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The potential of business intelligence for the development of analysis of financial and non-financial information in the economic activities of companies.

Mitrovic Stanislav

ORCID: 0000-0003-0664-7270

Ph.D. Candidate, the department of Accounting, Analysis and Audit, M. V. Lomonosov Moscow State University

119991, Russia, g. Moscow, Leninskie gory, 1, stroenie 46

Mitrovic.Stanislav@hotmail.com
Other publications by this author
 

 

DOI:

10.25136/2409-7802.2023.1.40099

EDN:

RWZRTS

Received:

02-04-2023


Published:

27-04-2023


Abstract: The subject of the study is the information and analytical support of the economic activities of companies based on the introduction of business intelligence. The object of scientific research is the systems of business intelligence in economic analysis.The purpose of the study is to scientifically substantiate the theoretical and methodological issues of the introduction of business intelligence for the development of the analysis of financial and non-financial information in the economic activities of companies. The research methodology is based on the analysis, grouping of data and generalization. Conclusions and scientific novelty. The scientific novelty of the research lies in the development of issues and principles of the application of modern information technologies of business intelligence for the purpose of further development of the possibilities of economic analysis of the economic activities of companies.The authors determined that a theoretically sound methodology for the introduction of business intelligence allows expanding the possibilities of information and analytical support for economic analysis, provided that: 1) "is based on the basic provisions of the theory of economic analysis, accounting and auditing; information theory, as well as on the requirements of current domestic and international standards, recommendations of research and professional organizations and regulatory committees in the field of development of reporting and economic analysis, information law, state regulation and principles of information and economic security" [1]; 2) reflects the basic principles of the method of economic analysis: the unity of analysis and synthesis, the study of economic phenomena in their interrelation, as well as in development and dynamics; 3) specifies the general methodology of economic analysis Ц methods of information processing, working stages and sequence of analysis;4) is based on analysis and application advanced practical and theoretical experience in the field of implementation of information solutions in the economic analysis of economic activity of companies.


Keywords:

economic analysis, business intelligence, investment project, payback, carbon collection, IT, economy, information technology, management, organization

This article is automatically translated. You can find original text of the article here.

IntroductionModern information technologies (IT) have radically changed the possibilities of conducting economic analysis, taking into account potential risks, identifying reserves for the development of economic activity, justification and speed of managerial decision-making, harmoniously supplementing the methodology of strategic and operational management with new capabilities.

The development of information technologies has allowed businesses to develop effective systems for analyzing the activities of companies, taking into account, among other things, the need to "meet the information requests of interested parties to ensure trust and create the foundation for the long-term development of the organization" [1], which is inextricably linked with the creation of conditions for the sustainable development of the organization and the creation of economic value [2].

In this study, we proceed from the definition of "economic analysis as a comprehensive means of obtaining a complete knowledge of economic activity, business knowledge, understanding of the activities of an economic entity" [3]. Thus, the view of economic analysis proposed in the study is based on the ideological semantic constructions of complex analysis and its potential for studying all aspects of business, as well as system analysis as the next stage in the evolution of economic analysis.

The complexity of economic analysis, which consists in studying all aspects of business in their interrelation, as well as the presence of "a single goal that allows combining individual areas of analysis into a single system" [3], implies the need for continuous improvement of information systems that are used for its analytical support. This indicates the evolution of economic analysis and the transition to the next "stage in the development of analysis, which is associated with the use of a systematic approach and simulation modeling, where the object under study Ц an enterprise or its branch Ц is considered as an economic system" [4]. System analysis in this paper is considered as the next level in the development of economic analysis and is also part of the basic semantic structure of the study.

†One of the key criteria that economic analysis should meet is the presence of an "adaptation mechanism embedded in it, which should ensure not only a timely response to changes in the external and internal environment, but also, if possible, predict these changes and thereby develop preventive measures" [5]. The creation of information support systems for the analysis of the activities of organizations of the Business intelligence class (Business Intelligence, BI) is aimed at solving this problem, among other things. The author believes that in modern conditions, the phenomenon of business intelligence from the point of view of economic analysis can be defined as an information support tool focused on the study and in-depth analysis of large amounts of information and heterogeneous data on diverse aspects of business, and their transformation into applied knowledge about the retrospective and prospects of economic activity of organizations. Thus, not only the result of the analysis is put forward in the foreground, but cause-andЦeffect relationships that affect the analyzed indicators and the construction of a logical sequence between them, and it is confirmed that "the basis of economic analysis is a dialectical approach when conducting research on processes and phenomena" [6].

