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BI (business intelligence) is computer methods and tools for organizations that translate transactional business information into a form suitable for business analysis as well as tools for working with information processed in this way.

The BI and business analytics terms are often used interchangeably, but there is a difference between them. Business analytics (in the narrow sense), unlike BI, deals with already refined data prepared for analysis, uses statistical and quantitative tools for assessing the current situation and forecasting. That is why it is more often called "advanced analytics".

BI, Business Intelligence, initially refines and consolidates data, converts it into the easy-to-analyze format. The following tasks are to interpret a large amount of data, focusing only on the key factors affecting efficiency, model the outcome of various options for action, and track decision results. The main purpose of BI is decision making for business.

BI supports a variety of decision making processes – from operational to strategic. Key operational decisions include product positioning or pricing. Strategic business decisions include priorities, goals, and directions. The BI system is the most effective when it combines data obtained from the market in which the enterprise operates (external data) with data from sources within the enterprise, such as financial and production ones (internal data). In combination, external and internal data provide a more complete picture of business, i.e. analytics that cannot be obtained from analyzing data from only one of these sources.

BI systems are developing in four main areas:

  • Data storage. The data in the BI storage system (data warehouse, DW) is structured in a special way for more efficient analysis and query processing (as opposed to regular databases, where information is organized so as to optimize the processing time of current transactions).
  • Data integration. To form and maintain data warehouses, ETL tools are used – tools that provide data extraction and transformation, i.e. reduction to the required format, and loading data in the storage or another database.
  • Data analysis. OLAP tools (online analytical processing) are used for comprehensive data analysis. They allow considering different data slices, identify trends and dependencies (by region, product, customer, etc.).
  • Presentation of data. Various graphical tools are used to present data – reports, graphs, and charts. Dashboards are common data visualization tools that display results in the form of indicators and scales that allow monitoring the current values ??of selected indicators, compare them with the minimum/maximum permissible ones, and thus identify potential threats to business.
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