Зрелость российских предприятий во внедрении аналитики данных
Целью данного исследования было понять, подходят ли существующие модели аналитической зрелости для использования в российских предприятиях, и исследовать текущий уровень аналитической зрелости Российских предприятий. Для достижения цели в исследовании использовался структурированный опрос мнений специалистов в области аналитики данных, работающих в Российских компаниях о том, как в их компании применяют аналитику данных. С последующим применением трех видов анализа полученных результатов. Вначале результаты были описаны с помощью описательного анализа. Затем была применена обратная пошаговая регрессия для подтверждения или опровержения выдвинутых гипотез. На последнем этапе был применен кластерный анализ для выявления уровней аналитической зрелости российских компаний и особенностей, присущих компаниям на каждом уровне. В результате было описано, как российские компании используют аналитику данных, на основании мнений сотрудников, работающих в этих компаниях. В результате проверки гипотез было выявлено, что российские компании используют аналитику данных в соответствии с существующей теорией об использовании аналитики данных. Это говорит о том, что российские компании могут применять существующие модели аналитической зрелости. В результате кластерного анализа были определены уровни аналитической зрелости российских компаний. Было дано описание того, как компании на определенных уровнях аналитической зрелости применяют анализ данных. Были сформулированы рекомендации по развитию аналитики данных для компании на каждом уровне.
TABLE OF CONTENTS ………………………………………………………………………………………………….6 INTRODUCTION ……………………………………………………………………………………………………………8 CHAPTER 1. THEORETICAL BACKGROUND ……………………………………………………………..10
1.1. Big Data………………………………………………………………………………………………………………10
1.2. Data Analytics ……………………………………………………………………………………………………..11
1.3. Instruments and methods of data analytics……………………………………………………………….14 1.3.1. Data storage …………………………………………………………………………………………………..14 1.3.2. Distributed computing …………………………………………………………………………………….14 1.3.3. Tools for data analytics……………………………………………………………………………………15 1.3.4. Methods of Data Analytics ………………………………………………………………………………17
1.4. Organizational issues of big data analysis………………………………………………………………..18 1.5. Analytics in Russian Companies …………………………………………………………………………….21 1.6. Analytics maturity models……………………………………………………………………………………..21 1.7. Research gaps and research questions ……………………………………………………………………..24
CHAPTER 2. METHODOLOGY …………………………………………………………………………………….26
2.1. Research Methodology ………………………………………………………………………………………….26
2.2. Conceptual research model…………………………………………………………………………………….27
2.2.1. Data collecting method justification………………………………………………………………..28
2.2.2. Research hypotheses formulation……………………………………………………………………29
2.2.3. Justification of the hypothesis testing method…………………………………………………..32
2.2.4. Justification of the method for determining the levels of analytical maturity………..33
2.3. Expected findings …………………………………………………………………………………………………35 CHAPTER 3. EMPIRICAL STUDY ………………………………………………………………………………..36 3.1. Survey design ………………………………………………………………………………………………………36 3.2. Data collection……………………………………………………………………………………………………..38 3.3. Description of companies and survey participants …………………………………………………….38 CHAPTER 4. RESULTS ANALYSIS………………………………………………………………………………41 4.1. Results of descriptive analysis and hypotheses testing ………………………………………………41 6
4.1.1. Data perspective …………………………………………………………………………………………….41 4.1.2. Data analytics governance perspective………………………………………………………………43 4.1.3. Target perspective…………………………………………………………………………………………..45 4.1.4. Analyst perspective…………………………………………………………………………………………47 4.1.5. Technologies perspective ………………………………………………………………………………..49
4.2. Maturity levels of analytics based on real performance of Russian companies……………..51 4.2.1. Cluster 4 description……………………………………………………………………………………….54 4.2.2. Cluster 3 description……………………………………………………………………………………….57 4.2.3. Cluster 2 description……………………………………………………………………………………….60 4.2.4. Cluster 1 description……………………………………………………………………………………….63
4.3. Result discussion ………………………………………………………………………………………………….66 4.3.1. Results of descriptive analysis and hypotheses testing ………………………………………..66 4.3.2. Results of cluster analysis and recommendations ……………………………………………….68 4.3.3. Limitations and further research……………………………………………………………………….70
CONCLUSION ……………………………………………………………………………………………………………..71 LIST OF REFERENCES…………………………………………………………………………………………………75 APPENDIX A. CLUSTERING VARIABLES …………………………………………………………………..79 APPENDIX B. SURVEY QUESTIONS……………………………………………………………………………80 APPENDIX C. THE LIST OF COMPANIES PARTICIPATED IN SURVEY………………………93
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.
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