DOI: https://doi.org/10.32515/2663-1636.2024.12(45).264-273

Comparative Analysis of Approaches to Data Analytics in the Management of Retail Networks

Dmytro Zamurenko

About the Authors

Dmytro Zamurenko, Postgraduate (student of the third (educational and scientific) level of higher education), Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, ORCID: https://orcid.org/0009-0008-4011-4674, e-mail: zamurenkodv@gmail.com

Abstract

The purpose of this study is to carry out a comparative analysis of different approaches to building a data analytics system in the management of retail networks, taking into account the current challenges of the retail market and the need to implement information and analytical systems to increase competitiveness. It is substantiated that the correct selection of organizational approaches to the functioning of data analytics has a significant impact on this process. The three main approaches to working with data analytics (centralized, decentralized and democratized) are analyzed, according to such criteria as: speed of decision-making, data consistency, flexibility, scalability, cost of implementation, need for specialized personnel. It is determined that the democratized approach, in which the IT department provides only the technical support, and all functions of data analysis and interpretation are assigned to business users, is characterized by the highest speed of decision-making, high scalability and low implementation cost, and contributes to the empowerment of users and the introduction of innovations. At the same time, it requires staff training and poses risks of incorrect data interpretation and data inconsistency in case of uncontrolled implementation of such technology, which requires the development and use of catalogs and data glossaries. Based on the analysis of the factors that can lead to an uncontrolled democratized approach, the necessity and ways of implementing the functionality of data catalogs and the functionality of data glossaries are substantiated. Prospects for further research in this area are the development of KPIs, methods of their calculation, as well as methods of analysis for key objects of retail networks.

Keywords

data analytics, Data Driven approach, retail trade, business intelligence, digital transformation, Self Service BI, Business Intelligence

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References

1. Hrynkevych, O.S., Matkovskyi, S.O., & Sydorova, A.V, et al. (2022). Economic analytics in business: textbook. Lviv: Ivan Franko National University of Lviv. https://econom.lnu.edu.ua/wp-content/uploads/2016/04/Navchalnyy-posibnyk_2022.pdf [in Ukrainian].

2. Yelisieieva, O.K., & Belozertsev, V.S. (2024). Data analytics and prospects for managing the digital data space. Trade and Market of Ukraine, 1(55), 7–14 [in Ukrainian]. https://doi.org/10.33274/2079-4762-2024-55-1-7-14

3. Yermakova, Y., Symonenko, K. (2024). Retail is on the offensive: the turnover of retail chains in Ukraine exceeded 1.5 trillion UAH (infographics). RAU. https://rau.ua/novyni/oborot-torgovelnih-merezh-rau/ [in Ukrainian].

4. Liakh, S.M. (2024). Analysis of current digitalization trends in Ukraine’s retail trade. Innovative Economics, (4) [in Ukrainian]. https://doi.org/10.37332/2309-1533.2024.4.7

5. Naumenko, M. (2024). Analysis and analytics of big data in marketing and trade of a competitive enterprise. Grail of Science, 40, 117–128 [in Ukrainian]. https://doi.org/10.36074/grail-of-science.07.06.2024.013

6. Ovander, N., Katunina, O., & Didur, H. (2024). Application of big data and analytics for business process optimization and cost reduction. Via

7. Satyr, L.M., Kepko, V.M., Stadnik, L.I., & Nepochatenko, A.V. (2020). Business analytical work in commercial activity: justification of economic decisions in retail trade. Investments: Practice and Experience, (15–16), 17–21 [in Ukrainian]. https://doi.org/10.32702/2306-6814.2020.15-16.17

8. Analytics as a Source of Business Innovation. MIT Sloan Management Review, 2017. https://sloanreview.mit.edu/wp-content/uploads/2017/02/621756dfdd-1.pdf?cid=1 [in English].

9. Davenport, T.H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98–107, 134. https://www.researchgate.net/publication/7327312_Competing_on_Analytics [in English].

10. Davenport, T.H., & Harris, J.G. (2007). Competing on analytics: The new science of winning. Harvard Business Press. https://books.google.com.ua/books/about/Competing_on_Analytics.html?id=n7Gp7 Q84hcsC&redir_esc=y [in English].

11. Gartner Survey Reveals 87% of Organizations Have Low BI and Analytics Maturity. Gartner Newsroom, 2021. https://www.gartner.com/en/newsroom/press-releases/2018-12-06-gartner-data-shows-87-percent-of-organizations-have-low-bi-and-analytics-maturity [in English].

12. Global Consumer Insights Survey 2021. PwC, 2021. https://www.pwc.com/gx/en/consumer-markets/consumer-insights-survey/2021/gcis-june-2021.pdf [in English].

13. Global Powers of Retailing 2022. Deloitte, 2022. https://www2.deloitte.com/content/dam/Deloitte/at/Documents/consumer-business/at-global-powers-retailing-2022.pdf [in English].

14. McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68. https://hbr.org/2012/10/big-data-the-management-revolution [in English].

15. The age of analytics: Competing in a data-driven world. McKinsey Global Institute, 2016. https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf [in English].

16. The Forrester Wave™: Big Data Hadoop Distributions, Q1 2016. Forrester, 2016. https://www.datascienceassn.org/sites/default/files/Hadoop%20Vendor%20Evaluations%202016.pdf [in English].

17. Top 10 Transformative Analytics Scenarios in Retail. Qlik, 2023. https://www.qlik.com/us/resource-library/top-10-transformative-analytics-scenarios-in-manufacturing [in English].