Another challenge and an important incentive for the development of information support for the analysis of economic activity of companies is related to the fact that the interest of business analytics users is increasingly going beyond the indicators of economic activity alone, and especially their retrospective aspect of descriptive analytics. Changing business conditions, high uncertainty, geopolitical risks, pandemic, demographic problems and a number of other variables increase the demand for expanding the analysis of companies' activities towards assessing prospects and possible business scenarios in the future (predictive analytics), as well as for information support, which can be attributed to predictive analytics Ц this is the case when analytics not only helps us to see a comprehensive picture of the business and various possibilities of enterprise management, but also takes the next step and chooses the ideal management solution for us.

Thus, "the modern agenda in business information support will be determined not only by the economic dimensions of business activity" [3]. And in this regard, we observe that the transition to analytical indicators, including even stock portfolios and indices that meet the requirements of sustainable development, is one of the largest transformations in business analytics on a global scale. The requirements for the new integrated reporting format expand data sources far beyond the enterprise itself and include the need to attract heterogeneous, external, off-system, unstructured and big data for economic analysis. Therefore, in recent years, advanced information systems that meet these criteria, including (and primarily) business intelligence systems, have been actively introduced into the economic activities of a wide range of companies both in Russia and abroad.

Bearing in mind the requirements of building transparent and logical links between disparate data, objective information and multiple management functions, and the potential of these links in improving the effectiveness of management decisions, we believe that one of the ways to fulfill these criteria is to introduce advanced information and analytical solutions based on business intelligence systems into economic analysis.

MethodsThe degree of development and relevance of the topic.

In the conditions of universal and widespread recognition of business intelligence, especially against the background of a new wave of the global economic crisis caused by the pandemic, geopolitics and the expectation of a fall in global GDP, with the introduction of new business intelligence systems, "in our opinion, more and more attention in domestic conditions should be paid to complex and systematic economic analysis, which consists in simultaneously solving a number of diverse tasks of organizational, theoretical, practical and methodological nature" [13].

In this study, important issues of the introduction of business intelligence into the economic analysis of companies have been developed, scientifically substantiated and systematized. The methodological principles of the introduction of business intelligence have been tested, and the consistency of the proposed methodology based on the implementation of a practical solution independently developed by the author for information support of economic analysis of companies has been positively assessed. The scientific and socio-economic significance of the proposed developments and their practical application is proved.

ResultsBased on the analysis of theoretical sources and the results of the empirical part of the study, it is concluded that the use of business intelligence systems in the economic analysis of economic activity of companies gives a multiplicative positive effect:

1) contributes to the improvement of the economic analysis of companies, its methodology, the possibility of expanding sources and integrating external and internal data with reporting data, the speed of implementation, and the accuracy of the data provided;

2) ensures the efficiency of the process of economic analysis, allowing with relatively lower costs to achieve the result in a shorter, relatively earlier information and analytical solutions, term;

3) with high efficiency and automation, it ensures the relevance and reliability of data, high accuracy of forecasts for given prerequisites, the ability to scale results and minimize risks underlying sound and rational management decisions;

4) allows you to reasonably respond to a comprehensive information request from users, complementing the retrospective aspect of reporting for past periods and its derived analytical indicators by analyzing prospective information on diverse areas of economic, social, environmental activities of companies, financial and non-financial metrics, identifying external and internal reserves to improve the competitiveness and efficiency of companies, their relationships and dependencies.

The emphasis in analysis is shifting from simple reporting and operational analytical processing of historical information towards more complex analytical tools, such as predictive and predictive analysis tools, in which the results of analyzing a wider range of sources of heterogeneous data obtained using specified business intelligence algorithms are used to issue and scale practical management decisions and recommendations, in particular including in real time. The content of the algorithm for introducing business intelligence into economic analysis is substantiated and structured - this is the process of sequential transformation of large volumes of homogeneous and heterogeneous data into a chain: information Ц understanding Ц general knowledge Ц applied knowledge Ц management decision.Discussion

Speaking about business intelligence in the analysis of companies' activities, it should be noted that there is still no single definition of this phenomenon agreed by a wide range of specialists.

"The problem of ambiguous interpretation of the term persists to this day in both domestic and foreign science and practice. The first mention of the term business intelligence ("business intelligence", "BI") dates back to 1958, when an American researcher, an expert in the field of information sciences H. P. Loon [7] characterized it in his scientific work as a set of individual components:

Ц the "business" component as a set of various activities carried out in science, technology, commerce, industry, legislation, defense, etc.;

Ц the "intelligence system" component as a set of communication systems supporting these types of activity, that is, accompanying intelligent activity" [8].

