steps. 

The first step is a formal command 

formulation and an informal discussion to 

reformulate the first vague intent. The second 

step is a question plan, which consists of three 

levels and is formed by ten questions. This is 

to help the manager reach his expectations in 

a specific field. The third step consists of ten 

seeking plans, one for each question.  

It takes a few weeks to prepare a small 

company for the Acrie Method. When the 

preparation process is achieved, experts 



 75 

implement the proposed model in the 

company. According to Frion and Yzquierdo-

Hombrecher (2009), a small company is doing 

BI when the company is running an outgoing 

coordination prior to the five mail skills of BI 

activity: questioning, information seeking, 

information treating, distribution and 

protection of information.  

The large amount of data and information 

entering business is not always a good 

phenomenon. This is why the authors 

developed a new information management 

model. Through this model, the Acrie Model, 

the leader creates plans and plans with the 

rest of the team to help to reach a better 

result for the company's interest. This method 

is used by small and large companies, with 

various tools to suit the needs of each 

company individually and to ensure the 

continuous coordination of the five main skills 

of competitive (business) intelligence. The 

Acrie Method is a proposed model of 

information management that can manage 

data and also focus on people who are involved 

in this process. 

6. CLOUD COMPUTING AND BI  

In the present section two papers, which aim 

to present BI in cloud computing platforms 

are discussed. 

Agostino et al. (2013) identified the key 

success factors in their study for the adoption 

of cloud BI for SMEs and their characteristics 

based on the needs of BI users and suppliers. 

Past approaches based on Scholz et al. (2010) 

and Yeoh and Koronios (2010) have discovered 

three categories of factors. The first category 

is distinct from the organization, the second 

by processes and third by technology. 

According to Rockart (2009), the critical 

success factors represent a number of areas 

where satisfactory results will ensure a 

competitive position for the individual, the 

enterprise or a company’s section. Little has 

been said about the association of SMEs with 

BI software in a cloud level and therefore 

there is no framework to analyse their 

connection. The categories of factors to assess 

the SaaS software level cloud according to 

Godse and Mulik (2009) are functionality, 

system architecture, use, reputation, costs and 

risk. 

Researchers’ methodology consists of two 

stages. The first stage was characterized as 

qualitative. Researchers interviewed four 

experts (BI suppliers and BI users) in cloud BI 

software. At this level, they tried to identify 

the weaknesses and the improvements 

through interviews given by BI users and 

suppliers. Critical success factors are divided 

into six categories: performance-functionality, 

integration, adaptability, reliability, support 

and cost of ownership. 

The second stage was considered to be a 

quantitative stage. At this level, researchers 

tried to rank the key success factors for the 

adoption of a BI system. For this, an electronic 

questionnaire was created, which aimed to 

rank the importance of success factors. The 

use of cloud BI for SMEs was always a 

challenge for researchers, as the number of 

enterprises applying such a system was 

limited. 

In this stage, scholars gathered 

information from 36 companies through a 

questionnaire on the issue of BI. The 

questionnaire was created by Bryman and 

Bell (2011).  

The findings of the first phase were that 

the main factors for the adoption of BI 

systems was that they must have reduced 

costs, installation time and implementation 

and a quick response to user requests. 

The results of the second stage showed 

that the main factors to be taken by an 

enterprise BI are the functionality of the 

system, continuous data access, rapid 

response to user requirements, a large 

amount of data management and 

implementation costs. Both stages have shown 

that users are looking for easy tools to use as 

they have the necessary expertise. The 

economic factor plays a very important role 

because SMEs have very limited resources. 

Cloud software is an economic solution, which 

outlines additional requirements adopted by 

SMEs. 

Both stages have shown that users are 

looking for easy tools to use as they have the 

necessary expertise. The economic factor plays 

a very important role because SMEs have very 

limited resources. The cloud is an economic 

solution, which outlines additional 

requirements adopted by SMEs. 

Sheshasaayee and Swetha (2015) present 

the challenges of BI software combined with 

cloud computing. The combination BI with 

cloud software has many important 

advantages.  

The most important advantages are the 

speed of construction and speed of services, 

reduced costs of organization and payment of 

services depending on the use (Henning and 

Kemper 2010).  



 76 

Over the years, it has been observed that 

the application of BI at the cloud level is 

increasingly of interest in the field of 

information technology. The goal of cloud 

services is the acquisition and provision of 

resources to meet the maximum requirements 

and needs of users. 

According to scholars, cloud software 

consists of a three level structure: 

infrastructure, platform and software. 

Cloud software is easy to use and flexible, 

but has some problems. The most common 

problems according to the scholars are the 

different compatibility models, risk 

performance, and the variable price and cost 

ratios. 

