Data Warehouse
CBOSSdwh (Data Warehouse) is a hardware-software solution intended for automated collection of data from different sources, data cleansing, conversion, consolidation and storing in order to use it in CBOSS analytical systems.
Many companies face difficulties in storing, processing and analyzing corporate information. The volumes of collected data enable solving the tasks related to tactical and strategic planning. However, employment of several information systems complicates preparation of consolidated reports and practically prevents from analyzing information commonly available for divisions that collect it.
CBOSSdwh is based on two main concepts: integration of collected data in a unified warehouse and structuring of data sets by solutions: Financial management, Sales management, Loyalty management, Marketing management, Traffic management, Inventory management, Performance management.
System Operation
CBOSSdwh acts as an intelligent node of data collection from different sources. The following data sources are supported:
- B&OSS: billing systems, store subsystems
- External resource systems: switches, SMS centers
- Enterprise hardware-software solution: server equipment, software.
The collected information becomes the baseline for its further processing and provision to end-users by analytical systems: CBOSSdss (Decision Support System) and CBOSSudr (User Defined Reports System).
DWH interaction with data sources and analytical systems
Key Features
- Automatic scheduled collection, and processing of data
- Loading of data from different sources (relational databases and flat files)
- Loading of history data; storing of information over long periods
- Monitoring of data loading and conversion
- Rollback of loaded data and processing results
- Multilanguage support
- Logging
- Protection of collected and processed information from unauthorized access.
Benefits
- Reducing time expenses for collection and preliminary processing of data
- Creation of a unified enterprise management information environment
- Increasing the quality of decision-making
- Increasing the efficiency of data provision
- Minimizing human factor effect at data collection and processing
- Optimizing distribution of load to data sources.
