Database Systems Journal vol. IV, no. 4/2013                                                                                                      21 
                                      Data Mining Solutions for the Business Environment 
                                                                            
                                                                 Ruxandra PETRE 
                                             University of Economic Studies, Bucharest, Romania 
                                                      ruxandra_stefania.petre@yahoo.com 
                                                                            
                     Over the past years, data mining became a matter of considerable importance due to the 
                     large  amounts  of  data  available  in  the  applications  belonging  to  various  domains.  Data 
                     mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques, 
                     from  statistics,  machine  learning,  database  systems  or  artificial  intelligence,  in  order  to 
                     discover  relevant  patterns,  trends  and  relations  contained  within  the  data,  information 
                     impossible to observe using other techniques. 
                     The paper focuses on presenting the applications of data mining in the business environment. 
                     It  contains  a  general  overview  of  data  mining,  providing  a  definition  of  the  concept, 
                     enumerating six primary data mining techniques and mentioning the main fields for which 
                     data mining can be applied. The paper also presents the main business areas which can 
                     benefit from the use of data mining tools, along with their use cases: retail, banking and 
                     insurance. Also the main commercially available data mining tools and their key features are 
                     presented within the paper. 
                     Besides the analysis of data mining and the business areas that can successfully apply it, the 
                     paper  presents  the  main  features  of  a  data  mining  solution  that  can  be  applied  for  the 
                     business environment and the architecture, with its main components, for the solution, that 
                     would help improve customer experiences and decision-making. 
                     Keywords: Data mining, Business, Architecture, Data warehouse 
                      
                         Introduction                                         analysis  a  matter  of  significant  importance 
                         Nowadays,  companies  collect  huge                  and  necessity  today.  Data  mining  –  the 
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                     volumes  of  data  on  a  daily  basis.                  analysis  step  within  the  KDD  (Knowledge 
                     Analyzing this data and discovering the                  Discovery  in  Databases)  process  –  uses  a 
                     meaningful  information  contained  by  it               diversity of advanced data analysis methods 
                     became an essential need for businesses.                 to  explore  the  data  and  discover  useful 
                     As  the  business  environment  develops                 patterns and trends. 
                     and changes constantly, facing every day                 Data  mining  consists  of  applying  data 
                     new  challenges,  the  companies  try  to                analysis  and  discovery  algorithms  that, 
                     strengthen  their  market  position  and                 under  acceptable  computational  efficiency 
                     achieve  competitive  advantage  by  using               limitations,       produce        a      particular 
                     new and  innovative  solutions,  like  data              enumeration of patterns (or models) over the 
                     mining.                                                  data. [1] 
                     Data     mining       solutions     implement            With the imminent growth of the amounts of 
                     advanced  data  analysis  techniques  used               data in every application, using data mining 
                     by companies for discovering unexpected                  methods for automatically identifying valid 
                     patterns extracted from vast amounts of                  and meaningful patterns in order to produce 
                     data,    patterns     that    offer    relevant          useful information and knowledge became a 
                     knowledge        for     predicting       future         requirement  for  various  fields  including 
                     outcomes.                                                business,     education      or    science      and 
                                                                              engineering,  fields  for  which  data  mining 
                     2. General overview of data mining                       can fulfill the following purposes: 
                     The  availability  and  affluence  of  data                   Business – data mining can be applied 
                     belonging to various domains make data                         in  retail,  banking  or  insurances,  for 
                                                                                    activities  like  customer  segmentation 
                          
            22                                              Data Mining Solutions for the Business Environment
                                                                                                       
                 and    retention,  market    basket       from the data mart, data warehouse and, in 
                 analysis or fraud detection;              particular  cases,  even  from  operational 
                Education  –  data  mining  can  be       databases. [2] 
                 applied   for  grouping   students,       The  data  mining  methods,  used  for 
                 predicting  student   performance,        extracting hidden patterns from the data, are 
                 planning and scheduling courses or        classified into the following two categories: 
                 understanding student behavior;           description methods and prediction methods. 
                Science  and  engineering  –  data        Description  methods  are  oriented  to  data 
                 mining can be used for domains like       interpretation,   which     focuses     on 
                 bioinformatics,         astronomy,        understanding (by visualization for example) 
                 medicine,     genetics,   electrical      the  way  the  underlying  data  relates  to  its 
                 power,    telecommunications     or       parts.  Prediction-oriented  methods  aim  to 
                 climate data.                             automatically  build  a  behavioral  model, 
            Data mining can be defined as a process        which obtains new and unseen samples and 
            of  exploring  and  analysis  for  large       is  able  to  predict  values  of  one  or  more 
            amounts of data with a specific target on      variables related to the sample. [3] 
            discovering    significantly   important       Data mining analyzes the data by applying a 
            patterns  and  rules.  Data  mining  helps     wide  variety  of  techniques,  developed  for 
            finding    knowledge      from      raw,       the  efficient  handling  of  large  volumes  of 
            unprocessed  data.  Using  data  mining        data.  The  six  primary  data  mining 
            techniques  allows  extracting  knowledge      techniques are presented below in figure 1: 
                                                          
                                           Fig. 1 Data mining techniques               
                                                          
            The  main  data  mining  techniques  are            prediction variable; 
            organized  into  the  following  categories:       Clustering:  is  a  common  descriptive 
            [1]                                                 task  where  one  seeks  to  identify  a 
                Classification:  consists   of    a            finite  set  of  categories  or  clusters  to 
                 function  that  maps  (classifies)  a          describe the data; 
                 data  item  into  one  of  several            Association rule learning (Dependency 
                 predefined classes;                            modeling): consists of finding a model 
                Regression: involves a function that           that describes significant dependencies 
                 maps a data item  to  a  real-valued           between variables;  
                
