A system for the intellectual analysis of data of trade enterprises with methods for data clustering, search for associations, classification and analysis of time series from data OLAP-cubes is developed. Data analysis algorithms are implemented in the R language. The system is designed to search for associative links by product categories, analyze the effectiveness of marketing expenses and other typical tasks of large trading enterprises. The system allows to analyze and make a decision on the most important tasks of financial and economic activities of medium and large trading enterprises. Data Mining methods allow to search for the main five regularities (classification, clustering, association, sequence, and forecasting) that are the value in the analysis of trading system data.

A set of standard reports, implemented on the basis of OLAP cubes, was developed with analysis of data in the following areas. Standard reports were developed to solve the following problems:

• Analysis of purchases and sales in the context of goods movements
• Profit and sales performance analysis
• Analysis of purchases and sales in the context of goods movements, taking into account payments for goods.
• Search for associative links by product category, analysis of the effectiveness of marketing costs
• Search for close by characteristics goods using cluster analysis
• Classification of goods based on FMR analysis
• And others