20100430 introduction to business objects data services


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Introduction to BusinessObjects Data Services

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20100430 introduction to business objects data services

  1. 1. Introduction to SAP BusinessObjects Data Services XI 3.0
  2. 2. Disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information on this document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.
  3. 3. Agenda 1. Why Enterprise Information Management 2. Data Services: One tool for data integration and data quality management 3. Metadata Management: Impact and lineage to prove trustworthiness of your data 4. Some data services use cases in an SAP environment 5. Wrap-up
  4. 4. Trusted Information is Elusive Key Problems in Managing Data The volume of data within enterprises is exploding Application silos make it difficult for people to collaborate Users have little understanding of the quality of available data Excessive time is spent on integration rather than innovation IT is struggling to address rapidly changing business requirements
  5. 5. Poorly Managed Information Leads to Inefficiency and Risk “ 90% of upper level management feel they don’t have the necessary information for critical business Lower Profits decisions; 50% of them are afraid they are making poor decisions because of it.” ― Economist Intelligence Unit “ 50% to 70% of ERP implementations are reported as “challenged” in part to data integrity and/or data Low accuracy problems.” Productivity ― Adaptive Growth, Inc. “ © SAP 2009 90% of all businesses still do not have sufficient policies in place to meet data Compliance governance regulations.” / Page 4 ― IT Policy Compliance Group Failures
  6. 6. Challenges to Effectively Managing Information How Does This Impact IT? Top issues How do I empower all users with the information they need to make better decisions? How do I help people to work across enterprise boundaries? How do I provide applications with accurate data to drive business operations? How do I implement data governance to ensure compliance and meet regulatory requirements? How can I minimize cost and manage complexity?
  7. 7. Inconsistent Data Across Different Silos Impacts Business Results and Increases Costs Data Managed in Silos Plant Finance Sales Departments create and store their own data Data inconsistencies, redundancies, and errors impact business results and increase costs
  8. 8. Manage Information as a Strategic Asset Reduce Inefficiencies and Deliver Trusted Information Enterprise Information Management Plant Enterprise-Wide Reusable Information Finance Sales Each information asset is readily understood, available and trusted Formal information architecture to identify, share and govern all data
  9. 9. SAP Provides A Complete Approach To EIM Encompassing People, Process & Technology Enterprise Information Management Executive IT PEOPLE Sponsor Line of Data Business Steward Owners PROCESS Create Cleanse Integrate Manage Govern Archive Monitor TECHNOLOGY Content & Data Integration & Data Warehouse Master Data Information Quality Management Management Life-Cycle Management Management
  10. 10. SAP Provides Best-In-Class EIM Solutions Deliver Information That Is Complete, Accurate, and Accessible Data Integration & Quality Management: Master Data Management: SAP BusinessObjects Data Services SAP NetWeaver Master Data Management SAP BusinessObjects Data Federator SAP Master Data Governance for Financials SAP BusinessObjects Text Analysis SAP Data Maintenance by Vistex SAP BusinessObjects Data Insight SAP Data Migration services Content & Information Lifecycle Management: Enterprise Data Warehousing: SAP NetWeaver Information Lifecycle Management SAP NetWeaver Business Warehouse SAP Extended ECM by Open Text SAP NetWeaver Business Warehouse Accelerator SAP Document Access by Open Text SAP BusinessObjects Rapid Marts SAP Archiving by Open Text SAP BusinessObjects Metadata Management
  11. 11. Agenda 1. Why Enterprise Information Management 2. Data Services: One tool for data integration and data quality management 3. Metadata Management: Impact and lineage to prove trustworthiness of your data 4. Some data services use cases in an SAP environment 5. Wrap-up
  12. 12. SAP BusinessObjects Data Services Data Services is the first single tool for data integration and data quality Data Integrator XI R2 Data Services Development User Interface One Development User Interface Metadata Repository Runtime Architecture One Metadata Repository Administration and Connectors One Runtime Architecture Access Data Quality XI R2 Transform Development User Interface Improve Metadata Repository Deliver Runtime Architecture One Administration Environment Administration and Connectors One Set of Connectors
  13. 13. Data Services Architecture SAP ERP, SAP CRM, SAP Master Data Management (MDM), SAP NetWeaver BI, … SOA SAP R/3, Data Migration, SAP ERP, SAP NetWeaver BI Data Synchronization, … Data profiling Cleansing Oracle, SQL, Data DB2, etc. Real Services Time Data Query, Engine Validation Reporting, PeopleSoft, Analysis, Oracle Apps, Batch and Dashboards Siebel, etc. Data Auditing SAP NetWeaver BI Files, XML, Data Lineage Mainframe, Shared Metadata Excel, etc. Impact Analysis
