Day 02 sap_bi_overview_and_terminology


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Day 02 sap_bi_overview_and_terminology

  1. 1. Day 1 SAP BI Training
  2. 2. 2 SAP Stands for Systems Applications Products In data processing
  3. 3. 3 What is SAP • SAP stands for Systems Applications and Products in Data Processing • Name of the company SAP AG • Name of the software SAP • Founded in 1972 in Germany • World’s fourth largest software provider • World’s largest provider of ERP
  4. 4. 4 Why SAP “SAP's solution is a best-practices approach based on collective experience with thousands of Industries . It delivers a totally integrated, robust and scalable product that empowers the user, instead of creating a costly dependence on the vendor” • Simultaneous visibility across the whole enterprise • Supports databases, applications, operating systems and hardware from almost every major supplier • Integrated Modules • Extensive interfacing capabilities • Designed for all business types • Supports multiple languages and currencies • Top ERP Provider
  5. 5. 5 3 Tier Architecture
  6. 6. 6 Introduction Data Integrating Data Data Representation TableField Primary Key Object Attribute Company-Data / Department-Data Metadata
  7. 7. 7 Introduction (2) Database Transactional Database Data Warehouse Master Data Transaction Data Data Mart Data vs. Information ERP – Enterprise Resource Planning Actual Data Plan Data
  8. 8. 8 ERP – Enterprise Resource Planning An integrated system that operates in real time A common database, which supports all applications. A consistent look and feel throughout each module. It’s a Complete integrated Application modules provided by SAP to Integrate by the Information Technology (IT) Department I
  9. 9. 9 R/3 OR ECC Core Business Process
  10. 10. 10 OLTP(ECC) Vs OLAP (BW)
  11. 11. 11 OLTP(ECC) Vs OLAP (BW) OLTP System OLAP System Online Transaction Processing Online Analytical Processing (Operational System) (Data warehouse) Source of data Operational data; OLTPs are the original source of the data. Consolidation data; OLAP data comes from the various OLTP Databases Purpose of data To control and run fundamental business tasks To help with planning, problem solving, and decision support What the data Reveals a snapshot of ongoing business processes Multi-dimensional views of various kinds of business activities Inserts and Updates Short and fast inserts and updates initiated by end users Periodic long-running batch jobs refresh the data Queries Relatively standardized and simple queries Returning relatively few records Often complex queries involving aggregations Processing Speed Typically very fast Depends on the amount of data involved; batch data refreshes and complex queries may take many hours; query speed can be improved by creating indexes Space Requirements Can be relatively small if historical data is archived Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP Database Design Highly normalized with many tables Typically de-normalized with fewer tables; use of star and/or snowflake schemas Backup and Recovery Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss and legal liability Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method
  12. 12. 12 Business Intelligence Definition Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to information to help a business make better business decisions.
  13. 13. 13 Evaluation of SAP BW/BI Evaluation of SAP BI/BW Name Version Release BIW 1.2A Oct-1988 BIW 1.2B Sep-1999 BIW 2.0A Feb-2000 BIW 2.0B Jun-2000 BIW 2.1C Nov-2000 Name Change to BIW to BW BW 3.0A Oct-2011 BW 3.0B May-2002 BW 3.1 Nov-2002 BW 3.1C Apr-2004 BW 3.3 Apr-2004 BW 3.5 Apr-2004 Name Change to BW to BI BI 7 Jul-2005 Name Change to BI to BW BW 7.3 Nov-2011
  14. 14. 14 Why it came that way?
  15. 15. 15 Solution needed
  16. 16. 16 Key Capabilities
  17. 17. 17 Key Capabilities
  18. 18. 18 SAP BI Architecture
  19. 19. 19 SAP BI Key Components
  20. 20. 20 Data Warehousing & ETL (Extract, Transform & Load)
  21. 21. 21 Data Warehousing
  22. 22. 22 Operational Data Store and Data Warehouse layer
  23. 23. 23 Open Hub
  24. 24. 24 BI Suite
  25. 25. 25 For All User Types Authors and analysts ■ need advanced analysis functionality and ad-hoc data exploration capabilities ■ require useful, manageable tools Executives and knowledge workers ■ require personalized information in context via an intuitive user interface ■ want predefined analysis paths and the option of in-depth analysis of summary data. Information consumers ■ need a snapshot of a particular data set to perform their operational tasks ■ do not interact extensively with the data.
