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Assessing Organisational Maturity in Predictive Analytics of Telecommunications Companies in the Republic of Macedonia.
- Source :
- Economic Analysis (0013-3213); 2019, Vol. 52 Issue 1, p48-55, 8p
- Publication Year :
- 2019
-
Abstract
- Data analytics and predictive analytics are among major trends companies are facing worldwide. In a highly digitalised environment, it is not only to question the usage of data analytics but how analytically mature organisations are. The goal of this paper is to assess organisational maturity in predictive analytics of telecommunications companies in the country. In order to assess the level of organisational maturity in predictive analytics, we use Predictive Analytics Maturity Framework Assessment (PAMFA) (Capgemini, 2012), since it best describes maturity levels in the telecommunications sector. The method of analysis is based on interviewing managers with a questionnaire that guides respondents through all dimensions and levels proposed by the framework. According to the PAMFA five dimensions are analysed (Vision and strategy, Enablers, Competence, Deployment and Governance). For each dimension, four maturity levels are defined: Level 1: Impromptu, Level 2: Solo, Level 3: Ensemble and Level 4: Symphony (Capgemini, 2012). Survey results confirmed that analysed companies fully understand the benefits of predictive analytics as a valuable source of gaining competitive advantage from data. The overall level of predictive analytics maturity is set between levels 2 or 3 for almost all dimensions. This research is the first attempt to analyse organisational maturity in predictive analytics in the country. Its originality derives from the specific characteristics and development of the telecommunications sector. This sector is one of the most advanced service sectors in the country and hence represents a benchmark concerning digital transformation. Results of this survey provide useful information needed to design a roadmap for migrating towards higher maturity levels. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00133213
- Volume :
- 52
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Economic Analysis (0013-3213)
- Publication Type :
- Academic Journal
- Accession number :
- 137903246
- Full Text :
- https://doi.org/10.28934/ea.19.52.12.pp48-55