Abstract
Today, most organizations use databases and at worst text documents and spreadsheet files as sources for data analysis, which prevent correct and error-free analysis. At best, the data can be constantly adjusted due to ambiguities and inaccuracies. The subject of the study is the intellectual analysis of outdoor advertising data. The methodology of successful data analysis is the correct storage of data, which is the basis for clear data analysis. Modern computer systems and computer networks allow the accumulation of large arrays of data to solve problems of processing and analyzing. Unfortunately, the machine form of data presentation itself contains the information that a person needs in a hidden form, and you need to use special methods of data analysis to obtain it. In order to get what you want, you need to create not just a database, but a data warehouse with a special storage structure. Thus, the data warehouse allows you to collect data from various sources, databases, table files and other things, store them throughout history and, unlike conventional databases, allows you to create systems for fast and accurate data analysis. Data warehouse is the basis for building decision support systems. Operational data is checked, cleared and aggregated before entering the data warehouse. Such integrated data is much easier to analyze. Different sources of operational data may contain data describing the same subject area from different points of view (for example, from the point of view of accounting, inventory control, planning department, etc.). A decision made on the basis of only one point of view can be ineffective or even erroneous. The goal is to use a data warehouse to integrate information that reflects different perspectives on the same subject area. Focus on the object, which will also allow the data warehouse to store only the data you need to analyze it. It will also significantly increase the speed of data access both due to the possible redundancy of the stored information and due to the exclusion of modification operations. Conclusion: the decision support system will ensure reliable storage of large amounts of data. Tasks will also be assigned to prevent unauthorized access, data backup, archiving, etc.
Highlights
The practice of using OLTP systems has shown the inefficiency of their use for full analysis of information
To enable the analysis of accumulated data, it is necessary for organizations to create data warehouses, which are integrated data collections accumulated from different systems of operative access to data
The fifth step is to transform the data; at this stage, the following procedures are performed: data aggregation, translation of values, creating fields and data cleaning. – Data aggregation will provide a smaller number of short records that will be transferred to DW. – Translation of values replaces encoded data with clearer descriptions. – Creating fields allows you to create not just ordinary fields, and special fields that will exclude additional calculation operations. – Data cleaning is aimed at detecting and removing errors and inconsistencies in the data in order to improve their quality
Summary
The practice of using OLTP systems has shown the inefficiency of their use for full analysis of information Such systems quite successfully solve the problem of collecting, storing and retrieving information, but they do not meet the requirements necessary for modern DSS (Barsehyan, Kupryyanov, et al, 2009). In the process of data accumulation, the need to use data mining methods as an effective aid increases; this allows the researcher to gain additional knowledge on the subject area, in which. He or she works, and must make informed decisions (Serheiev-Horchynskyi, Ishchenko, 2018). Data analysis is a part of an interactive automated system designed to help and support various human activities in making decisions about solving structured or unstructured problems
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