Improving the organization of cost analysis in a digital environment

Aim. The presented study aims to substantiate the need to use digital technologies in analytics to make effective management decisions and ensure cost reduction.

Tasks. The authors determine the place of a digital analytics platform in cost analysis; identify key factors in the implementation of the platform; describe the capabilities of the tools provided by the analytics platform and how they can be used in the cost management process.

Methods. This study uses general scientific methods; the results are summarized through tabular and graphical presentation; four groups of data analysis methods (descriptive and diagnostic analytics, predictive and prescriptive analytics) are identified; within each group, special methods of cost analysis (management analysis methods and advanced analytics methods) are indicated, which can be automated using an analytics platform.

Results. It is found that the introduction of an analytics platform transforms the structure of time costs associated with performing analytical work, freeing up working time for solving other management tasks. It is established that analytics platforms are flexible and can find highly specialized solutions due to a wide range of tools and methods implemented on the basis of the platform. The selection of tools and methods depends on the tasks set as part of the cost analysis. Acceptable options for using advanced analytics tools within the framework of cost management are formulated.

Conclusions. The analytical platform serves as a tool that improves the quality of data processing necessary for making effective management decisions. Comprehensive application of traditional cost analysis tools and advanced analytics facilitates the effective search for new opportunities for optimizing business processes, which is an urgent task in the context of uncertainty and limited resources. Considering the importance of the functions performed by the tools of the analytics platform and in connection with the withdrawal of foreign leaders in the field of business analytics from the Russian market due to sanctions, domestic vendors of analytical systems are faced with the challenge of further improving software products that should be on par with their foreign analogs in terms of functionality.

1. Main directions of development of the financial market of the Russian Federation for 2023 and the period of 2024 and 2025. Moscow: Bank of Russia; 2022. 85 p. URL: https://cbr.ru/Content/Document/File/143773/onfr_2023-2025.pdf (accessed on 29.01.2023). (In Russ.).

2. Zhukovskaya I.E. Modern approaches of application of digital analytical platforms in the activities of higher educational institutions. Deutsche international Zeitschrift für zeitgenössische Wissenschaft. 2021;(7):38-41. (In Russ.). DOI: 10.24412/2701-8369-2021-7-2-38-41

3. Mohanty S., Jagadeesh M., Srivatsa H. Big Data imperatives: Enterprise Big Data warehouse, BI implementations and analytics. New York, NY: Apress; 2013. 342 p.

4. Williams S. Business intelligence strategy and Big Data analytics: A general management perspective. Cambridge, MA: Morgan Kaufmann; 2016. 240 p.

5. Olszak C.M. Business intelligence and Big Data: Drivers of organizational success. Boca Raton, FL: CRC Press; 2020. 194 p.

6. Nazarov D.M., Ryzhkina D.A. Smart tools of business intelligence. Moscow: KnoRus; 2022. 242 p. (In Russ.).

7. Business intelligence as a tool for making management decisions. 1C: Pervyi Bit. Aug. 10, 2022. URL: https://www.1cbit.ru/company/news/458186/ (accessed on 29.01.2023). (In Russ.).

8. Smolenskaya O.V. The model of the accounting profession in the era of digital transformation: Key competencies. Finansovye rynki i banki = Financial Markets and Banks. 2022;(11):191-195. (In Russ.).

9. Shvetsov A.N. Improving business efficiency: The contribution of the latest digital technologies. Menedzhment i biznes-administrirovanie = Management and Business Administration. 2021;(4):69-81. (In Russ.). DOI: 10.33983/2075-1826-2021-4-69-81

10. Kizim A.V., Peresypkin A.M. Development of a training manual “Corporate business intelligence systems using artificial intelligence systems”. Sistemnyi analiz v nauke i obrazovanii = System Analysis in Science and Education. 2022;(2):11-16. (In Russ.).

11. Barilenko V.I., Chugumbaev R.R. The development of business analysis and the role of training business analysts in providing digital transformation of the Russian economy. Menedzhment i biznes-administrirovanie = Management and Business Administration. 2018;(2):146-155. (In Russ.).

12. Silakova L.V., Maharramov P.A., Semkina M.A. Automation of integrated business analysis for SME organizations based on MS POWER BI. Nauchnyi zhurnal NIU ITMO. Seriya: Ekonomika i ekologicheskii menedzhment = Scientific Journal NRU ITMO. Series: Economics and Environmental Management. 2019;(3):101-108. (In Russ.). DOI: 10.17586/2310-1172-2019-12-3-101-108

13. Business analytics vs business intelligence? Innovation Evangelism – Timo Elliott’s Blog. Mar. 09, 2011. URL: https://timoelliott.com/blog/2011/03/business-analytics-vs-business-intelligence.html (accessed on 29.01.2023).

14. Business intelligence (Russian market). TAdviser. Feb. 06, 2023. URL: https://www.tadviser.ru/index.php/Статья:Business_Intelligence_(рынок_России)?ysclid=ldwvuw87cq443163 (accessed on 29.01.2023). (In Russ.).