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Business Intelligence Training

Business Intelligence Training. Siemens Engineering Pakistan Zeeshan Shah December 07, 2009. Agenda. Introduction to the SAP Business Intelligence 1.1. Importance of BI Today 1.2. Data Warehouses 1.3 OLTP & OLAP Components of the BI System 2.1 Source Systems Modeling

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Business Intelligence Training

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  1. Business Intelligence Training Siemens Engineering Pakistan Zeeshan Shah December 07, 2009

  2. Agenda • Introduction to the SAP Business Intelligence 1.1. Importance of BI Today 1.2. Data Warehouses 1.3 OLTP & OLAP • Components of the BI System 2.1 Source Systems • Modeling 3.1 Objects in BI 3.2 Modeling • Classic Star Schema

  3. Business Intelligence • Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. • BI allows information systems to meet the following requirements required by decision makers: • Coverage of all business processes: cross-system and cross-process analyses are becoming increasingly important • High-quality decision-making support: The BI system must support the requirements of both operative and strategic management; only then is it possible to support decisions fully • Short implementation time with less resources: As well being quick to implement, a Data Warehouse must enable simple and quick access to relevant data, avoiding the labor-intensive preparation of heterogeneous data

  4. Business Intelligence • In summary, • “Business Intelligence software is the collection of applications needed to make sense of business data”.

  5. Data Warehouses • The Data Warehouse, a component of the Business Intelligence tool set, is the more specific tool responsible for the • cleanup, • loading, and • storage of the data needed by the business. • A Data Warehouse can help to organize the data. It brings together all operative DataSources (these are mostly heterogeneous and have differing degrees of detail). • A warehouse has the following properties:: • Read-only access: • Cross-organizational focus: • Data Warehouse data is stored persistently over a particular time period. • Designed for efficient query processing:

  6. Business Environments • Business environments are divided into: • OLAP (BI/Data Warehouse System ) • OLTP • Differences Between a BI/Data Warehouse System (OLAP) and an OLTP System • Level of detail: • History: Archiving data in the OLTP area means it is stored with minimal history • Changeability: Frequent data changes are a feature of the operative area,while in the Data Warehouse, the data is frozen after a certain point for analysis • Integration: In contrast to the OLTP environment, requests for comprehensive, integrated information for analysis are very high • Read access: An OLAP environment is optimized for read access. • It is most advantageous to technically separate all aggregated reporting-related demands made on the Data Warehouse from the OLTP system.

  7. Components of the SAP BI system

  8. Components of the SAP BI system • The BI database is divided into self-contained business information providers (InfoProviders). • You analyze the database of BI by defining queries against these InfoProviders in the BEx Query Designer • Data analysis based on multidimensional Data Sources (OLAP reporting) allows you to analyze more than one dimension of an InfoProvider (Time,place, and product) at the same time. • you can make any number of variance analyses (plan/actual comparison and business year comparison) • You can analyze data in the following areas in the Business Explorer • BEx Analyzer (Microsoft Excel-based analysis tool with pivot-table-like features) • BEx Web Analyzer (Web-based analysis tool with pivot-table-like features) • BEx Web Application Designer (customer-defined and SAP BI Content provided) • BEx Report Designer (highly formatted Web output)

  9. Components of the SAP BI system

  10. Components of the SAP BI system • The Data Warehouse architecture is structured in three layers: • sourcing the data, • storing It in the warehouse, and • reporting on it with analytics. • Source Systems • A source system provides the BI system with data. BI distinguishes between source systems: • mySAP Business Suite • Non-SAP systems • Flat files • Multidimensional sources from other Data Warehouses • XML: • Relational data in other database management systems • Data providers • Databases (DB Connect) or complex sources • You can send data from SAP and non-SAP sources to BI using SAP Exchange Infrastructure (SAP XI) Data transfer using SAP XI is based on (SOAP).

  11. Modeling • An InfoProvider is an object for which queries can be createdor executed in BEx. InfoProviders are physical objects or sometimes logical views that are relevant for reporting • InfoCubes are the primary objects used to support BI queries. They are designed to store summarized and aggregated data, for long periods of time. • DataStore objects are another primary physical database storage object used in BI. They are designed to store very detailed (transaction level) records.

  12. Classifying InfoObjects • InfoObjects are primarily divided into the major types • Key figures or • characteristics. • Time characteristics, • technical characteristics, and • units. • Characteristics InfoObjects are used to analyze key figures, for example, Customer (characteristic) Sales (key figure).

  13. The Classic Star Schema: The EDW Database Schema • This database schema classifies two groups of data: • Facts (sales amount or quantity, for example) and • dimension attributes (customer, material, or time, for example) • The fact data (values for the facts) is stored in a highly normalized fact table. • The values of the dimension attributes are stored from a technical perspective, in various denormalized dimension tables • In the star schema design shown, the key of the dimension tables is a machine-generated dimension key (DIM ID) that uniquely defines a combination of dimension attribute values • The DIM ID (a sequentially assigned number) is a foreign key in the fact table. In this way, all data records in the fact table can be uniquely identified.

  14. The Classic Star Schema: The EDW Database Schema

  15. The Classic Star Schema: The EDW Database Schema

  16. The Classic Star Schema: The EDW Database Schema • A InfoCube consists of precisely one fact table* in which key figure values are stored. • A fact table can contains a maximum of 233 key figures. • A InfoCube usually has a minimum of four dimension tables and a maximum of 16. Of these, 13 of the 16 are customer-created and three are the SAP-supplied dimensions: • Units dimension table • Data Package dimension table • Time dimension table • Customer dimensions contain SIDs linked to a maximum of 248 characteristics InfoObjects. • Data Package and Time dimension tables are always present in a InfoCube • Additional information about characteristics InfoObjects is referred to as master data in the BI system. A distinction is made between the following master data types: • Attributes • Texts • (External) Hierarchies

  17. Thank you!

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