Systematization of the maturity levels of the corporate architecture in the field of data analysis

The use of modern technologies and methods of data analysis allows you to create more advanced information systems for the organization of high-quality business. Therefore, when designing a cor-porate data architecture, it is necessary to take into account many factors and correctly select a cer-tain level of maturity. This article provides an overview of technologies and methods in the field of data analysis. The tools in question are in demand when building the company's analytical architecture. They can be used to search for, access, and process data. The advantages and disadvantages of the methods were consid-ered. The comparison of technologies is made by a number of characteristics, namely: the organization of data access, the method of building a data warehouse, the process of extracting, converting data, and the process of building a business report. These aspects are the main ones when choosing the tools for building a corporate architecture in the field of data analysis, since they are key in analytical data processing.

1. What is Enterprise Data Storage (DWH): BigData Basics. Available at: https://www.bigdataschool.ru/ (accessed 20.05.2021)

2. Sarka D. Microsoft SQL Server 2012. Implementation of data warehouses. 2014.

3. Zhukovsky O. I. Data storage. 2015.

4. Data Lake. Available at: https://habr.com/ru/post/485180/ (accessed 20.05.2021)

5. What are data lakes and why it is cheaper to store bigdata in them. Available at: https://mcs.mail.ru/blog/chto-takoe-ozera-dannyh-i-zachem-tam-hranyat-big-data (accessed 20.05.2021)

6. Mitchell T. Machine learning. McGraw-Hill Science/Engineering. 1997.

7. Talachaev I. E. Application of machine learning in the classification problem using the Py-thon language. Military innovation Technopolis "ERA". 2019.

8. Paklin N. B. Business analytics: from data to knowledge. 2013.

9. Notkin L. I. Artificial intelligence and learning problems. 1999.

10. Margaret A. Boden. Creativity and artificial intelligence. Elsevier. Artificial intelligence 1998;103:347-356

11. Shalyutin S. M. Artificial intelligence. 1985.

12. Magic Quadrant for Data Management Solutions for Analytics. Available at: https://b2bsalescafe.files.wordpress.com/2017/07/magic-quadrant-for-data-management-solutions-for-analytics-feb-2017.pdf (accessed 20.05.2021)