In scientific publications of Russian authors, there is a broader and more comprehensive approach, which is primarily associated with the definition of the term "digital economy (economy of the digital age), considered (according to Kleiner G.B.) simultaneously as:

1) the stage in the development of the economy at which:

a) the processes of production, distribution, exchange and consumption, including all related communications and interactions, are carried out on the basis of digital technologies;

b) real economic processes, objects, projects, environments in the course of communication and interaction are supplemented, and sometimes replaced by their computer (digital) models.

2) and the environment for the development of innovative processes (one of the components of a dual system tetrad (object, project, process, environment))" [9].

This approach allows us to consider the informatization of economic processes in a broader sense, including "the current state and at the same time the process of transformation" [9], as well as "associated management processes, communication of economic agents, new types of economic and social activity generated by the possibilities of digital economies" [9].

Taking into account the mentioned approach of domestic authors, we emphasize the expansion of the context of the use of information technologies in the economic analysis of economic activity of companies, and the fact that in addition to the method of data transformation (including the result of analysis) and the technologies themselves (including the technical aspect Ц the use of computer technology), an important component of the business intelligence system is also the organization (company), in which the business is conducted by the acting object of analysis, as an environment for the development of innovative processes and a tool that ensures the transition to a new level of business information support.

Source Ц Developed by the author

Figure 1 Ц Business intelligence system in economic analysis as a complex of 3 elements

Figure 1 - Business intelligence system in economic analysis, as a complex of 3 elements. Source: developed by the author†

The approach proposed by the author to the definition of the term business intelligence solves the urgent problem of overcoming the heterogeneity and inconsistency of its assessments not only in the plane of doctrinal analysis, but also for the purposes of law and norm-making. It is this approach that allows you to accurately formulate the tasks and functions of business intelligence, avoiding contradictions, "borderline" formulations and duplication of functions. The proposed approach will allow for targeted fragmentation of tasks and functions of business intelligence for the purposes of economic analysis, limiting them from other tasks and functions.

†††††††††††† Integration of information solutions complements the existing set of methodological tools and the possibilities of economic analysis of economic activity, but does not replace it. The result of economic analysis, even with the use of business intelligence systems in its course, remains the activity of the end user, which methodological information and analytical tools and business intelligence systems complement, expanding the possibilities of analysis to improve the effectiveness of management decisions, including: decision-making speed, risk minimization, availability of relevant sources, processing of a large array of heterogeneous internal and external data in real time, validity, rationality and realism of decisions, accuracy of forecasts, construction of predictive models, situational, predictive and predictive analysis, data quality improvement, inclusion of non-financial indicators of sustainable development, social and environmental reporting, analysis their interrelationships and dependencies.

As the results of the empirical part of the study show, one of the promising areas of business analysis of companies' activities concerns information support for conducting socially and environmentally responsible business, and compliance with the principles of sustainable development. "Ensuring the strategic sustainability of enterprises is one of the main factors for the sustainable development of the country's economy as a whole. This problem is one of the most important, but at the same time it has been little studied. Currently, qualitatively new opportunities are emerging to solve this problem using the advantages of the transition to the digital economy" [10].

At the end of the last century, the concept of sustainable development appeared, which implies business management, in which the company strives not only to make a profit, but also to solve the environmental, social and ethical agenda (ESG). And in this regard, we observe that the transition to stock portfolios and indices that meet the requirements of sustainable development is one of the largest transformations in the global financial sector.†Many Russian companies have also already developed and are successfully implementing plans to switch to a green economy. Sberbank, Polymetal, SIBUR Holding, Russian Railways and others are the leaders in the ESG ratings of Russian companies.†The approach of sustainable development is now more and more interested in investors. If earlier they looked only at financial attractiveness, now non-financial factors, such as ecology and corporate culture, also affect the financial results and the image of the organization.And although ESG is now central to many strategies of companies around the world, there is still a lot of uncertainty when it comes to accurate measurement, and the integration of sustainability indicators into the information environment and investment portfolios.

