Factors affecting the use of data mining in Mozambique

We present a study aimed at finding important factors that affect the acceptance and use of data mining in Mozambique. Input from potential users has been collected and analysed using a mix of qualitative and quantitative methods. The findings indicate that the level of adoption of data mining in Mozambique is primarily affected by poor quality of data, limited skills and human resources, limited support of stakeholders, organizational issues, limited financial resources and lack of adequate technology. These factors are similar to those identified in other studies.

[1]  Hyun Kyu Lee,et al.  ORGANIZATIONAL DATA MINING IN KOREA , 2007 .

[2]  Ying Liu,et al.  Decision analysis of data mining project based on Bayesian risk , 2009, Expert Syst. Appl..

[3]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[4]  K. Perreault,et al.  Research Design: Qualitative, Quantitative, and Mixed Methods Approaches , 2011 .

[5]  M. Aanestad,et al.  Analysing the quality of routine malaria data in Mozambique , 2004, Malaria Journal.

[6]  Jiawei Han,et al.  Data Mining: Concepts and Techniques, Second Edition , 2006, The Morgan Kaufmann series in data management systems.

[7]  Lars Asker,et al.  ICT for automated forecasting of electrical power consumption: A case study in Maputo , 2011, 2011 IST-Africa Conference Proceedings.

[8]  Ian H. Witten,et al.  Data mining - practical machine learning tools and techniques, Second Edition , 2005, The Morgan Kaufmann series in data management systems.

[9]  M. Hart.  Progress of organisational data mining in South Africa , 2006, South Afr. Comput. J..

[10]  Chuang-Chun Liu,et al.  An empirical investigation of factors influencing the adoption of data mining tools , 2012, Int. J. Inf. Manag..

[11]  Yun Chen,et al.  Data Mining and Critical Success Factors in Data Mining Projects , 2006, PROLAMAT.

[12]  Hamid R. Nemati,et al.  Key factors for achieving organizational data-mining success , 2003, Ind. Manag. Data Syst..

[13]  Jeffrey W. Seifert,et al.  Data Mining: An Overview , 2004 .

[14]  M. L. Hart,et al.  Issues affecting the adoption of data mining in South Africa , 2002, South Afr. Comput. J..

[15]  Andreas Hilbert.  Critical Success Factors for Data Mining Projects , 2005, Data Analysis and Decision Support.

[16]  B. J. Oates,et al.  Researching Information Systems and Computing , 2005 .

[17]  Jørn Braa,et al.  Assessing immunization data quality from routine reports in Mozambique , 2005, BMC public health.

[18]  Margaret H. Dunham,et al.  Data Mining: Introductory and Advanced Topics , 2002 .

[19]  Henrik Boström,et al.  Extracting Patterns from Socioeconomic Databases to Characterize Small Farmers with High and Low Corn Yields in Mozambique: a Data Mining Approach , 2012, ICDM.