2018. No. 2 (44)

Fedor V. Krasnov - Expert, Science & Technology Centre, Gazprom Neft PJSC
Address: 75–79 liter D, Moika River Embankment, Saint Petersburg, 190000, Russian Federation
E-mail: Krasnov.FV@gazprom-neft.ru

Alexander V. Butorin - Expert, Science & Technology Centre, Gazprom Neft PJSC
Address: 75–79 liter D, Moika River Embankment, Saint Petersburg, 190000, Russian Federation
E-mail: Butorin.AV@gazpromneft-ntc.ru

Alexander N. Sitnikov - Deputy Chief Executive Officer for Geology and Field Development, Science & Technology Centre, Gazprom Neft PJSC
Address: 75–79 liter D, Moika River Embankment, Saint Petersburg, 190000, Russian Federation
E-mail: Sitnikov.AN@gazpromneft-ntc.ru

      The business goal of interpreting seismic data has always been addressed by the high-level experts engaged. The authors applied a computer vision approach to interpret seismic data. The expert task of interpreting seismic data has become partially automated via machine learning techniques utilized to classify the images used by the authors. The methods of transformation of seismic traces through spectral decomposition were used to obtain the data set. In the previous works of the authors, methods of spectral decomposition via continuous wavelet transformation were created, and this also laid the foundation of this study.
      Use of artificial neural networks of deep learning has enabled the authors to meet the goal of image classification. In this regard, it is important to note that the business policy related to information dissemination imposed certain limitations on the computing capacity used and the number of the data labeled. The solution found for the use of trained artificial neural networks and image augmentation helped us to successfully cope with the goal, in spite of the limitations.
      The results obtained allow us to identify geological units with a test accuracy of 90% rendering to the F1-score measure. This has enabled the Scientific and Technical Center of Gazprom Neft to implement automated procedures in the existing business processes in order to significantly reduce the time needed to process seismic data.
      The authors consider the possibility of “digitizing” and preserving the knowledge of the highest-level experts in interpreting seismic data, as well as the possibility of using contactless examination to locate geological units in the seismic data within the Gazprom Neft group of companies to be a socially efficient outcome of this study.

Eduard A. Babkin - Professor, Department of Information Systems and Technologies, National Research University Higher School of Economics
Address: 25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod, 603155, Russian Federation
E-mail: eababkin@hse.ru

Tatiana S. Babkina - Senior Lecturer, Department of Information Systems and Technologies, National Research University Higher School of Economics
Address: 25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod, 603155, Russian Federation
E-mail: tbabkina@hse.ru

Boris I. Ulitin - Senior Lecturer, Department of Information Systems and Technologies, National Research University Higher School of Economics
Address: 25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod, 603155, Russian Federation
E-mail: bulitin@hse.ru

      Joint analysis of the general structure of online Internet discussions and different attributes of particular text comments becomes an important scientific task in theoretical and applied aspects. Although methods of machine learning facilitate stochastic analysis of text messages, appropriate modeling of dynamics of online discussion and psycho-linguistic characteristics of comments in the presence of multiple individual authors remains the unresolved problem. In this article, the authors suggest applying the methods of multi-agent simulations for resolution of that problem. This work offers two models of online discussion which allow us to take into account characteristics of individual comments and the presence of multiple authors with individual models of behavior. The behavior models are designed in the result of analysis of actual online Internet discussions. The first model is centralized and represents the behavior of each author in the same manner, using a set of fixed parameters. In comparison to the centralized model, the multi-agent distributed model states the individualized behavior for every author through the Markov chain of the special form. Such individualized structure allows us not only to approach the real dynamics of the discussion, but also to compare the models with the actual online Internet discussions. Using pre-processed factual data of real discussions from various Internet portals became an important feature of the suggested approach to simulation modeling. Pre-processing includes expert evaluation of psycho-linguistic characteristics (intent and content analysis), as well as methods of mathematical statistics. Therefore, this research is a positive example of inter-disciplinary research of Internet communication phenomena.

This work was supported by the Russian Foundation for Basic Research (project No. 16-06-00184 А)

Yuri A. Zelenkov - Professor, Department of Information Systems and Digital Infrastructure Management, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: yzelenkov @hse.ru

      A modern enterprise has to react to permanent changes in the business environment by transformation of its own behavior, operational practices and business processes. Such transformations may range from changes of business processes to changes of information systems used to support the business processes, changes in the underlying IT infrastructures and even in the enterprise information system as a whole. The main characteristic of changes in a turbulent business environment and, consequently, in the enterprise information system is unpredictability. Therefore, an enterprise information system should support the operational efficiency of the current business model, as well as provide the necessary level of agility to implement future unpredictable changes of requirements.
      This article aims to propose a conceptual model of an agile enterprise information system, which is defined as a working system that should eliminate the largest possible number of gaps caused by external events through incremental changes of its own components. A conceptual model developed according to the socio-technical approach includes structural properties of an agile enterprise information system (actors, tasks, technology, and structure). Structural properties define its operational characteristics, i.e. measurable indicators of agility – time, costs, scope and robustness of process of change. Different ways to build such an agile system are discussed on the basis of axiomatic design theory. We propose an approach to measurement of time, cost, scope and robustness of changes which helps to make quantitative estimation of the achieved level of agility.