According to Sheshasaayee and Swetha, 

BI refers to technologies that convert users’ 

available data resources and exportable 

information into business solutions. 

The cloud combined with BI is considered 

to be one of the most modern technologies in 

the field of information technology and this is 

the main reason it is facing some serious 

challenges. In studies, it is argued that the 

combination of BI and cloud software 

encountered some obstacles.  

The main challenges are the introduction 

of new technologies to the general public, the 

absence of idealized suppliers of specific 

software systems, the lack of control over the 

cloud services as all activities are done online 

and the movement of some compatible models 

that attempt to replace the actual abilities of 

cloud systems (Henning and Kemper 2010). 

This together leads to the conclusion that 

cloud software is aimed at companies with 

reduced financial resources, such as SMEs, 

but is easy to use and functional. The 

functions that cloud BI offers have been 

designed specifically for the needs of SMEs. 

7. CONCLUSIONS 

Through studies and surveys, many 

researchers have reached the conclusion that 

SMEs are the largest part of the market, and 

therefore of the economy, in most European 

countries. They are the driving force of the 

economy as they provide the majority of jobs 

in the private sector, so they compete with 

larger companies. The main tool in the 

development and support of competitiveness 

among SMEs is BI. The decision support 

systems that are based on computer 

applications offer tools so that businesses can 

process data to extract information and to 

make better business decisions 

Many researchers have researched the 

topic of BI in SMEs as well the benefits and 

challenges arising from the implementation of 

BI. 

Hidayanto et al. (2012) shaped and 

developed a framework so that businesses can 

know in advance their level of readiness to 

adopt BI systems, as to avoid unpleasant 

results. 

Tutunea and Rus (2012), undertook more 

commercial research. They focused on the 

available BI tools and their capabilities 

according to the type of business and their 

needs. 

Scholz et al. (2010) found that the main 

beneficial factors from the application of BI 

are the improvements in data support, 

improvements in decision support  and 

economic factors, while the main challenges 

they face are the errors and failures of 

software, the complexity of handling the 

failure of appropriate data and often 

inadequate data protection. 

Nenzhelele and Pellissier (2014) were able 

to identify in which sections companies 

applied BI and what challenges the 

enterprises face. The main application areas 

are market research and the independent 

sector, since businesses have no formal and 

specific organizational structure. The 

challenges identified in this study proved to be 

the lack of resources, lack of time to learn and 

economic restraints. Decision support systems 

don't only have benefits but they also have 

challenges and obstacles.  

Frion and Yzquierdo-Hombrecher (2009) 

created a new competitive intelligence model 

to help companies reach better decisions by 

managing a large volume of data. The 

proposed model (the Acrie Model) takes a lot 

of time to implement and, according to 

previous studies that have been conducted, 

SMEs don’t have the necessary time to deal 

intensively with the software learning 

process. 

Some researchers have focused on the new 

technology of cloud computing combined with 

BI.  Agostino et al. (2013) identified the key 

success factors from adopting BI in cloud 

software. Through questionnaires and 

interviews given by businesses using similar 

systems and BI suppliers, they concluded that 

continuous data access, ease of use, reduced 

costs and quick installation time, 

implementation, and responsiveness are the 

main features that lead users to purchase 

software. But even this technology faces some 



 77 

challenges. According to Sheshassayee and 

Swetha (2015), the main challenges of cloud 

software are the extra costs that may arise 

from their use, the limited checking services 

and the non-establishment within the general 

public. 

The main tool to create and support 

competitiveness is considered to be BI or 

otherwise competitive intelligence. Decision 

support systems based on computer 

applications offer the necessary  tools and the 

right infrastructure so businesses can process 

the data, extract relevant information and 

come to appropriate conclusions and therefore 

make better business decisions. Until a few 

years ago, the acquisition of BI systems by 

SMEs was considered difficult. Also, business 

owners did not consider it useful to obtain 

such a system. But over time, the evolution of 

technology and the continuous increase in 

competition, led to BI systems becoming a 

necessary tool for facing businesses’ 

competitors and helping SMEs to evolve. 

However, SMEs have different needs 

compared to larger companies. This is the 

main reason BI vendors design and create 

software that is affordable, convenient and 

effective so as to meet the needs of smaller 

companies and organizations. Such technology 

is called cloud computing, and it is easy to 

use, economical and provides many features. 

Some of the advantages of using decision 

support information systems are the 

conversion of data into useful information in 

order to draw useful conclusions, the 

understanding of key elements in a company 

(e.g., customers, suppliers, or resources) and 

the use of a common code of understanding 

between different departments, the company's 

profit growth and the creation of a competitive 

advantage. 

