The business provides many alternatives, and an investor-entrepreneur has always to make a decision on how best to use his fund. The first challenge for a potential investor is that the business environment is constantly changing, becoming more complex. The process of data acquisition and subsequent information generation is continuously cyclical, time and cost consuming. The second challenge is analytics. The typical small business owner makes decisions by trial-and-error. In today's data-driven AI-first environment to make frequent and quick strategic, tactical, and operational decisions, the owner/entrepreneur needs advice and guidance on startup specific issues, which should only be provided by professional experts. The goal of this research is to elaborate a theoretical foundation of an information support model for a startup – the Startup-Information-Support Model (SISM). The study uses a deep theoretical study - a qualitative method - by applying content analysis of relevant literature and primary research techniques. PESTEL and the Porter's five forces model are employed as analytical tools of expert-opinion technique. PESTEL provides a framework for information collection and data analysis at mega level, while industry attractiveness analysis is based on the Porter's five forces model. The strength of each factor (each force) is analyzed through well-defined concrete indicators, and then is measured by scoring their values. Finally, the SISM presents the whole portrait of attractiveness of specific industry. The SISM equips consultant companies with an effective tool for conducting business intelligence and analytics. The companies would be able to offer additional value added to B2B services to potential investor-entrepreneurs. The Model has the potential to employing AI, which can more systematically incorporate environmental issues at both levels – mega forces and industry factors - into the monitoring process. Equipped with such tools, decision makers can perform complex simulations, test many possible scenarios, and quickly evaluate various impacts at low cost.

Abstract² The paper is considered to the problems of development of analytical information resource management systems. The authors present a serviceoriented architecture solution that provides data collection and aggregation at the points where information emerges. The user is provided by a set of functional complexes, allowing to build the applications covering the entire lifecycle of analytical information resource from the planning data collection to the stages of data processing.