CA数据仓库PPT课件

1、Data Warehousing In The Mobile Telecommunication Industries Terry Yeo Practice Director Wong Bak Wei Solution Architect What is Data Warehousing Basic Situation nBusinesses need more information faster to remain competitive. nBusinesses have lots of data, but little information. nTechnology is catch

2、ing up to the demand. Business Drivers w Increased Competition w Faster Business Cycles w Mergers 19.1%Annual Growth w IDC Survey: Average ROI = 401%! w but 1 in 2 are Failing Why are People Failing? w Not Business-Driven w Not Partnering With the Business w Wrong Expectations Set Up Front w Taking

3、Short-Sighted Approaches w “Build It and They Will Come” Data Mart Only Warehouses wIndependent Data Marts Seems Faster, Cheaper No common architecture; No shared reference data Once built, difficult to integrate wVs. Dependent and/or Architected Data Marts External Data Systems Data Warehousing Mar

4、ket Themes wAvoiding Failure 1 in 2 are failing High Risk wReturn on Investment Measuring Business Value Data Warehouses vs. Data Marts wData Warehouse Management Evolve with, not after, the business does. Support growing number of users, in more places, with more data! Session Agenda w Data Warehou

5、sing today w 8 Reasons of Failures to Avoid ! w 10 Critical Success Factors w How to measure success w Summary Data Warehousing Today w Many flavors of DW has been implemented (in Asia as well) Enterprise end-to-end DW Metadata Management Subject Area Data Marts Reporting OLAP EIS w ERP users implem

6、enting DW w More emphasis of Web-enablement w However . 1 of 2 DW projects fails ! 8 Reasons of Failure w Success is hard to measureFailure is easy ! w Reasons why DW projects fail No more funding Bad data quality Users unhappy with query tools Only a small percentage of users use the DW Poor perfor

7、mance Inability to expand Data is not integrated ETL process does not fit batch window Critical Success Factors w Common Data Definitions Consolidate different sets of departmental definitions (Extremely difficult .) These definitions are rarely documented ! Each project should have a glossary of bu

8、siness terms to support the project Legacy Applications VSAMIDMS IMSCICS COBOL Multimedia Documents Packaged Applications Groupware Databases Critical Success Factors w Well-defined transformation rules Data from source systems will be transformed in one way or another. Data will always be specifica

9、lly selected, recorded, summarized and integrated with other data Transformation rules are critical to do this correctly Critical Success Factors w Properly trained users Regardless of how easy to use a tool is, users must be trained Training should be geared to the level of user and the way they us

10、e the data warehouse Types of training how to use the tools how to use any custom developed applications availability of predefined queries & reports the data itself, and data structures for more powerful users Critical Success Factors wExpectations communicated to users Performance. Availability of

11、 the data warehouse. Functions and what data is accessible, what predefined queries and reports are available, the level of detail data and how data is integrated and aggregated. The expectations of simplicity and ease-of-use. The expectations of accuracy in both data cleanliness and what the data m

12、eans. Timeliness of when data will be available, and frequency of refreshing the data. Schedule expectations (system delivery). Where support comes from. Critical Success Factors w Ensured user involvement Solicit requirements input from users. Have the users involved throughout the project. Best sc

13、enario - Business and Technical users Critical Success Factors w The project has a good sponsor The best sponsor is from the business side, not IT. Should be willing to provide ample budget. Should be able to get resources needed for the project. Should be accepting problems as they occur, and not u

14、se them as an excuse to kill the project. Should be in serious need of DW capabilities to solve a problem, or gain some advantage. Critical Success Factors w The team has the right skill set Resources with the right skill sets should be dedicated to the team. Critical roles should report directly to

15、 the PM Critical Success Factors w The schedule is realistic Unrealistic schedule - most common cause of failure. Project schedules should be imposed with the concurrence of the PM and team members. Schedules must include task and effort required. Critical Success Factors w Proper project control pr

16、ocedures (change control) The scope will always change. Changes in the project must be managed and controlled. Critical Success Factors w The right tools must be chosen Decide on the right categories of tools Tools must match requirements of the organization, users and project. Tools must work toget

17、her without the need to build interfaces or special code. EIS Data Warehouse OLAP Reporting How to measure success w Functional quality Do the capabilities of the data warehouse satisfy the user requirements? Does the data warehouse provide the information necessary for the users to do their job? Ho

18、w to measure success w Data quality Ask the users if their reports are accurate Maintain a scorecard on the quality of the data. How to measure success w Computer performance Query response time Report response time Time to load/update/refresh the data warehouse Machine resources How to measure succ

19、ess w User satisfaction Is the DW solving their business problem? Does the DW make their jobs easier? Do they access the DW often to obtain information? Are they asking for more information to be put into the DW? Data Warehouse Applications w Is a process not a one-time project. w Is business driven

20、 not product/technology. w Effort 80-20 : 80% back-end, 20% front-end. w Implementation required not buy-and-install ! w Is different for every organization. w Must be expandable, and more importantly, avoid repetition. w Should be built, using integrated technology and tools. Summary w More organiz

