- 1. Free One Day Business Intelligence Workshopfor IT Professionals (Together we can fight recession by helping each other in the community) May 09 2009 (9.30 AM 3.30 PM) Welcome
- Welcome and Introductions ( 9.30 - 9.45 AM)
- Information Age Demo (9.45 - 10.00 AM)
- Introduction Data/Information Lifecycle Management & Business Intelligence Concepts Praveen Moturu (10.00 10.30 AM)
- Introduction to Data Warehouse Lifecycle and Concepts -Sri Tripurani (10.30 - 11.00 AM)
- Business Intelligence - ETL and BO Technical Overview and Case Study Demo - Sashi- ( 11.00 - 12.30 PM)
- Business Intelligence - Reporting - Cognos Technical Overview and Case Study Demo -Srikanth (1.30 - 3.00 PM)
- Job Counseling and Expert Advise -3.00 to 4.00 PM (Shree, Raj Kavuru, Subba Rao Inampudi, Srikanth & Sashi)
3. Welcome and Introductions 4. SME Contacts
- Sri Tripurani (Data Warehousing, ETL, ODS)
- Brokerage Data Management - ODS Team
- Email - firstname.lastname@example.org
- Srikanth Danda (Cognos 8.x)
Gayatri Kalluri (Data Warehousing/BI) 630.205.3069 Sashi Palavalla (Business Objects & Informatica) Cell: (924) 635 9885 [email_address] 5. Chicago Telugu Association (CTA)
- About Chicago Telugu Association (CTA)
- A non-profit organization primarily focused to provide community service for Telugu Community in and around Chicago Land.
- Some of the programs and services CTA plans to organize and promote include:
- Skills Development & Training, Leadership Development Sessions, Immigration Help
- Health Awareness including Yoga Sessions & Physical Education, Spirituality Activities
- Medical Camps, Medical Camps and Screening
- Employment Help, Student Support Services, Job Counseling, Support Services for Students
- Urgent Need - Provide possible help during special situations by bringing community together in responding to the needs of people.
- Helpline Services -Toll Free Number
- Telugu Festival objective is to bring telugu people in and around Chicago
- Telugu Festival July 2 ndEvening (keeravani Musical Night) and July 3 rdDay and Evening
- Programs (Local Talent followed by Tollywood Star Show Night)
- The main objective of this conference is to bring the telugu community in and around Chicago together
- for fun and entertainment and raise funds for CTA to provide services to the Telugu Community in and
6. Information Age Demo Role of Information Future State 7. Introduction Data/Information Lifecycle Management &Business Intelligence ConceptsPraveen Moturu (10.00 10.30 AM) 8. Information/Data
- Recording of events, actions, facts in Numbers and Letters.
- Raw facts, stored out of context and without semantic meaning. Bits and Bytes
- Representation in terms of IT
- Data are often viewed as a lowest level of abstraction from whichinformationand
Information: Processed Data used to provide Context Data in context meaning, format, timeframe, relevance Ex: Customer Address Data/Information Format Structured Content:ex: Data adhering to a well defined schema/model Semi Structured Content:ex: MS Word, PDF, HTML, XMLUn Structured Content:ex: TIFF, GIF, IMG, etc 9. Information/Data Management Big Picture ExternalInformation InternalReporting ExternalReporting Metrics MDM Governance/Stewardship Data Access EDW ETL MetaData .) ExternalInformation** DM ODS ECM OM Applications Business Purpose Business Purpose Source Systems ExternalInformation** 10. Definitions
- Data Warehouse (DW) : An integrated, centralized, historical, relational database and the related software used to collect, cleanse, transform and load data from a variety of operational sources for reporting and analysis by business professionals.
- Data Mart:A database of aggregated and summarized historical data typically focused on a specialized subject area for reporting and analysis by business professionals. A data mart may be may beindependent , or part of a larger data warehousing environment and fed from a data warehouse ( dependent ).
- Business Intelligence (BI) : Knowledge workers (executives, managers, staff) using information to answer the questions that inform business decisions (formerly known as decision support).
- Business Intelligence Environment:The information, support, tools and technology that enables knowledge workers to find the answers they need.
- Business Intelligence Management:Providing the information, technologies & support knowledge workers need to find the answers that inform business decisions.
11. Data Management Lifecycle Activities DataAnalysis &Minning DataProfiling DataQuality DataAuditing DataMetrics DataExtraction, Transformation, Loading CREATE, READ, UPDATE, DELETE (CRUD) DataBackup, Retention, Purge,Data Modeling (Relational/Dimensional Models) (Conceptual, Logical & Physical Models) 12. What is Business Intelligence
- Business Intelligence (BI) is about getting the right information, to the right decision makers, at the right time.
