The paper studies possibilities of intelligent data analysis application for discovering knowledge hidden in small and medium-sized enterprisesâ€™ (SMEs) data, on the territory of the province of Vojvodina. The knowledge revealed by intelligent analysis, and not accessible by any other means, could be the valuable starting point for working out of proactive and preventive actions for the development of the SMEs sector.Intelligent data analysis, CRISP-DM, clustering, small and medium enterprises., Research and Development/Tech Change/Emerging Technologies, C8, L2,

Bosnjak, Sasa

Bosnjak, Zita

Grljevic, Olivera

English

Research Papers in Economics

Perspectives of Innovations, Economics & Business, Volume 3, 2009 
www.pieb.cz 
 
 
International Cross-Industry Journal  
57 
INTELLIGENT DATA ANALYSIS - SUPPORT FOR 
DEVELOPMENT OF SMES SECTOR * 
 
  OLIVERA GRLJEVIC,  
SASA BOSNJAK, PH.D., 
ZITA BOSNJAK, PH.D.  
 
Faculty of Economics Subotica 
University of Novi Sad, Serbia 
JEL Classifications: C8, L2 
Key words:  Intelligent data analysis, CRISP-DM, clustering, small and medium enterprises.  
Abstract:  The paper studies possibilities of intelligent data analysis application for discovering knowledge hidden in small and 
medium-sized enterprises’ (SMEs) data, on the territory of the province of Vojvodina. The knowledge revealed by intelligent 
analysis,  and  not  accessible  by  any  other  means,  could  be  the  valuable  starting  point  for  working  out  of  proactive  and 
preventive actions for the development of the SMEs sector. 
ISSN: 1804-0527 (online)  1804-0519 (print)    PP. 57-58 
 
Introduction 
Methods
* and techniques of intelligent data analysis, also 
known as data mining algorithms, have become an important 
research area, because their joint application with traditional 
data  analysis  can  reveal  knowledge  -  hidden  relations, 
behavioral patterns, entity profiles, and similar regularities in 
data stored in large databases or warehouses. In the paper we 
described our efforts to take advantage of the intelligent data 
analysis and discover some knowledge hidden in small and 
medium  sized  enterprises’  (SMEs)  data,  with  the  aim  to 
support the development of SMEs sector. The data collection 
consisted of 2365 records, each containing data on one SME 
in Vojvodina province. The data were collected by means of 
distributed  questionnaires,  provided  by  four  Regional 
Agencies for the Development of Small and Medium Sized 
Enterprises and Entrepreneurship.  
CRISP-DM as a framework                                                  
for knowledge discovery  
Knowledge  discovery  is  a  very  intricate,  uncertain  and 
time consuming process. Therefore, of utmost importance is 
to follow the existing methodological guidance, among which 
the  CRISP-DM  (the  CRoss-Industry  Standard  Process  for 
Data  Mining)  is the  most  favored  one.  It  comprises  of the 
following  tasks:  (a)  business  understanding;  (b)  data 
understanding;  (c)  data  preparation;  (d)  modeling;  (e) 
evaluation; and (f) deployment. Generally, these tasks follow 
each other as subsequent phases, but within this main stream, 
many iterative cycles can be observed. In our research we also 
used this methodology.  
                                                           
*  This  paper  is  the  result  of  a  research  on  the  project  titled: 
“Comparative Advantages of Intelligent Data Analysis Methods in 
Strengthening the Sector of Small and Medium Sized Enterprises”, 
No. 114-451-01092/2008-01, funded by the Ministry of Sciences and 
Technology  Development  of  Province  of  Vojvodina,  Republic  of 
Serbia.  
 
Crucial for the success of intelligent data analysis was the 
second phase of CRISP-DM methodology, comprising of data 
formatting,  description,  exploration,  and  data  quality 
verification, because the data source was partially erroneous 
and of poor quality. The source data were originally stored in 
MS  Access  format,  but  they  were  further  transformed  into 
appropriate input to intelligent data analysis tools. During the 
exploration  of  data  on  SMEs, some interesting  things  were 
observed, such as the fact that there were almost three times 
less  female  than  male  directors  in  SMEs  in  Vojvodina 
(24.06% vs. 64.27%). Such insights into the sector of SMEs 
had to be treated with precautions, due to the large number of 
missing data (for e.g.  12% of values for the director’s gender 
were missing). In the phase of data cleansing, missing values 
were  either  replaced  by  some  neutral  value,  or  the  records 
with  missing  data  were  excluded  from  further  analysis. 
Visualization techniques were also very useful in “outliers” 
detection,  and  for  verifying  or  rejecting  initially  stated 
hypothesis  on  data  dependences.  For  e.g.  the  hypothesized 
correlation  between  equipment  maturity  and  lack  of 
investments into new technologies had to be rejected (r = -
0.20295). This was in contrast with our expectation that SMEs 
that had outdated equipment invested less in the last 5 years 
than  SMEs  having  contemporary  equipment.  In  subsequent 
steps  of  the  Knowledge  Discovery  in  Databases  (KDD) 
process, the unique set of data was divided into subsets and 
different data mining methods and techniques were used for 
their analysis, as described in the sequel. 
The challenges and limitations                                                   
of data modeling  
The modeling phase of CRISP-DM methodology includes 
the application of different machine learning techniques, with 
wide  scale  of  tunable  parameters,  each.  The  stress  in  our 
investigations  was  on  devising  discriminators  among 
successful  and  less  successful  enterprises,  to  describe  the 
general  profile  of  businesses  that  were  likely  to  fail  in 
achieving  their  goals,  to  select  the  attributes  of  high 
predictability in forecasting future business gains/losses, etc. 
Within our research, we have created data models by fuzzy c-
means algorithm and Kohonen neural networks for clustering Perspectives of Innovations, Economics & Business, Volume 3, 2009 
www.pieb.cz 
 
