Digital Transformation as a Determinant of the Essence of Modern Analytical Software

Abstract

Goal. The present study is aimed at identifying the key features of new digital model of business analytics and determination of methodological conditions necessary for its effective implementation. Special attention is paid to corporate data management as the main factor of ensuring their quality and reliability for making reasonable decisions. Methods. Such general scientific research methods as analysis, synthesis, comparison, and generalization are applied. The work is based on the analysis of modern scientific publications and international standards in the field of analytical systems, management and data quality. Comparative generations review of business analytics (Analytics 1.0-3.0) was performed, and the concept of expanded analytics combining artificial intelligence and machine learning methods to automate data preparation and analysis processes was considered. Results. It is shown that transition to intelligent, proactive analytics based on AI technologies allows companies to make decisions in real time, thus increasing their flexibility and competitiveness. At the same time, it is emphasized that sustainable effect due to introduction of such technologies is possible only if there is a developed institutional environment for working with data. To do this, it is necessary to formalize the principles of data management, that is, to define the roles, responsibilities and policies that ensure stable data quality at every stage of analytical cycle. Without mature data management system, even the most modern analytical platforms can be reduced only to formal visualization of reports that do not provide practical be-nefits. Thus, successful implementation of expanded analytics requires not only technological investments, but also the development of corporate governance mechanisms and data quality assurance, which is the key element of digital transformation of business intelligence.