  14. 14. Enterprise-Wide Data Access Support for structured and unstructured data Broad connectivity to databases, applications, legacy, file formats, and unstructured data Databases Applications Files/Transport Mainframe Unstructured Data (with partner) Oracle JD Edwards Text delimited ADABAS Any text file type DB2 Oracle Apps Text fixed width ISAM 32 languages Sybase & IQ PeopleSoft EBCDIC VSAM SQL Server Siebel XML Enscribe Informix Salesforce.com Cobol IMS/DB Teradata SAP NetWeaver Excel RMS ODBC BI HTTP Both direct and MySQL SAP R/3 JMS change data Netezza – ABAP SOAP HP NeoView – BAPI (Web Services) – IDoc
  15. 15. Data Integrator : Enterprise-class Data Integration platform Explore, transform, and move data anywhere, at any frequency Deliver trusted information Market leading data quality functionality within the data integration environment Built-in features for validating data against business rules and auditing data movement execution End-to-end metadata impact analysis and lineage Agility and ease-of-use Single easy to use development interface to build, test,and deploy Web-based administration and management Collaboration features for team development Deliver extreme ETL scalability Parallelized processing down to the transform level Grid computing for high availability and throughput Services-based architecture enabling right-time data delivery Powerful prepackaged transformations
  16. 16. Increase Value of Data Assets with Data Quality Measure and analyze data through data assessment and continuous monitoring Cleanse and enhance customer and operational data anywhere across the enterprise Match and consolidate data at multiple levels within a single pass for individuals, households, or corporations Improve and automate the delivery of direct mail and goods
  17. 17. Data Cleansing examples : Customer Data (name) Input record Output record Maggie.kline@future_electronics.com Salutation: Ms. Margaret Smith-Kline phd First name: Margaret Last name: Smith-Kline FUTURE Electronics Postname: Ph. D. 5/23/03 Match standards: Maggie, Peg, Peggy 101 6th ave Gender: Strong Female manhattan Company name: Future Electronics Address 1: 101 Avenue of the Americas ny City: New York 10012 State: NY 001124367 ZIP+4: 10013-1933 Email: maggie.kline@future_electronics.com SSN: XXX-XX-XXXX Date May 23, 2003
  18. 18. Data Cleansing examples : Product Data (Universal Data Cleanse) Input Parsed output Description Product Dimension Type Form Kallkyle screw screw Kallkyle test steel plate 20 x 35 mm plate 20x35 mm steel test wire 23.33 x 40.50 cm wire 23.33 x 40.50 cm 34 x 60 mm steel plate plate 34 x 60 mm steel steel plate 34,0 60 mm plate 34 x 60 mm steel 34.0 x 60,0 mm steel plate plate 34 x 60 mm steel 34 x 60 mm steel plate? plate 34 X 60 mm steel plate plate steel plate plate steel
  19. 19. Matching and Consolidation example Ms. Margaret Smith-Kline Ph.D. Future Electronics Consolidated record 101 Avenue of the Americas New York NY 10013-1933 Name: Ms. Margaret Smith-Kline Ph.D. maggie.kline@future_electronics.com Company name: Future Electronics Co. LLC May 23, 2003 SSN: 001-12-4367 Input records Maggie Smith Purchase date: 5/23/2003 Future Electronics Co. LLC 101 6th Ave. Address: 101 Avenue of the Americas Manhattan, NY 10012 New York, NY 10013-1933 maggie.kline@future_electronics.com Latitude: 40.722970 001-12-4367 Longitude: -74.005035 Ms. Peg Kline Fed code: 36061 Future Elect. Co. 101 6th Ave. Phone: (222) 922-9922 New York NY 10013 Email: maggie.kline@future_electronics.com 001-12-4367 (222) 922-9922 5/23/03
  20. 20. Data Services in action Cleanse Validate Deliver Access Profile
  21. 21. Agenda 1. Our vision of information management 2. Data Services: One tool for data integration and data quality management 3. Metadata Management: Impact and lineage to prove trustworthiness of your data 4. Some data services use cases in an SAP environment 5. Wrap-up
  22. 22. Data Lineage Helps Users Make Confident Decisions Where did this number come from? Data lineage provides information on how a number in your BI report is calculated during the ETL process and its origin
  23. 23. Data Lineage and Impact Analysis Data Lineage: End users are able to trace back data in their reports to the original source through all different layers and transformation and presentation steps. Impact Analysis: IT needs to know which reports and users will be affected when a change is made to one of the source systems. © SAP 2008 /
  24. 24. Metadata Management Architecture Consolidate metadata from multiple systems Integrate into one central, open metadata repository Audit impact, usage, and lineage using the Metadata Explorer Trust your metadata with added business content Consolidate Integrate Audit Custom Attributes Annotations, Metapedia Business Metadata
  25. 25. Agenda 1. Our vision of information management 2. Data Services: One tool for data integration and data quality management 3. Metadata Management: Impact and lineage to prove trustworthiness of your data 4. Some data services use cases in an SAP environment 5. Wrap-up
  26. 26. Scenario 1: Data Quality for SAP CRM Data Quality Management for SAP provides a prepackaged native integration of data quality best practices within the SAP environment using the BusinessObjects Data Services platform Enforces data discipline directly within SAP CRM or SAP ERP systems No extracts for external processing required Virtually undetectable presence that provides: Global address correction and standardization Comprehensive duplicate detection Convenient record searching Supported versions SAP CRM: Releases 4.0 and 5.0 SAP ERP Central Component: Release 5.0 and 6.0 SAP R/3 4.6C
  27. 27. Examples Review the Corrected and Fielded Information Potential Duplicates Identified!
