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BI and Big Data solutions in project management

100%
Pondel J., Pondel M.
Informatyka Ekonomiczna
|
2015
|
issue 4(38)
55-63
EN
The success of projects in enterprises and the success of an entire organization’s business largely depend on the possession and efficient use of the relevant information. In a broader context, success depends on having the adequate knowledge at the right time and place. Business processes generate large amount of data that are collected and processed in a way enabling transforming data into a measurable and useful value, which is information. Its efficient usage streamlines business processes, and allows to respond quickly to changes and proper decision-making. The aim of the paper is to present and define the project management challenges and ideas of Business Intelligence and Big Data systems. The types of analysis available in both platforms are also discussed. In the paper, the authors try to identify the areas of project management that can benefit from Business Intelligence and Big Data analysis.
2
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Metodyczne podejście do analizy i eksploracji danych marketingowych

100%
Korczak J., Pondel M.
Studia Ekonomiczne
|
2017
|
vol. 342
52-71
PL
W artykule zaproponowano metodykę realizacji projektu systemu wspomagania decyzji marketingowych z wykorzystaniem metod eksploracji danych i technologii Big Data. Inspiracją podejścia była metodyka eksploracji danych CRISP-DM, która oryginalnie nie była zorientowana na projekty Big Data. Z tego powodu metodykę tę zmodyfikowano pod kątem celu i wymagań funkcjonalnych oraz technologicznych projektowanego przez nas systemu. Główne prace badawcze w projekcie koncentrowały się na analizie i eksploracji dużych, heterogenicznych zbiorów danych o dużej zmienności. W artykule szczegółowo opisano etapy procesu realizacji projektu według rozszerzonej metodyki CRISP-DM, z uwzględnieniem specyfiki procesów analizy i eksploracji dużych baz danych marketingowych przetwarzanych w czasie rzeczywistym. W celu ilustracji podejścia podano też przykłady zadań w trakcie realizacji etapów projektu na konkretnych danych o klientach, transakcjach i produktach sklepu internetowego.
EN
The article proposes a methodology for development of a marketing Decision Support System using data mining methods and Big Data technologies. The main research findings focus on the analysis and exploration of very large, heterogeneous sets of highly volatile marketing data. The approach is inspired by the CRISP-DM methodology which is not oriented towards Big Data applications. The article describes in detail the stages of the project development according to the extended CRISP-DM methodology, taking into account the specificity of the analysis and exploration processes of large marketing databases processed in real time. In order to illustrate the approach, the examples based on real data about customers, transactions and products of the Internet store were discussed.
3
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Identification of advanced data analysis in marketing: A systematic literature review

81%
Bąska M., Dudycz H., Pondel M.
Journal of Economics and Management
|
2019
|
vol. 35
18-39
EN
Aim/purpose – Marketing is an important area of activity for the vast majority of enterprises. Many of them try using marketing data analysis. Both the literature and the practice of many enterprises describe the use of advanced data analysis. However, interpretations of this concept differ. The aim of this paper is to identify the interpretation of advanced data analysis in marketing, in support of decision-making processes applied in the retail trading sector. Design/methodology/approach – The study was conducted using a systematic literature review, suggested by B. Kitchenham (2004), extended by C. Wohlin & R. Prikladniki (2013). This method was modified and expanded through the division of the whole study into two phases. Each phase is intended to facilitate obtaining answers to different important research questions. The first phase constitutes an exploratory study, whose results allow the detailed analysis of the literature in the second phase of the study. Findings – The results of this study of the relevant literature indicate that scholarly publications do not use the phrase ‘advanced data analysis’, and its context is described with the term ‘data analysis’. Another term used broadly within the sphere of data analysis is ‘big data’. The concept of ‘data analysis’ in marketing is focused around the term ‘big data analytics’ and terms linked to the word ‘customer’, such as ‘customer-centric’, ‘customer engagement’, ‘customer experience’, ‘customer targeting service’, and ‘customers classification’. The study of the literature undertaken indicates that marketing employs data analysis in such areas as customer needs identification and market segmentation. Research implications/limitations – The study of the literature review was carried out using selected four databases containing publications, i.e. Web of Science, IEEE, Springer and ACM for the period 2008 to 2018. The research described in the article can be continued in two ways. First, by analysing the literature presented in this paper on advanced data analysis in marketing using the method called snowball sampling. Secondly, the results obtained from the first stage of the study can be used to conduct the study with other databases. Originality/value/contribution – The main contribution of this work is the proposal of modifying the systematic literature review method, which was expanded through the introduction of two phases. This division of two stages is important for conducting studies of literature when there are no clear, established definitions for the concepts being employed. The result of the study is also a set of ordered terms and their meanings that clearly define advanced data analysis in marketing.
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