Business intelligence and SMEs: Bridging the gap

Ekavi Papachristodoulou, Margarita Koutsaki, Efstathios Kirkos


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.


Business intelligence, competitive intelligence, SMEs

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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.


ryman, 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,

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


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

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,

(3), 23-32.


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