21、ation realize the importance of DW. w DW is becoming part of the Internet. w Avoid the known failures before even thinking on how to achieve success. w Success is not always measured by numbers w DW is a solution which must consist of integrated tools, and services. Our Approach and Offerings Our Ap

22、proach Enterprise Data Warehouse Data Transformation Information Access Analyst, Executive, Marketing Trend Analysis, etc. Scalability over Time Fast Return Mining OLAP EIS wData Warehousing is a processnot a project. wEnsures ability to: Address both known and ad hoc information needs Dynamically r

23、espond to changing business environment Support iterative development - incremental benefit for incremental cost Process-Oriented Approach Architecture-Based Approach wDont have to choose between doing it right and doing it now wBlueprint for long-term evolution wMinimize risk Departmental Efforts C

24、hanging Needs Query and Reporting Tools REPOSITORY Operational Databases Catalogs Data Warehouse Directory Warehouse Tools Transformation Rules Applications Dictionary CASE Tools Encyclopedia Tools Parameters Update Dialog Custom Dialog Business User Metadata Repository Approach Data Warehouse Offer

25、ings wData Warehouse Planning wData Architecture Definition wData Warehouse Construction wData Mart Construction wData Warehouse Management wData Warehousing Tool Support & Training DecisionBase InfoBeacon InfoReports Forest & Trees Summary of CAs Key Strength wExperience-based Methodology - A reusa

26、ble methodology wEnd to End - IT to Business Understanding wEnd to End Solution - Building Ground to Roof wFast Return on Investment wScalability, Expandability & Maintainability wOpenness of Solution No Compromise Data Warehouse Solution DecisionBase Transformation & Movement Issues w Finding the r

27、ight data to satisfy end user needs Designing the warehouse/mart w Moving the right data into the warehouse/mart Building the population jobs w Scheduling & monitoring transformation and movement w Providing visual access to the transformation and movement process w Linking transformation and moveme

28、nt metadata with all other metadata activity The Problems with Hand Coding wWriting COBOL to access multiple data sources as a coherent whole wImplementing a solution before user community needs change wGaining confidence of users by providing the right data from the right sources wNo easy method fo

29、r ensuring data consistency over time wHuge effort to change target DBMS The DecisionBase Solution Repository Simple Point & Click Development Complex Transformations Made Easy + Unmatched data access from a variety of data sources, including ERP systems such as SAP R/3. Seamless integration with Re

30、pository The DecisionBase Solution w Generates code automatically w Combines data from multiple relational and non-relational sources w Defines mappings quickly and graphically w Records transformations in Repository for future reference w Ensures data consistency through Repository technology w Lev

31、erages ERwin data models InfoBeacon InfoReports Forest &Trees Business Objects Brio * Cognos * DecisionBase Technology ERwin Data Design Mapper Workflow Metadata Browser Data Acquisition SAP/R3 Flat Files ODBC VSAM SybaseSybaseSybase InformixInformixInformixInformix OracleOracle ODBCODBC DB2DB2 IMSI

32、MS MicrosoftMicrosoft DATA SOURCES Data Warehouse Data Mart DecisionBase Tools DecisionBase Metadata Data Sources & Targets w Oracle w Sybase w Microsoft SQL Server w DB2/MVS and DB2 UDB w Informix w Flat files (both EBCDIC and ASCII) w SAP R/3 (source only) w AS/400 w Various others via ODBC Graphi

33、cal Mapping DecisionBase Workflow End User Tools w Forest & Trees w InfoBeacon w InfoReposrts w Business Objects* w Cognos* w Brio* Single, integrated web browsermetadata, queries, Word documents all in one place DecisionBase Metadata Execute Queries DecisionBase Architecture TARGET WAREHOUSE SAP/R3

34、 Informix METADATA SOURCES Data Movement Server Metadata Manager Mapper Scan Metadata Map Transformations & Store Metadata Generate Movement Program Move Data From Source Databases to Target ODBC Sybase Informix Oracle ODBC DB2 Microsoft Development EnvironmentRuntime Environment SOURCE DATA SAP/R3

35、ODBC Sybase Informix Oracle ODBC DB2 Microsoft Flat Files Workflow Flat Files Informix ODBC VSAM Sybase Oracle ODBC DB2 IMS Microsoft Competitive Advantages w Single vendor solution Data design: Erwin Data movement: DecisionBase Metadata: DecisionBase (based on the Microsoft Repository standard) Dat

36、a access: Forest & Trees, InfoBeacon, w Integration with third party tools Business Objects, Brio, Cognos Q & A The Business Values Challenges Challenges Why The Interest In Data ? Why The Interest In Data ? The Information Warrior What Does Data Warehouse Offer ? What Does Data Warehouse Offer ? Wh

37、at Can You Do With The Information Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis Data Warehouse Analysis -

38、 Our Solution Decision Support System Data Warehouse At Work TELCO IT Architecture Warehouse Conceptual Model - The Architecture Decision Support Application - Customer Profiling Decision Support Application - Customer Profiling Decision Support Application - Customer Profiling Decision Support Appli