Vladimir I. Ananyin - Senior Lecturer, Department on Business Processes Management, The Russian Presidential Academy of National Economy and Public Administration  
Address: 82, Prospect Vernadskogo, Moscow, 119571, Russian Federation 
E-mail:v.ananiin@gmail.com

Konstantin V. Zimin - Editor-in-Chief, Information Management Journal, Member of the Board, The Russian Union of CIO  
Address: 34, Seleznevskaya Street, Moscow, 123056, Russian Federation 
E-mail: konst.zimin@gmail.com

Mikhail I. Lugachev - Professor, Head of Department of Economic Informatics, Lomonosov Moscow State University; Academic Supervisor, IBS Corporate University
Address: 1, build. 46, GSP-1, Leninskie Gory, Moscow, 119991, Russian Federation
E-mail: mlugachev@gmail.com

Rinat D. Gimranov - Head of IT Department, OJSC Surgutneftegaz; Head of Surgutneftegaz Department of Vocational Relationships, Surgut State University  
Address: 1, block 1, Grigoriya Kukuevitskogo Street, Surgut, 628415, Russian Federation
E-mail: gimranov_rd@mail.ru

Kirill G. Skripkin - Associate Professor, Department of Economic Informatics, Lomonosov Moscow State University;
Address: 1, build. 46, GSP-1, Leninskie Gory, Moscow, 119991, Russian Federation
E-mail:k.skripkin@gmail.com

      This article discusses the characteristic changes in management practices occurring in the context of the digital transformation of business. It shows the mutual interconnections of these changes, as well as the links to changes in the organizational culture of the organization. Among the new management practices reviewed are those both at the level of the enterprise as a whole (digital products, digital business models, digital management of value creation chains, digital business processes), as well as on the local level in adoption of management decisions – unlimited knowledge and management of the enterprise in real time (Real Time Enterprise). The article demonstrates the need for formation of certain cultural norms in the organization, including total knowledge management and an orientation to rapid changes. Review is made of the succession and qualitative distinctions of traditional automation from digitalization of enterprises. We discuss the possibility of using theories and methods connected with such concepts as complementary assets for research into new forms of organization for the digital enterprise.
      The article also presents a research program conducted in the framework of a program for digital transformation of activities of the OJSC Surgutneftegaz, Orbita 2.0. In the given research program the accent is placed on analysis of the problem of sustainability of the organization. In order for the organization to be flexible and changeable, it should periodically be in a condition of instability. In the contrary case, strong resistance to change will develop in it. The search for principles and forms of organization ensuring the controllability of sustainable organizations is an important area of this research. 

Evgeniy A. Isaev - Professor, Head of Department of Information Systems and Digital Infrastructure Management, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: eisaev@hse.ru

Nina L. Korovkina - Associate Professor, Department of Information Systems and Digital Infrastructure Management, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: nkorovkina@hse.ru

Maria S. Tabakova - Graduate of MSc Program, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: mstabakova@edu.hse.ru

      Digital business transformation is a priority for Russian companies in all industries. To develop a company to its full value in the digital environment, it should include an IT department capable of meeting business needs. Evaluation of the current state of the IT department in terms of digital transformation will determine the company’s potential for further development.
      This article presents a solution to the problem of assessing the IT department’s readiness for digital business transformation by developing a quantitative assessment of the maturity level of the IT department processes for meeting the needs of the enterprise. The approach to solving this problem consists in the joint use of models for assessing the digital maturity of the enterprise as a whole and models for assessing the maturity of the IT department processes  and herein is the scientific novelty of the results obtained.
      At the first stage of the study, based on the analysis of modern information and digital management practices, as well as on the study of approaches to assessing the digital maturity of the enterprise and the processes of the IT department, we developed the requirements for the IT department maturity model of digital business transformation. The study identified the prospects for IT departments that affect its maturity level, developed a model for quantifying each perspective and a model for calculating the minimum level of maturity of the IT department to achieve the expected assessment of the company’s digital maturity.
      To assess the willingness of IT departments to digitally transform business, a regression equation of IT department maturity level is constructed from the influencing prospects (factors). The results of approbation of the model are presented.

Tatiana K. Kravchenko - Professor, Head of Department of Business Analytics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: tkravchenko@hse.ru

Nikolay I. Golov - Senior Lecturer, Department of Business Analytics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: ngolov@hse.ru

Aleksey V. Fomin - Senior Lecturer, Department of Business Analytics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: afomin@hse.ru

Aleksey Y. Lipatnikov - Graduate of MSc Program, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: lipatnikov94@gmail.com

      Developing variants of scenarios for a production enterprise’s achieving a desirable financial position using simulation modeling tools requires a specification and verification of requirements for the simulation model. Availability of such variants of scenarios allows stakeholders to select the most efficient solution. The verification is performed by business analysts and key stakeholders; the aim is to assess readiness of the requirements for final approval and to check that the requirements provide all material information for future work. Verification includes evaluating the requirements regarding their compliance with the company’s business analysis standards, as well as assessment of the model’s completeness and common terminology used for description of the requirements. Understanding the desired solution, which meets all the stakeholders’ requirements, is the most important element in requirements verification. 
      For developing a simulation model, which is essential for determining variants of development scenarios and covers financial flows of a typical production company, a list of verified requirements is determined. Criteria of requirements verification include not only acceptance criteria, but also the Graphical Requirements Analysis framework (GRA framework), that is used for verification of functional requirements. In contrast with other notations, representation of requirements for using the GRA framework allows one to understand their structure and internal logic, as well as to detect emergent effects. The result of determining the verification criteria is the Verification Cross Reference Matrix (VCRM) that includes all the requirements, methods and criteria. In the final stage, we present an example of a diagram for one of the functional requirements.
      The verified requirements should be used at different stages of constructing the simulation model, which is focused on development of scenarios for achieving the production enterprise’s desired future financial position. Modeling scenarios of future development of the types ”what will happen, if…?” and “what to do to achieve the goal?”, using simulation modeling systems allows one to dramatically increase the quality of decision making.