  28. 28. Scenario 2: Load SAP NetWeaver BW SAP NetWeaver 7.0 Business Warehouse Get easy access to data stored in non- SAP sources Replace database connectivity with Reporting Layer Rapid Mart application understanding InfoSource (Architected Data Marts) Business Transformation Layer Operational Include data cleansing and validation Data Store rules before loading data Data Propagation Layer Corp. Mem- PSA Standardize, correct, and enhance DataSource Harmonization Layer ory Match and consolidate Data Acquisition Layer Validate against validation rules before loading into SAP NetWeaver BI Data Services Order Management
  29. 29. Scenario 3: Data Migration for SAP Traditional ? LEGACY SAP Steps SYSTEM(S) ERP Provision of data Upload “Let’s cross the fingers and hope it loads … But attention: Data migration is far from easy. Analysts Why? More than 80% of data migration projects run over time Lack of experience and/or over budget. Cost overruns average 30%. Time Migration project plan is unrealistic overruns average 41%. Data quality issues/lack of confidence in Bloor, 2007 the data You need to extend the consultants time for 80% of organizations … will underestimate the costs the SAP ERP project related to the data acquisition tasks by an average of 50 percent Gartner, Data Warehouse Infrastructure for Growth and It is crucial to identify the right Change partner with the right expertise for your data migration project.
  30. 30. SAP Data Migration Services End-to-End Solution for Data Migration Data migration is a critical element in the success of the ERP implementation Ensure accountability and a robust project approach to data migration from a single strategic vendor Enable data governance best practices that live on after the project Industry-leading BusinessObjects Data SAP Services platform benefits data migration and DATA MIGRATION delivers many additional benefits in information SERVICES management Framework LEGACY SAP SYSTEM(S) Templates ERP Pre-built framework and tools reduce the Methodology need for custom scripts and deliver data Tools insights more quickly and consistently Expertise Engage business users early with better The right tools combined with the access to data quality and metrics right methodology and powerful expertise – That’s SAP Data Migration Service
  31. 31. SAP Data Migration Framework Based on BusinessObjects Data Services Data Governance Visualization Project Management Summary Business users/Data Steward Metrics reports Track % quality and progress over Show detailed data exceptions time against project requiring resolution for each SAP milestones Business Objects, e.g., Customer, including reconciliation exceptions BUSINESS Data Exceptions & DQ Metrics Migration Framework Staging SAP Load: Oracle Apps, XML, IDOC, PeopleSoft, Cleanse, Harmonize, Reconcile File JDEdwards, Siebel Extract & SAP Business Object transform to Validation Library Legacy SAP IT SAP Library of DS objects to structures validate the data pre- Post load load. reconciliation XML, The metrics created from flat file this process drive the Data Governance Engine
  32. 32. Agenda 1. Our vision of information management 2. Data Services: One tool for data integration and data quality management 3. Metadata Management: Impact and lineage to prove trustworthiness of your data 4. Some data services use cases in an SAP environment 5. Wrap-up
  33. 33. 7 Key Points to Take Home Data is spread throughout systems and applications in the enterprise You need a broad connectivity to all source systems Don‘t forget the unstructured data! Make sure you understand your data well before starting any project Profiling your data will give you insight into your source data and identify potential data quality issues The quality of data will vary among source systems Standardize, cleanse, and enrich data before loading into the target applications Avoid duplicate data Find matches and consolidate to get one “golden record” Use a “data quality firewall” Define validation rules to prevent incorrect data to get loaded Deliver data in “right-time” Sometimes you need real-time services, in other cases you need to bulk load data overnight. One architecture should be able to serve all needs. Don‘t underestimate the importance of metadata Metadata gives insight in where the data comes from and how it was transformed
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