18. Vorobets, Ye., Khmeliuk, A., Moshkovska, O., Valiyev, V.I., & Moskalenko, O. (2024). The role of data analytics in making management decisions by the logistics intermediaries. Financial and Credit Activity: Problems of Theory and Practice, 4(57), 185–196 [in English]. https://doi.org/10.55643/fcaptp.4.57.2024.4422

Citations

  1. Економічна аналітика в бізнесі : навч. посібник / [О.С. Гринькевич, С.О. Матковський, А.В. Сидорова та ін.] ; за ред. О.С. Гринькевич, С.О. Матковського, А.В. Сидорової, Н.С. Струк. Львів : ЛНУ ім. Івана Франка, 2022. 480 с. https://econom.lnu.edu.ua/wp-content/uploads/2016/04/Navchalnyy-posibnyk_2022.pdf (дата звернення: 1.12.2024).
  2. Єлісєєва О. К., Бєлозерцев В. С. Дата-аналітика та перспективи управління цифровим простором даних. Торгівля і ринок України. 2024. 1(55). DOI: https://doi.org/10.33274/2079-4762-2024-55-1-7-14
  3. Єрмакова Я., Симоненко К. Ритейл іде у наступ: оборот торговельних мереж України перевищив 1,5 трлн грн (інфографіка). 2024. URL: https://rau.ua/novyni/oborot-torgovelnih-merezh-rau/ (дата звернення: 1.12.2024).
  4. Лях С.М. Аналіз сучасних трендів цифровізації в роздрібній торгівлі України. Інноваційна економіка. 2024. №4. DOI: https://doi.org/10.37332/2309-1533.2024.4.7
  5. Науменко М. Аналіз та аналітика великих даних в маркетингу та торгівлі конкурентного підприємства. Grail of Science. 2024. 40. C. 117–128. DOI: https://doi.org/10.36074/grail-of-science.07.06.2024.013
  6. Овандер Н., Катуніна О., Дідур Г. Застосування великих даних та аналітики для оптимізації бізнес-процесів і зниження витрат. Via economica. 2024. Випуск 4. С. 133-139. DOI: https://doi.org/10.32782/2786-8559/2024-4-18
  7. Сатир Л. М., Кепко В. М., Стаднік Л. І., Непочатенко А. В. Бізнес-аналітична робота в комерційній діяльності: обґрунтування господарських рішень щодо роздрібної торгівлі. Інвестиції: практика та досвід. 2020. № 15-16. С. 17–21. DOI: 10.32702/2306-6814.2020.15-16.17
  8. Analytics as a Source of Business Innovation. MIT Sloan Management Review, 2017. URL: https://sloanreview.mit.edu/wp-content/uploads/2017/02/621756dfdd-1.pdf?cid=1 (Last accessed: 20.11.2024).
  9. Davenport T. H. Competing on analytics. Harvard Business Review. February 2006. 84(1). P. 98-107, 134. URL: https://www.researchgate.net/publication/7327312_Competing_on_Analytics (Last accessed: 20.11.2024).
  10. Davenport T. H., Harris J. G. Competing on analytics: The new science of winning. Harvard Business Press, 2007. 218 p. URL: https://books.google.com.ua/books/about/Competing_on_Analytics.html? id=n7Gp7Q84hcsC&redir_esc=y (Last accessed: 20.11.2024).
  11. Gartner Survey Reveals 87% of Organizations Have Low BI and Analytics Maturity. Gartner Newsroom, 2021. URL: https://www.gartner.com/en/newsroom/press-releases/2018-12-06-gartner-data-shows-87-percent-of-organizations-have-low-bi-and-analytics-maturity (Last accessed: 20.11.2024).
  12. Global Consumer Insights Survey 2021. PwC, 2021. URL: https://www.pwc.com/gx/en/consumer-markets/consumer-insights-survey/2021/gcis-june-2021.pdf (Last accessed: 20.11.2024).
  13. Global Powers of Retailing 2022. Deloitte, 2022. URL: https://www2.deloitte.com/content/dam/Deloitte/at/Documents/consumer-business/at-global-powers-retailing-2022.pdf (Last accessed: 20.11.2024).
  14. McAfee A., Brynjolfsson E. Big data: The management revolution. Harvard Business Review. 2012. 90(10). P. 60-68. URL: https://hbr.org/2012/10/big-data-the-management-revolution або https://ailab-ua.github.io/courses/MIS510/big_data_-_the_management_revolution_0.pdf (Last accessed: 20.11.2024).
  15. The age of analytics: Competing in a data-driven world. McKinsey Global Institute, 2016. URL: https://www.mckinsey.com/~/media/mckinsey/industries/public%20and%20social%20sector/our%20insights/the%20age%20of%20analytics%20competing%20in%20a%20data%20driven%20world/mgi-the-age-of-analytics-full-report.pdf (Last accessed: 20.11.2024).
  16. The Forrester Wave™: Big Data Hadoop Distributions, Q1 2016. Forrester, 2016. URL: https://www.datascienceassn.org/sites/default/files/Hadoop%20Vendor%20Evaluations%202016.pdf (Last accessed: 20.11.2024).
  17. Top 10 Transformative Analytics Scenarios in Retail. Qlik, 2023. URL: https://www.qlik.com/us/resource-library/top-10-transformative-analytics-scenarios-in-manufacturing (Last accessed: 20.11.2024).
  18. Vorobets Ye., Khmeliuk A., Moshkovska O. Valiyev V. I., Moskalenko O. The Role of Data Analytics in Making Management Decisions by the Logistics Intermediaries. Financial and Credit Activity Problems of Theory and Practice. 2024. 4(57). P. 185–196. DOI: https://doi.org/10.55643/fcaptp.4.57.2024.4422 (Last accessed: 20.11.2024).
Copyright (c) 2024 Dmytro Zamurenko