Зрелость российских предприятий во внедрении аналитики данных

In recent decades, due to the development of technology, companies have been able to collect more and more data. The analysis of the collected data allows the company to gain significant advantages, gain new knowledge about the company’s processes, products, and the industry in which they work. However, effective implementation of data analytics in a company is not always an easy process. (Morabito, 2015, Posavec & Krajnovic, 2016) Companies are faced with constantly evolving data analysis technologies, and organizational issues that affect the effectiveness of data analytics. So among such problems, there is often a lack of understanding of how analytics can improve business processes, a lack of analytical skills, and a lack of attention from the company’s top management to the problems of data analytics. Therefore, in order for companies to be more effective in the field of data analytics, they need to constantly analyze the current state of company data analytics and make appropriate decisions to improve it.
In order to help companies in the process of implementing data analytics, tools such as company analytical maturity models have been developed. There are many different models of analytical maturity. These tools allow to evaluate how effectively a company implements data analytics and how far it has progressed in this area. These tools also allow companies to understand what needs to be done to become more mature in data analytics, therefore, they are a decision- making tool. However, the literature raises several important questions regarding the development and use of analytical maturity models. Many authors agree that due to the rapid development of technologies, the model of analytical maturity requires constant improvement, taking into account the emergence of new technologies. Also, several authors raise the issue of the need to adapt the model of analytical maturity to the conditions of the market in which the company operates. At the moment, most of the analytical maturity models have been developed in a developed market, while no analytical maturity models have been found developed and adapted for emerging markets, such as the Russian market. Thus, the use of existing models of analytical maturity for Russian companies raises doubts about its effectiveness on these markets. Therefore, further research is needed on the possibility of applying existing analytical maturity models for Russian companies. Also in the future, an important research issue is the development of an analytical maturity model developed and adapted for the Russian market.
The identified problems in the field of analytical maturity models became the basis for this study. The purpose of this study is to understand whether it is possible to apply existing models of analytical maturity in Russian enterprises, and to study how Russian companies currently use data analytics and what level of analytical maturity they have. This will give an understanding of
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whether Russian companies can use the popular models of analytical maturity that already exist and will lay the foundation for further development of an analytical maturity model adapted specifically for the conditions of the Russian market and Russian companies. With this in mind, the research goal and research questions were formulated.
The research goal of the thesis is to understand whether the existing models of analytical maturity are suitable for use in Russian enterprises, and to investigate the current situation with the analytical maturity of Russian enterprises.
The research questions can be formulated as follows:
RQ1 How do Russian companies currently use data analytics?
RQ2 Can the existing models of analytical maturity be considered applicable for Russian companies?
RQ3 What levels of analytics maturity can be determined for Russian companies? RQ4 What are the characteristics of companies at each maturity level?
RQ5 What Russian companies need to do to get to the next level of maturity?
As a result of the study, all the research questions were answered. It was investigated how Russian enterprises currently use data analytics. The study also confirmed that Russian companies have no major differences in the use of data analytics from foreign companies. This may indicate that the existing models of analytical maturity can be effective for Russian companies. Also, the companies studied were divided into four levels of analytical maturity. The characteristics of companies at each of the revealed levels of analytical maturity were described. Recommendations were also formulated for the company at each of the four levels of analytical maturity, on what companies need to focus on to move to the next level of analytical maturity. The results obtained are the basis for future research in the development of an analytical maturity model adapted for Russian companies. The results also have practical managerial applications, as they offer concrete actions for companies to improve data analytics.