The illustration below shows the typical technical components involved in a Business Intelligence implementation. This particular example is focused on a Sales & Marketing BI example, but the basic components would be similar for any Business Intelligence solution.
The illustration shown could be built using a variety of technologies, including Business Objects, Cognos, Google, Spotfire, Informatica, etc. For more information about various Business Intelligence tools please see the 'Tools' page on this site for more details.
Actionable: Actionable means that you not only have a complete understanding of what is happening, but you have a reasonable understanding of where and why it is happening. With that basis, you are in a position to take action.1
Business Intelligence (BI): The process, architecture, technologies and tools that help companies transform their data into accurate, actionable and timely information and disseminate that information across the organization. It includes, but is not limited to, data modeling, data warehousing, data marts, metadata, master data management, data cleansing, predictive analytics, reporting, analysis, alerts, dashboards and scorecards. (Similar term: decision support system or DSS)
Business Performance Management (BPM): A framework that optimizes the execution of an organization’s strategy and consists of a set of integrated processes, supported by technology (such as performance dashboards, data warehousing, analysis and reporting) that enables organizations to communicate, monitor, measure and manage performance against goals.2
Dashboards and Scorecards: Multi-layered performance management systems, built on business intelligence and data integration infrastructures, that enable organizations to measure, monitor and manage business activity using both financial and non-financial measures. Dashboards tend to monitor the performance of operational processes, whereas scoreboards tend to chart the progress of tactical and strategic goals.3
Data Warehouse (DW): An integrated, non-volatile, time-variant, collection of data organized to support the management needs of the business.4
Data Mart (DM): A subject-oriented collection of data that may be integrated, non-volatile, time-variant and/or summarized to support the organization. (W. H. Inmon)
Data Quality: The accuracy of data stored, reported and analyzed by an organization in relation to the exactness, correctness and completeness of the data:
Exactness refers to how well the data that is stored matches to the original source of that data (i.e., a customer reports its name as ABC, Inc. and it is stored as ACB)
Correctness refers to how close the data that is stored or reported matches to the business meaning of that data. (i.e., a customer master field stores the customer type of a customer as P, and a report classifies it as “pertinent” instead of “public”)
Completeness is the degree to which the necessary parts or elements of the data exist in the source system(s) (i.e., customer phone number – 800-921-103x)
Extract, Transform and Load (ETL): The process used to migrate data from a data source to a target. It often involves converting the source data into a form required for the target, which may involve filtering, sorting, joining, translating, deriving, transposing, summarizing and/or denormalizing. With regard to business intelligence (BI), it is generally used to populate an operational data store (ODS), data warehouse (DW) and/or a data mart.
Master Data Management: It is the practice of defining and maintaining consistent definitions of business entities, such as customers and products, then sharing them via integration techniques across multiple IT systems within an enterprise and sometimes externally.5 (Similar term: Customer data integration or CDI)
Operational Data Store (ODS): A subject-oriented integrated, current, volatile collection of data used to support the tactical decision-making process for the enterprise (W. H. Inmon). It can be used as a staging area for populating a data warehouse or a data mart.
Predictive Analytics: A set of business intelligence (BI) technologies that uncover relationships and patterns within large volumes of data that can be used to predict behavior and events.6 (Similar term: data mining)
Staging Area: The data warehouse staging area is a temporary location where data from source systems is copied. A staging area is mainly required in a data warehousing architecture for timing reasons. In short, all required data must be available before data can be integrated into the data warehouse.