As part of our research, we analyzed a lot of data on sustainable development, focusing on data on greenhouse gas emissions and environmental data. And we came to the conclusion that indicators for companies are often contradictory and one of the most common complaints of investors about sustainable development analytics is the lack of data consistency and even consistent ratings. There are many examples of companies that are rated very well by some ESG rating providers and very poorly by others. Obviously, this makes it difficult to analyze sustainable development trends, since they will not necessarily coincide from different sources, and this problem is becoming more and more noticeable for investors. The conclusion here is obvious Ц some aspects of business are developing faster than the information environment that should accompany them.

Companies participating in the sample to test the results of the study face similar problems, analyzing not only the overall level of sustainable development, but also more specific areas. One of them is the impact of greenhouse gases and potential risks, and additional costs associated with the introduction of a carbon levy, which is already relevant for foreign companies. Business analytics in this direction is complicated by the fact that, on the one hand, companies set goals to reduce gas emissions, and on the other hand, there is no way to accurately measure the existing level. CO2 and greenhouse gas emissions are at the heart of sustainable investment goals. But this topic is currently one of the most difficult to build reliable and accurate business intelligence. In accordance with current international practices, companies should analyze not only the total amount of greenhouse gas emissions, but break it down into at least 3 categories:

Volume 1:†all direct greenhouse gas emissions that are associated with the company's core business, production and sales.

Volume 2:†Indirect emissions resulting from the consumption of purchased energy.

Volume 3:†Other indirect emissions, such as the production of purchased materials and transportation activities of suppliers and buyers, that is, in organizations that are not controlled by the company that reports.

Thus, companies that want to conduct socially and environmentally responsible business for the first time in history are expected to analyze a large number of data that are difficult to measure and, moreover, that are not even related to its activities, but to the activities of other organizations with which it interacts. Business intelligence systems have a great potential for solving these issues, since they create the possibility of integrating internal and external, financial and non-financial data, analyzing their relationships and forming conclusions.

As part of our research, the author has developed a solution [11]. based on business intelligence to calculate the payback of investment projects, which is based on standard methods of discounting cash flow. The essence of this decision is that the analysis of the financial indicators of the investment project includes data on energy use (electricity, gas, fuel and others), then the solution considers the change in energy consumption in kWh / year and the annual change in emissions in tons of CO2 equivalent. The next step is the calculation of the carbon tax (carbon fee) applied to a ton of CO2 equivalent for all types of energy and the final indicator, which is considered as additional costs in the calculation of the performance indicators of the investment project. For this calculation, the proposed information and analytical solution integrates external and internal data of all divisions of the company consuming different types of energy, emphasizing a multidimensional approach to the formation of cash flows of projects and "environmental analysis, consisting in the study of the mutual influence of the project and the environment" [12].

Source: Developed by the author

Figure 2 Ц Layout of the interface of a business intelligence-based solution for calculating carbon collection

Figure 2 - Interface layout of the solution based on business intelligence for calculating the carbon fee. Source: developed by the author.††Source: Developed by the author

Figure 3 Ц Layout of the interface of a business intelligence-based solution for calculating the payback of investment projects, taking into account the carbon feeFigure 3 - Layout of the interface of a solution based on business intelligence for calculating the payback of investment projects, taking into account the carbon fee.†Source: developed by the author.††

Despite the fact that the carbon tax has not yet been introduced into the practice of Russian organizations, this calculation gives an idea of the possible costs that will appear during the operation of the project, taking into account trends that already exist abroad and consideration of the draft national version of the carbon tax in the Russian Federation.

The analysis of the financial efficiency of the investment project in the proposed automated solution allows you to see the calculation option in the traditional form and taking into account the carbon fee. Thus, heterogeneous data turns into practical knowledge and can be used for informed management decision-making. The emphasis in analysis is shifting from simple reporting and operational analytical processing of historical information towards more complex analytical tools, such as predictive and prescriptive (predictive) analysis, in which the results of analyzing a wider range of sources of heterogeneous data obtained using specified business intelligence algorithms are used to issue and scale practical management decisions and recommendations, including in real time. ††† The most significant cumulative positive effect when using business intelligence in the analysis of the activities of companies included in the sample for the empirical part of the study is reflected in Table 1.

Table 1 Ц The most significant cumulative positive effect when using business intelligence in the analysis of the activities of companies included in the sample for the empirical part of the study

Table 1 - The most significant cumulative positive effect when using business intelligence in the analysis of the activities of companies included in the sample for the empirical part of the study

Characteristics of the information solution

BI + Big Data

Evaluation of the effects of using business intelligence in the analysis of companies' activities

Direction of implementation

Scope of application

Increase in income

Cost reduction

Organizational and additional aspects

1

2

3

4

5

Forecasting

Sales and profit forecast. Analysis of available goods in stock. Production, sales and inventory management

Revenue growth by 15%, due to an increase in sales of individual items ("B, C") to 50%

Reduction of working capital by up to 30% (more accurate forecasting). Reduction of forecast preparation time from 14 to 1 day.

