+7 925 966 4690, 9am6pm (GMT+3), Monday – Friday
ИД «Финансы и кредит»

JOURNALS

  

FOR AUTHORS

  

SUBSCRIBE

    
Economic Analysis: Theory and Practice
 

Monetization of big data technology: Information costs modeling

Vol. 19, Iss. 11, NOVEMBER 2020

Received: 8 October 2020

Received in revised form: 20 October 2020

Accepted: 30 October 2020

Available online: 27 November 2020

Subject Heading: ECONOMIC ADVANCEMENT

JEL Classification: B41, D23

Pages: 1990–2011

https://doi.org/10.24891/ea.19.11.1990

Karpychev V.Yu. National Research Lobachevsky State University of Nizhny Novgorod (UNN), Nizhny Novgorod, Russian Federation
kavlyr@yandex.ru

https://orcid.org/0000-0001-8527-2600

Shal'nova Yu.P. PAO Sberbank, Nizhny Novgorod, Russian Federation
julia.shalnova@gmail.com

ORCID id: not available

Subject. The article addresses information costs associated with the creation and use of big data technology.
Objectives. We focus on specification and analysis of information costs as a parameter that determines the possibility to monetize big data technology; identify the main sources, the nature and factors impacting these costs; develop approaches to assessing and reducing them.
Methods. The study employs methods of systems approach, classification, functional modeling, functional cost analysis.
Results. We propose a conceptual functional model of big data technology. Using the model, we explore the structure, content and nature of investment costs involved in creation and implementation of big data technology, as well as operating costs at the data preprocessing stage. The paper shows the transactional nature of information costs of big data technology, points out the need for cost minimization to monetize the technology, offers a methodology for assessing the information costs, which integrates modern methods for business process investigation.
Conclusions. Prospects for monetization and extensive use of big data technology in economic activities are determined by the possibility of reducing the information costs inherent in the technology. To overcome existing difficulties, special scientific studies in the field of technology and economics are needed.

Keywords: big data, monetization, functional modeling, transaction costs, functional cost analysis

References:

  1. García S., Ramírez-Gallego S., Luengo J. et al. Big Data Preprocessing: Methods and Prospects. Big Data Analytics, 2016, vol. 1, iss. 9. URL: Link
  2. Kantardzic M. Data Mining: Concepts, Models, Methods, and Algorithms. Hoboken, New Jersey, John Wiley & Sons, Inc., 2020, 661 p.
  3. Abou Zakaria Faroukhi, Imane El Alaoui et al. Big Data Monetization Throughout Big Data Value Chain: A Comprehensive Review. Journal of Big Data, 2020, vol. 7, iss. 3. URL: Link
  4. Petryashov D.V. [Information costs in the system of concepts of economic science]. RISK: Resursy, Informatsiya, Snabzhenie, Konkurentsiya = RISK: Resources, Information, Supply, Competition, 2014, no. 1, pp. 184–187. URL: Link (In Russ.)
  5. Coase R. Problema sotsial'nykh izderzhek. Firma, rynok i pravo [The Firm, the Market and the Law]. Moscow, Novoe izdatel'stvo Publ., 2007, 224 p.
  6. Marca D., McGowan C. Metodologiya strukturnogo analiza i proektirovaniya SADT [SADT: Structured Analysis and Design Techniques]. Moscow, MetaTekhnologiya Publ., 1993, 240 p.
  7. Anderson C. Creating a Data-Driven Organization: Practical Advice from the Trenches. O'Reilly Media, 2015, 302 p.
  8. Frawley W.J., Piatetsky-Shapiro G., Matheus C.J. Knowledge Discovery in Databases: An Overview. AI Magazine, 1992, vol. 13, iss. 3, pp. 213–228. URL: Link
  9. Barsegyan A.A. et al. Tekhnologii analiza dannykh: Data Mining, Visual Mining, Text Mining, OLAP [Data analysis technologies: Data Mining, Visual Mining, Text Mining, OLAP]. St. Petersburg, BkhV-Peterburg Publ., 2007, 384 p.
  10. Kim W. et al. A Taxonomy of Dirty Data. Data Mining and Knowledge Discovery, 2003, vol. 7, pp. 81–99. URL: Link
  11. Bellman R.E. Adaptive Control Processes. A Guided Tour. Princeton, N.J., Princeton University Press, 1961, 255 p.
  12. White C. Data Integration: Using ETL, EAI, and EII Tools to Create an Integrated Enterprise. BI Research, 2005, no. 11, pp. 25–43.
  13. Kresov A.A., Uvarov V.V. [Principles of Data Integration in Subsoil Management]. Vestnik kibernetiki = Proceedings in Cybernetics, 2011, no. 10, pp. 83–89. URL: Link (In Russ.)
  14. Ternovskii D.S., Lavrova Yu.S. [Methodological aspects of transaction costs assessment in the organization's activities]. Vestnik Belgorodskogo universiteta kooperatsii, ekonomiki i prava. Ser.: Ekonomicheskie nauki = Herald of the Belgorod University of Cooperation, Economics and Law. Series: Economic Sciences, 2014, no. 4, pp. 85–90. URL: Link (In Russ.)
  15. Kir'yanov I. [Quantitative evaluation of transaction costs organizations. The general methodological approach]. Vestnik NGUEU = Vestnik NSUEM, 2015, no. 4, pp. 78–101. URL: Link (In Russ.)
  16. Kaplan R., Cooper R. Funktsional'no-stoimostnoi analiz. Prakticheskoe primenenie [Cost and Effect: Using Integrated Cost Systems to Drive Profitability and Performance]. Moscow, Vil'yams Publ., 2017, 352 p.
  17. Usenko V.R., Sklyarova V.A., Sheravner V.M. Funktsional'no-stoimostnoi analiz v kommercheskikh organizatsiyakh: teoriya i praktika [Activity Based Costing (ABC) in commercial organizations: Theory and practice]. Moscow, Flinta Publ., 2015, 206 p.

View all articles of issue

 

ISSN 2311-8725 (Online)
ISSN 2073-039X (Print)

Journal current issue

Vol. 23, Iss. 11
November 2024

Archive