The relevance of information and analytical support for management of the security potential of an enterprise is due to the increasing complexity of the market environment, high variability of demand and the speed of the emergence of technological innovations. By security potential we mean a set of resources, competencies and development opportunities that are capable of creating economic value under the condition of timely and justified management decisions. The quality of these decisions depends on the ability of the enterprise to collect structured and unstructured data, clean them, integrate them with internal and external sources, and convert them into knowledge and forecasts. The purpose of the article is to theoretically substantiate information and analytical support for management of the security potential of an enterprise with an emphasis on data quality, knowledge analytics, organizational manageability and measurable results. The object of the study is the system of management decisions of the enterprise, which is based on integrated data, analysis methods and institutional rules of interaction of departments. The article considers the information and analytical support of the management of the security potential of the enterprise as a managed system of data transformation into knowledge and results. The conceptual principles of building a single data loop with an emphasis on quality, compatibility, accessibility and security are determined. The need for combining strategic goals with measurable performance indicators is substantiated, which makes it possible to coordinate decisions between finance, operations, marketing and information technology. It is established that the synergistic effect is achieved under the condition of systematic interaction of business analytics, machine learning models and solutions based on artificial intelligence in combination with data management policies. The organizational roles of data owners, administrators and analysts are characterized, as well as management practices that support version control, change logging, quality audit and transparent interpretation of results. The importance of ethical responsibility and privacy protection as inseparable elements of the analytical loop is revealed.
Горбан et al. (Wed,) studied this question.