 
International Cross-Industry Journal  
58 
tasks, and Multilayer Perceptron (MLP) neural networks for 
classification and forecasting tasks. As data mining is known 
to  be  a  time-consuming,  laborious  endeavor,  without 
guaranties that interesting and potentially useful patterns will 
be revealed, we were prepared to the creation of large number 
of data models and trial-and-error approach to data analysis. 
Some models, such as the clustering model which divided the 
SMEs according to the main problems they  were facing in 
everyday  business  operations  (lack  of  available  funds, 
complex  administrative  and  legislative  regulations, 
disharmony with standards, insufficient market information, 
etc.)  resulted  in  interesting  findings.  By  this  model, 
enterprises were clustered into 4 groups. One of the clusters 
comprised of SMEs that had not recognized any of the above 
listed  threats  as  a  serious  one  to  their  business  operations, 
while all other enterprises had recognized the lack of funds as 
a serious threat. Surprising was the finding that membership 
of SMEs in domestic/foreign business associations, as well as 
their  involvement  in  industry  clusters  had  no  influence  on 
overcoming the problem of insufficient funds, despite the fact 
that  both  business  associations  and  industry  clusters  are 
established primarily for this reason.  
Some  other  models,  like  the  MLP  classification  model, 
developed with the aim to classify SMEs based on seven input 
attributes  related  to  difficulties  in  everyday  business 
operations,  into  predefined  classes:  SMEs  with  outdated 
equipment, SMEs with moderately outdated equipment, and 
SMEs with “new generation” equipment, could not be built. 
After number of trials with different MLP configurations and 
various  learning  methods  offered  in  applied  tools,  we 
concluded that the defined output variable was not dependant 
on the selected input attributes in any way, i.e. there was no 
hidden relation we were hopeful to find. 
Some  data  models  were  impossible  to  build because  of 
some manifestations of poor data quality that became obvious 
only during the modeling phase of CRISP-DM methodology. 
The clustering model we tried to build for investigating the 
similarities/dissimilarities  between  SMEs  concerning  legal 
and administrative limitations to the development of business 
operations was one of these. 
Knowledge discovery improvement by                              
utilization of diversified data analysis tools 
Data analysts have a great responsibility to carry out the 
interpretation of the results obtained by data mining, and to 
give a meaningful explanation of observed relations. That is 
why we selected a composite approach to data analysis that 
implies  an  application  of  diverse  tools,  methods  and 
techniques for data mining. Within the composite approach to 
data analysis process, we managed to take advantage of the 
utilization of DataEngine (DE), Intelligent Data Analyzer tool 
(iDA),  and  Waikato  Environment  for  Knowledge  Analysis 
(Weka) tool.  
The  combination  of  three  different  data  mining  tools 
proved to be superior to the  application of one single  data 
mining  tool.  For  e.g.,  the  clustering  model,  that  partitions 
SMEs according to business problems they were coping with, 
was firstly developed in DE tool. We used a relation of the 
partitioning  coefficient  and  the  classification  entropy  as  a 
validity measure, to determine the optimal number of clusters 
as 4. The iDA clustering technique partitioned the data into 
the same optimal number of clusters. We took advantage of 
this knowledge to set the initial number of clusters in Weka 
and conducted the clustering. The visualization of clustering 
results  was  different in  all three tools,  providing  additional 
information on defined clusters to data analysts. 
Conclusion 
Although knowledge discovery in data, and data mining as 
its integral part, are indispensable for data analysts, it is true 
only in case of good quality input data. In the described case 
of the analysis of SMEs data, it was an unpleasant truth that 
the goal and importance of the knowledge discovery endeavor 
were  not  recognized  or  fully  understood  by  SMEs 
representatives and that they were not motivated sufficiently 
to provide all the required data. Consequently, lots of source 
data  were  missing or  erroneous.  We  managed to  overcome 
this obstacle by utilization of multiply tools when clustering 
tasks were in question, and despite the limitations we found 
some  very  interesting  and  unexpected  relations  between 
particular subsets of attributes describing SMEs in Vojvodina. 
Agencies for the Development of Small and Medium Sized 
Enterprises  and  Entrepreneurship  could  use  the  discovered 
relationships in SMEs everyday business activities to structure 
programs to better suite the need of SMEs and to ensure in 
such  a  way  further  development  of  their  businesses  and  to 
decrease  the  trend  of  closing  such  enterprises,  which  was 
evident  in  the  previous  few  years.  Also,  the  revealed 
knowledge  could  be  used  to  create  more  successful 
employment policies and to maintain balance between supply 
and demand on the work force market.  
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Intelligent data analysis - support for development of SMEs sector

Intelligent data analysis - support for development of SMEs sector

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