Economic Analysis: Theory and Practice

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Predictive and prescriptive analysis: Theoretical aspects

Vol. 18, Iss. 7, JULY 2019

Received: 22 May 2019

Received in revised form: 4 June 2019

Accepted: 17 June 2019

Available online: 30 July 2019


JEL Classification: G30, G32

Pages: 1243–1255

Kogdenko V.G. National Research Nuclear University MEPhI, Moscow, Russian Federation

Subject The article considers theoretical issues of predictive and prescriptive analysis.
Objectives The aim is to summarize algorithms and areas of prognostic and prescriptive analysis and unveil sources of company efficiency as a result of use of digital analytics.
Methods I employ general scientific principles and methods of research, like analysis and synthesis, grouping and comparison, abstraction, and generalization.
Results The paper gives characteristics of predictive and prescriptive analysis, modeling algorithms, and identifies six areas of analysis. I consider the modeling algorithm that includes work with training and test datasets. Within each area of analysis, I highlight the main elements and sources of business efficiency improvement.
Conclusions The paper defines ways to improve business efficiency as a result of the use of digital analytics. They include adaptation of product and marketing complex to clients' needs, lowering the cost of business processes, development of effective personnel policy, preventive decision-making on fraudulent operations, business model optimization. The article may be useful for specialists of analytical services of companies.

Keywords: predictive analytics, prescriptive analytics


  1. Morkhat P.M. Pravo intellektual'noi sobstvennosti i iskusstvennyi intellekt: monografiya [Intellectual property law and artificial intelligence: a monograph]. Moscow, YUNITI-DANA Publ., 2018, 121 p.
  2. Mayer-Schönberger V., Cukier K. Bol'shie dannye. Revolyutsiya, kotoraya izmenit to, kak my zhivem, rabotaem i myslim [Big Data. A Revolution That Will Transform How We Live, Work, and Think]. Moscow, Mann, Ivanov i Ferber Publ., 2014, 240 p.
  3. Siegel E. Proschitat' budushchee: Kto kliknet, kupit, sovret ili umret [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die]. Moscow, Al'pina Pablisher Publ., 2018, 374 p.
  4. Davenport T. et al. O chem govoryat tsifry. Kak ponimat' i ispol'zovat' dannye [Keeping up With the Quants: Your Guide to Understanding and Using Analytics]. Moscow, Mann, Ivanov i Ferber Publ., 2014, 224 p.
  5. Bruskin S.N. [Methods and tools of advanced business analytics for corporate information analytical systems in the digital transformation era]. Sovremennye informatsionnye tekhnologii i IT-obrazovanie = Modern Information Technology and IT Education, 2016, vol. 12, no. 3-1, pp. 234–239. URL: Link (In Russ.)
  6. Aptekman A., Kalabin V. et al. Tsifrovaya Rossiya: Novaya real'nost' [Digital Russia: New Reality]. URL: Link (In Russ.)
  7. Tyshkovskii R. Chto delat' SEO vo vremya tsifrovoi revolyutsii. V kn.: Biznes v tsifrovuyu epokhu [What CEOs should do during the digital revolution. In: Business in the digital age]. URL: Link
  8. Loleyt M. et al. Matematika rossiiskogo lyuksa: perspektivy rosta i potrebitel'skoe povedenie. Ispol'zovanie uglublennoi analitiki dlya sovershenstvovaniya strategii rosta lyuksovykh brendov [Mathematics of the luxury market in Russia]. URL: Link (In Russ.)
  9. Lyubushin N.P., Lykov A.I., Babicheva N.E. [Use of resource oriented economical analysis in estimation of stable development of managing subjects]. Vestnik Tambovskogo universiteta. Ser.: Gumanitarnye nauki = Tambov University Review. Series: Humanities, 2015, no. 2, pp. 32–45. URL: Link (In Russ.)
  10. Dobrynin A.P., Chernykh K.Yu., Kupriyanovskii V.P., Sinyagov S.A. [The Digital Economy – the various ways to the effective use of technology (BIM, PLM, CAD, IOT, Smart City, BIG DATA, and others)]. International Journal of Open Information Technologies, 2016, vol. 4, no. 1, pp. 4–11. URL: Link (In Russ.)
  11. Bruskin S.N. [Models and tools of predicting analytical research for digital corporation]. Vestnik Rossiiskogo ekonomicheskogo universiteta im. G.V. Plekhanova = Vestnik of the Plekhanov Russian University of Economics, 2017, no. 5, pp. 135–139. URL: Link (In Russ.)

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Vol. 18, Iss. 7
July 2019