External and internal sources are integrated, the SLA process is established and automated. 95% forecast accuracy (previously: 60-70%)

Sale

Approval of selling prices. Assortment management. Analysis of the inventory matrix for regional warehouses in the Russian Federation.

Sales growth of up to 15%, due to the formation of prices, taking into account the specifics of the buyer.

Reducing the processing time of buyers' request for individual prices by 70%

Automation of the process, integration of data, internal and external systems (the client has access to information about the free stock of goods in the warehouse)

Continuation of table 11

2

3

4

5

Production

Analysis of specific customer requests - development of new products. Analysis of internal reserves

Profit growth by 30% due to product development.

Reduction of production costs by 20% due to the identification of internal reserves

The KPI is defined for each production unit. Formalized accounting of internal reserves (RAR Ц Productivity Action Plan)

Supply Chain Management

Planning Ц SIOP (Sales Inventory & Operations Planning)

Increase in sales of individual (slow-turning) positions up to 50%

Reduction of working capital by 20% due to reduction of inventory days.

The indicator "timely and full satisfaction of demand" (OTIF Ц On-Time and In-Full) -98%

Choosing a strategy

Scenario analysis. Formation of the risk matrix

Sales growth of up to 15% due to entering new markets

Reduction of scenario preparation and analysis time, from 20 to 5 days

Identification of the risk map and key factors affecting the business.

Customer service Ц After-sales activities

Processing claims from buyers. KPIs for measuring customer satisfaction

Reduction of product returns up to 25%

Reduction of request processing time by 30%

Increase in the Consumer loyalty Index (NPS) by 20%

Sustainable development

Integrated Energy Source Management reporting.

-

Reduce energy costs by 10%

Formation of integrated reporting. Inclusion of CO2 in the calculation of projects.

Source - compiled by the author based on the results of the study for the period 2020-2023.

Source - developed by the author based on the results of a study for the period 2020-2023.

The positive multiplicative effect of the use of business intelligence in the analysis of companies' activities is proved by the author through the development, implementation and testing of a practical information and analytical solution to optimize management decision-making, accelerate analysis preparation processes, create the possibility of scaling results, minimize risks and improve the quality, relevance and reliability of analytical data and forecasts.

ConclusionsSumming up, the author generally defines business intelligence as an important strategic aggregated research intangible asset of a modern organization.

Based on the results of the study, the potential of business intelligence for further development of economic analysis capabilities is summarized. Among the main directions of the development of economic analysis in the information environment, the author confirmed (identified):

1) development of the analysis potential taking into account the growth of user information requests;

2) increasing the degree of scientific justification of management decisions by taking into account the influence of various factors of the external and internal environment affecting the analyzed object, due to the variability of the information methodological tools used and the possibilities of comprehensive coverage of the maximum number of indicators;

3) increasing the reliability and reliability of the results and conclusions obtained by conducting as detailed an analysis as possible in a computer environment;

4) development of the potential for real-time research in the field of economic analysis, situational, predictive and prescriptive (predictive) analysis due to the possibilities of information tools and the development of new information technologies (cloud, etc.);

5) expanding the business analysis of companies' activities to the field of non-financial information, including, inter alia, aspects of social and environmental activities and sustainable development;

6) further development of reserves for the implementation of complex analytical studies related to the processing of large amounts of analytical information and unstructured data in order to select and implement the strategy of companies;

7) development of the methodology of informatization of economic analysis using applied knowledge of various sciences, which will contribute to a deeper development of the most significant, fundamental characteristics of information and informatization;

8) further methodological development of indicators for evaluating the effectiveness (success) of the integration of information technologies in the field of economic analysis;

9) further development of risk management methodology in the field under study.

Russian organizations still need to come to a mature methodological approach when implementing advanced information support tools for complex and systematic economic analysis, realizing that the real effect for an organization from the use of business analytics comes when its full potential is used, including planning, forecasting, modeling, budgeting, developing goals and forming key indicators (KPIs), risk management, consolidation of various kinds of external and internal data, application of a system of balanced indicators and expansion of business analysis to all types of company activities, including, in addition to financial reporting data and analytical indicators, and the aspect of social and environmental activities and sustainable development.



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