  26. 26. 26 Business Explorer
  27. 27. 27 Query, Analysis and Reporting
  28. 28. 28 Web Application Framework
  29. 29. 30 Information Broadcasting
  30. 30. 31 Authorization
  31. 31. 32 Open Analysis Interfaces
  32. 32. 33 Business Content
  33. 33. 34 Business Content Predefined, role-based and task-oriented information models ♦ Provide technical definitions, such as extraction and transformation rules ♦ Predefined templates for reporting and analysis. For various industries and business areas
  34. 34. 35 Business Content Benefits
  35. 35. 36 Road Map
  36. 36. 37 SAP Approach: ASAP Methodology ASAP(Accelerated SAP) The implementation of your SAP System covers the following phases: 1. Project Preparation 2. Business Blueprint 3. Realization 4. Final Preparation 5. Go Live & Support
  37. 37. 38 ASAP: 1. Project Preparation In this phase you plan your project and lay the foundations for successful implementation. It is at this stage that you make the strategic decisions crucial to your project: Define your project goals and objectives Clarify the scope of your implementation Define your project schedule, budget plan, and implementation sequence Establish the project organization and relevant committees and assign resources
  38. 38. 39 ASAP: 2. Business Blueprint In this phase you create a blueprint which Documents your enterprise’s requirements and establishes how your business processes and organizational structure are to be represented in the SAP System. You also refine the original project goals and objectives and revise the overall project schedule in this phase.  Define your project goals and objectives
  39. 39. 40 ASAP: 3. Realization Configure the requirements contained in the Business Blueprint.  Baseline configuration (major scope) is followed by final configuration (remaining scope) Conducting integration tests and Drawing up end user documentation.
  40. 40. 41 ASAP: 4. Final Preparation Complete your preparations, including testing, end user training,  System management, and cutover activities. Resolve all open issues in this phase. Ensure that all the prerequisites for your system to go live have been fulfilled..
  41. 41. 42 ASAP: 5. Go Live & Support Moved from a pre-production environment to the live system. Setting up production support, Monitoring system transactions, and Optimizing overall system performance.
  42. 42. 43 The new intelligence platform Value added within an SAP landscape
  43. 43. 44 Products Directions for BI solutions Richest offering for all business users
  44. 44. 45 Data Warehouse
  45. 45. 46 Basics of Star Schema Types of Data: 1. Master Data 2. Transation Data Master Data: Master data is data that remains unchanged over a long period of time. Master data contains information that is needed again and again in the same way Example: Customer ID Customer Name Customer Address Customer Phone C01 John north America 90001234 C02 Cater Uganda 90001234 C03 Robert USA 90001234 C04 Philips UK 90001234 C05 Rakul Singapore 90001234
  46. 46. 47 Basics of Star Schema Transactional Data Data relating to the day-to-day transactions is the Transaction data Customer ID Customer Name Customer Address Quantity Price C100 John USA 10 100 C200 Cater UK 20 200 C300 Cooper UAE 30 300 C400 Scot DNM 40 400
  47. 47. 48 Data Warehouse Star Schema
  48. 48. 49 Classic Star Schemas A schema is called a star schema if all dimension tables can be joined directly to the fact table. The following diagram shows a classic star schema.
  49. 49. 50 Infocube - Extended Star Schema
  50. 50. 51 Enterprise Data Warehouse Architecture Consolidating data warehouse layers that were not developed together may produce following inconsistencies Uncontrolled data flows Multiple extraction of the same data Local BI initiatives (without a global agreement) Several inconsistent data models Silos, standalone systems An unreliable corporate information basis (unreliable headquarter reporting) Overall: Redundant, expensive development
  51. 51. 52 Why EDW? All decisions made for the entire company To produce a valid and stable corporate Data Warehouse solution that satisfies all of the demands for integrated and consistently structured information. For this, it is necessary to adhere to generally accepted guidelines. The Enterprise Data Warehouse architecture reflects all of these decisions. The architecture is a "system design" decision that is valid and stable for a specified timeframe.
  52. 52. 53 Master Data / Transactional Data InfoArea  InfoObject Catalog  Application Component  InfoObject  DataSource  InfoCube  DataStore Objects (DSO)  Characteristics  Key Figures  Dimension Table  Fact Table  SID Table TERMINOLOGY - 1
  53. 53. 54 TERMINOLOGY - 2 Attributes  Text  Hierarchy  InfoProvider  Source System  Data Targets  Transformation  Data Transfer Process  BEx Suites  BEx Web  BEx Analyzer
  54. 54. 55 Summary
  55. 55. Thank You.