Technological Foundation of Business Intelligence
December 2013 requires businesses to adopt new approaches to data consolidation. To analyze sales performance or financial results, it is necessary to combine information from many sources that independently perform only operational tasks. The solution to this challenge is building a corporate Data Warehouse (DWH) using ETL processes.
Extract, Transform, Load Processes
The ETL process is a key mechanism for preparing analytical data:
- Extract: obtaining raw data from relational databases (e.g., SQL Server) or ERP systems.
- Transform: converting data to a unified format, cleaning it of duplicates, and calculating derived indicators.
- Load: placing the prepared data into a repository for further analysis.
For building such processes in the Microsoft SQL Server 2012 environment, the standard is to use SQL Server Integration Services (SSIS). This toolkit allows for the automation of data loading, configuration of error logging, and ensuring the regularity of analytical data mart updates.
Analytical Level
After loading data into the DWH, OLAP multidimensional analysis technologies become available. They allow managers to conduct in-depth data analysis across dimensions of time, regions, or product groups. We advise adhering to "star" or "snowflake" schemas when designing the repository, which significantly accelerates report generation.
Criteria for Success
It is important to understand that implementing a warehouse is a project at the intersection of business and IT. It is necessary not only to set up technical tools but also to align rules for calculating indicators between departments. High-quality configuration of ETL procedures, automation of integrity control, and regular data verification are mandatory conditions for analytical reporting to become a reliable source of information for decision-making.
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