Glossary: Keywords on the subject of Business Intelligence
Business Intelligence (BI)
Collection, analysis and presentation of all available data in a business. Coined by the Gartner Group, BI is identified as a process by which data is converted into information and further knowledge. BI today is considered to be the catch-all term for Data Warehousing, Data Mining and OLAP.
Balanced Scorecard (BSC)
Economical analysis of causes and effects for cross-divisional control of business objectives. BSC does not only include accounting numbers, but also the human aspects of the company: Customer relations, internal processes, professional development prospects for employees. These are considered forward-looking indicators and performance drivers.
Corporate Performance Management (CPM)
Method of illustrating and improving performance and profit in a business. CPM is considered an additional benefit of Business Intelligence. While in BI the analysis of history and present takes center stage, CPM also encompasses the future with planning, projection and sales promotions in mind. Synonymous terms: Business Performance Management (BPM) and Enterprise Performance Management (EPM).
Smaller, specialized Data Warehouses for specific service branches or applications. With this, individual divisions or users have access to specialized data bases. Data Marts point to parts of the total Data Warehouse.
Through different methods like for example cluster-analysis, large data bases in the Data Warehouse are automatically scanned for relevant information. The purpose is to find links which were previously not recognized.
Central “data warehouse“ for information, that helps management in reaching its decisions. All relevant business data is collected here and standardized. Thus access to information is accelerated and data is available for the subsequent analysis.
Online Analytical Processing (OLAP)
Method for analytical data evaluation. There are large overlaps with the concept of Data Warehouse. OLAP is considered to be part of the “hypotheses- based analysis methods”: The user first has a hypothesis and then checks it with OLAP. His/her hypothesis will then be either confirmed or rejected by the analytical result.