                     Database Systems Journal vol. IV, no. 4/2013                                                                                                      23 
                          Anomaly  detection  (Change  and                   applications  for  data  mining,  which  have 
                           deviation  detection):  focuses  on                improved many domains of human life. 
                           discovering  the  most  significant                 
                           changes in the data from previously                3. Data mining applications for business 
                           measured or normative values;                      Data mining is defined as a business process 
                          Summarization:  involves  methods                  for  exploring  large  amounts  of  data  to 
                           for  finding  a  compact  description              discover meaningful patterns and rules. [4] 
                           for a subset of data.                              Companies can apply data mining in order 
                     Data mining has evolved in the past two                  to   improve  their  business  and  gain 
                     decades,      becoming       a    fundamental            advantages over the competitors. 
                     discovery  process.  It  has  incorporated               The  most  important  business  areas  that 
                     techniques  from  many  other  fields,                   successfully apply data mining, presented in 
                     including statistics, machine learning and               Fig. 2 below, are: 
                     database systems. 
                     The diversity of data and the multitude of 
                     data  mining  techniques  provide  various 
                                                                            
                                                                                                          
                                             Fig. 2 Business areas that successfully apply data mining 
                                                                            
                     1.  Retail                                               Data     mining      techniques      have     many 
                     Retail  data  mining  can  help  identify                applications in the retail industry, including 
                     customer  buying  behaviors,  discover                   the following: 
                     customer  shopping  patterns  and  trends,                    Customer        segmentation:        identify 
                     improve the quality of customer service,                       customer  groups  and  associate  each 
                     achieve  better  customer  retention  and                      customer to the proper group; 
                     satisfaction, enhance goods consumption                       Establish customer shopping behavior: 
                     ratios,   design  more  effective  goods                       identify customer buying patterns and 
                     transportation  and  distribution  policies,                   determine what products the customer 
                     and reduce the cost of business. [5]                           is likely to buy next; 
                                                                                   Customer retention: identify customer 
                                                                                    shopping  patterns  and  adjust  the 
                          
             24                                                    Data Mining Solutions for the Business Environment
                                                                                                                  
                   product  portfolio,  the  pricing  and        3.  Insurance.  
                   the promotions offered;                       Data  mining  can  help  insurance  firms  in 
                  Analyze  sales  campaigns:  predict           business  practices  such  as:  acquiring  new 
                   the   effectiveness    of    a   sales        customers,  retaining  existing  customers, 
                   campaign  based  on  the  certain             performing  sophisticated  classification  or 
                   factors, like the discounts offered or        correlation  between  policy  designing  and 
                   the advertisements used.                      policy selection. [7] 
             Retail  industry  offers  a  wide  area  of         In  insurance  the  data  mining  techniques 
             applications  for  data  mining  due  to  the       have the following applications: 
             large  amounts  of  data  available  for                 Risk factor identification: analyze the 
             companies.                                                factors, like customer claims history or 
                                                                       behavior  patterns,  that  can  have  a 
             2.  Banking                                               stronger or weaker influence over the 
             There  are  various  areas  in  which  data               insured’s level of risk; 
             mining can be used in financial sectors                  Fraud detection:  establish  patterns  of 
             like    customer      segmentation       and              fraud  and  analyze  the  factors  that 
             profitability,  credit  analysis,  predicting             indicate a high probability of fraud for 
             payment  default,  marketing,  fraudulent                 a claim; 
             transactions,     ranking      investments,              Customer segmentation and retention: 
             optimizing     stock     portfolios,    cash              establish customer groups and include 
             management and forecasting operations,                    each new customer to the appropriate 
             high risk loan applicants, most profitable                group  and  identify  discounts  and 
             Credit Card Customers and Cross Selling.                  packages that would increase customer 
             [6]                                                       loyalty. 
             The main examples of applications of the            Data    mining     techniques     have    many 
             data  mining  techniques  in  the  banking          applications  in  the  insurance  business  and 
             industry are the following:                         can  improve  it  by  analyzing  the  large 
                  Credit   scoring:    distinguish   the        amounts of data available for companies. 
                   factors,  like   customer  payment             
                   history,  that  can  have  a  higher  or      4. Data mining tools used in the business 
                   lower influence over loan payment;            environment 
                  Customer  segmentation:  establish            Data  mining  tools  commercially  available 
                   customer  groups  and  include  each          implement  various  data  mining  techniques 
                   new customer in the right group;              for  performing  advanced  data  analysis  on 
                  Customer       retention:     identify        large volumes of data. The main data mining 
                   customer  shopping  patterns  and             products, presented in Table 1 below, along 
                   adjust  the  product  portfolio,  the         with  their  key  features,  are:  IBM  SPSS 
                   pricing and the promotions offered;           Modeler,  developed  by  IBM,  the  data 
                  Predict     customer     profitability:       mining  tools  included  by  Microsoft  SQL 
                   identify  patterns  based  on  various        Server  Analysis  Services,  Oracle  Data 
                   factors,  like  products  used  by  a         Mining,  embedded  within  the  Oracle 
                   customer,  in  order  to  predict  the        database,  SAS  Enterprise  Miner,  produced 
                   profitability of the customer.                by  SAS,  and  STATISTICA  Data  Miner, 
             The information systems for the banking             developed by StatSoft. 
             industry    contain   large   amounts  of            
             operational  and  historical  data,  being  a 
             fitted application area for data mining.