It is understood that BI is an essential 

part in the development of SMEs. Businesses 

will be able to make better business decisions 

and compete more effectively by choosing an 

appropriate system from a wide variety of 

programs based on the programs’ weaknesses 

and challenges. Of course, the results from the 

use of the systems are not initially visible, but 

are perceived gradually. Businesses initially 

make slow but steady movements to become 

familiar with system tools. Then they take 

into account the system outputs that lead to 

decisions. Finally, once the companies are 

familiar with the system, all decisions are 

made by it. Once one knows the challenges 

and obstacles that may arise they will be in a 

position where they are prepared to face any 

obstacle presented to reach a satisfactory 

result through the application of BI. 

8. REFERENCES 

Agostino, A., Solberg Søilen, K., & Gerritsen, B. 

(2013). Cloud solution in Business 

Intelligence for SMEs -vendor and costumer 

perspectives. Journal of Intelligence Studies 

in Business, 3, 5-28. 

Atre, S. (2003). The Top 10 Critical Challenges 

for Business Intelligence Success. Atre Group 

Inc.  

Bernstein, J. H. (2009). The Data-Information-

Knowledge-Wisdom Hierarchy and its 

Antithesis. In Proceedings of the 2009 North 

American Symposium on Knowledge 

Organization, 68-75.  

Bryman, A., & Bell, E. (2011). Business Research 

methods. Third Edition. Oxford University 

Press Inc. 

Cheng, C.H. (1997). Evaluating Naval Tactical 

Missile Systems by Fuzzy AHP Based on The 

Grade Value of Membership Function. 

European Journal of Operational Research, 

96(2), 343-350. DOI: 10.1016/s0377-

2217(96)00026-4 

Fowler, J.F. (2001). Survey Research Methods. 

Third Edition. Sage Publications. 

Frion, P., & Yzquierdo-Hombrecher, J. (2009). 

How to implement competitive intelligence in 

smes?. In Proceedings of the Third VISIO 

Conference, 162-173. 

Godse, M., & Mulik, S. (2009). An approach for 

selecting software-as-a-service (SaaS) 

product. In Cloud Computing, 2009. 

CLOUD'09. IEEE International Conference 

on, 155-158 

Henning B, & Kemper, H.G. (2010). Business 

intelligence in the cloud?.  In Proceedings of 

the PACIS 2010, 1528–1539.  

Hidayanto, A.N., Kristianto, R., & Shihab, M.R. 

(2012). Business intelligence implementation 

readiness: A framework development and its 

application to small medium enterprises 

(SMEs). In 3rd International Research 

Symposium in Service Management (IRSSM).  

Kaiser, H.F., & Rice, J. (1974). Little Jiffy, Mark 

IV. Educational and Psychological 

Measurement, 34(1), 111–117. 

Nenzhelele, T. (2014). Competitive Intelligence 

Location in Small and Medium-Sized 



 78 

Enterprises. Mediterranean Journal of Social 

Sciences, 5(23), 608-615. 

Nenzhelele, T., & Pellissier, R. (2014). 

Competitive Intelligence Implementation 

Challenges of Small and Medium-Sized 

Enterprises. Mediterranean Journal of Social 

Sciences, 5(16), 92-99. 

Scholz, P., Schieder, C., Kurze, C., Gluchowski, 

P., & Bohringer, M. (2010). Benefits and 

Challenges of Business Intelligence Adoption 

in Small and Medium-Sized Enterprises. In 

Proceedings of the 18th European Conference 

on Information Systems.  

Sheshasaayee, A., & Swetha, M.T. (2015). The 

Challenges of Business Intelligence in Cloud 

Computing. Indian Journal Of Science And 

Technology, 8(36).  

Taylor, B. W. (2005). Introduction to 

Management Science. 8th Ed. Prentice Hall. 

Thompson, B., & Daniel, L.G. (1996). Factor 

analytic evidence for the construct validity of 

scores: A historical overview and some 

guidelines. Educational and Psychological 

Measurement, 56(2), 197–208. 

Toms, M.L., Cummings-Hill, M.A., Curry, D.G., 

& Cone, S.M. (2001). Using cluster analysis 

for deriving menu structures for automotive 

mobile multimedia applications. In SAE 

Technical Paper Series 2001-01-0359, 

Warrendale, PA. 

Tutunea, M., & Rus, R. (2012). Business 

Intelligence Solutions for SME's. Procedia 

Economics and Finance, 3, 865-870.  

Williams, S., & Williams, N. (2004). Assessing 

BI Readiness: The Key to BI ROI. Business 

Intelligence Journal, 9(3), 15-23. 

Yeoh, W., & Koronios, A. (2010). Critical Success 

Factor for Business Intelligence Systems. 

Journal of Computer Information System, 

50(3), 23-32.