Decision support systems
Decision support system is a reference source of information and significantly increases the speed and quality of decision-making processes at all company levels. It is based on the proven Kimball/Inmon data warehouse architecture and modern data integration, storage, management, and presentation tools.
The decision support system includes two key initiatives: DHW and BI
Data Warehouse (DWH)
DWH is an integration project that aims to establish a core location (a centralized database or several synchronized distributed databases) where all the company's data will be stored and processed. Everyone who uses this system needs to assume it credible and authentic - a single version of the truth. DWH must enable fast and reliable data access.
A data warehouse has a stage part (a place where data is temporarily stored after being transferred from the source system), an enterprise part (a repository that contains all currently relevant company business data), and corresponding user data marts (an archive or library of data that has been collected for analysis and is intended for a specific department within the company, say, for the sales department).
Technologically speaking, a data warehouse is based on systems for parallel processing of vast amounts of data, such as VMware Greenplum or IBM Netezza. At the same time, Talend Data Management or IBM DataStage platforms are used for integration.
Business Intelligence (BI)
Business intelligence is a contemporary reporting system that has to provide explanation about what happened in business and why it happened. It must result in information and insights that bring a competitive advantage.
In the implementation and visual presentation of the business reporting system, BI dashboards and self-service BI tools take into account the best practices of controlling and IBCS standards. The tools of our choice in BI front-end implementation are MS PowerBI and IBM Cognos Analytics with Watson.
Use case example
The organization wants to raise the quality of managerial reporting by establishing a stronger connection between the general ledger's financial indicators and the business' operational levels. The company requires fast and straightforward support for top-down data analysis (from general to specific) and immediate and transparent insight into effects of decisions. Moreover, the organization wants to consolidate data from multiple sources in order to establish a complete view of the business, harmonize code books, sanitize data and get ready for the escalation of challenges related to data quality.
In addition to insight into management, the system must be able to create appropriate reports for all levels of the organization (either by using relevant dashboards or by periodically sending automated e-mail messages). Furthermore, the system must have the ability to integrate reports into operational processes and analytical applications.
