1.BI is a framework that allows a business to transform data into information, information into knowledge, and knowledge into wisdom.
2.Monitoring results to evaluate outcomes of the business decisions is outside the scope of BI.
3.The BI architecture is composed of data, people, processes, technology, and the management of such components.
4.A data store is used by the data analyst to create the queries that access the database.
5.MDM’s main goal is to provide a partial and segmented definition of all data within an organization.
6.SAP is a portal vendor.
7. To tie a KPI to the strategic master plan of an organization, a KPI will be compared to a desired goal within a specific time frame.
8.Operational data and decision support data serve the same purpose.
9.Decision support data are a snapshot of the operational data at a given point in time
10.Queries against operational data typically are broad in scope and high in complexity.
11.The decision support database query optimizer must be enhanced to support non-normalized structures.
12.Data warehouse data are organized and summarized by table, such as CUSTOMER and ADDRESS.
13.Once data enter the data warehouse, they are never removed.
14.Creating a data warehouse is a simple exercise that takes little time, money, or effort.
15.The data warehouse development life cycle differs from classical systems development.
16.Facts can be computed or derived at run time.
17.The data warehouse designer must define common business dimensions that will be used by the data analyst to narrow a search, group information, or describe attributes.
18.Data warehouses usually have one fact table
19.Normalizing fact tables improves data access performance and saves data storage space.
20.Periodicity, usually expressed as current year only, previous years, or all years, provides information about the time span of the data stored in the table.
21.It is a relatively easy task to provide a precise list of characteristics of data-mining tools.
22.In the prognosis phase, the data-mining findings are used to predict future behavior and forecast business outcomes
23.Multidimensional data analysis techniques include advanced computational functions.
24.The mainframe environment enables an OLAP system to be divided into several components that define its architecture.
25.In most implementations, the data warehouse and OLAP are stand-alone, independent environments.
26.To provide better performance, some OLAP systems merge the data warehouse and data mart approaches by storing small extracts of the data warehouse at end-user workstations.
27.The star schema is designed to optimize data query operations rather than data update operations.
28.MOLAP is a logical choice for companies that already use relational databases for their operational data.
29.Because a data cube’s dimensions are predefined, all cells are populated.
30.ROLAP and MOLAP vendors are working toward the integration of their respective solutions within a unified decision support framework.