- BI is an enterprise-wide platform that supports reporting, analysis and decision making.
- fact-based decision making
- single version of the truth
- BI includes reporting and analytics.
- What do I want to happen?
13. 14. Business Intelligence Applications
- OLAP Online Analytical Processing
- Data Mining (pattern identification, predictive analysis)
- What If Modeling & Forecasting
- Analytical Applications (e.g., budgeting, sales force analysis)
- Dashboards and Scorecards
- Business Performance Management
- Executive Information Systems
15. BI Concepts
- Scheduled & Adhoc Reports based on users
- Dashboards, Scorecards, Alerts, Query, & Analysis with drilldown Capability, lineage derivation capabilities
16. Information/Data Big Picture 17. ETL ETL ETL Information/Data Lifecycle End to End 18. Introduction Data Warehouse Lifecycle& ConceptsSri Tripurani (10.30 11.00 AM) 19. Data Warehouse Life Cycle
- In early 1990s Data Warehousing practice been based on assumption that,
- From a design perspective, once in production data warehouses and data marts were essentially static.
- Data warehouse change management practices were fundamentally no different than those of other kinds of production systems.
20. Data Warehouse Life cycle
- The classical system development life cycle (SDLC) does not work in the world of the DSS analyst.
- The SDLC assumes that requirements are known at the start of the design (or at least can be discovered).
- However, in the world of the DSS Analyst, requirements are usually the last thing to be discovered in the DSS development life cycle
21. Data Warehouse Life cycle
- Dimensional schema design methodology
- One of the earliest and to this day the most effective
- Interacts with the business users at business process level to design star schemas
- The population of those star schema was then largely a technical matter of matching available data elements in transactional source systems to the designed schema, creating or synthesizing data elements when they were not available natively in the systems of record.
22. Data Warehouse Life cycle Although specific vocabularies vary from organization to organization, the data warehousing industry is in agreement that the data warehouse lifecycle model is fundamentally as described in the diagram below. 23. Data Warehouse Life cycle Although specific vocabularies vary from organization to organization, the data warehousing industry is in agreement that the data warehouse lifecycle model is fundamentally as described in the diagram below. 24. Data Warehouse Life cycle
- The development, from both available data inventories and business analyst requirements and analytical needs, of presentation layer views or robust star-schema-based dimensional data models
- End-user interview cycles.
- Source system cataloguing, definition of key performance indicators and other critical business metrics.
- Mapping of decision-making processes to the underlying information needs.
- Logical and physical schema design tasks, which feed the prototyping phase of the lifecycle model quite directly.
25. Data Warehouse Life cycle
- The deployment, for a select group of opinion-makers and leading practitioners in the end-user analytical communities, of a populated, working model of a data warehouse or data mart design, suitable for actual use.
- Prototyping shifts might occur, as the design team moves back and forth between design and prototype.
- As the gap between stated needs and actual needs closes over the course of 2 or more design-prototype iterations, the purpose of the prototype shifts toward diplomacy gaining commitment to the project at hand from opinion leaders.
26. Data Warehouse Life cycle
- The formalization of a user-approved prototype for actual production use, including the development of documentation, training, operations and management processes and the host of activities traditionally associated with enterprise IT system deployment .
- Typically involves at least two environments.
- Deployment into Non-Production environment (Test)
- Deployment into Production environment
- Production support and End user documentation is completed.
27. Data Warehouse Life cycle
- The day-to-day maintenance of the data warehouse or data mart, the data delivery services and client tools that provide business analysts with their access to data warehouse and data mart data
- Management of ETL process.
- From batch processing to real-time data processing
- Keep Data Warehouse current with respect to source system
28. Data Warehouse Life cycle
- The modification of logical schema designs in response to changing business requirements, operations and management processes (ETL and Scheduling), and physical technological components.
- Externalbusinessconditions change discontinuously, or organizations themselves undergo discontinuous changes (as in the case of asset sales, mergers and acquisitions)
- Enhancement moves seamlessly back into fundamental design.
29. Data Warehouse Life cycle
- Data becomes active as soon as it is of interest to an organization.
- Data life cycle begins with a business need foracquiring data.
- Active data are referencedon a regular basis during day-to-day business operations.
- Over time, this data loses itsimportance and is accessed less often, gradually losing its business value, and ending with its archival or disposal.
30. Data Warehouse Life cycle 31. Data Warehouse Life cycle
- Active data is of business use to an organization.
- The ease of access for business users to active data is an absolute necessity in order to run an efficient business.
- The simple, but critical principle, that all data moves through life-cycle stages is key to improving data management .
32. Data Warehouse Life cycle
- By understanding how data is used and how long it must be retained, companies can develop a strategy to map usage patterns to the optimal storage media, thereby minimizing the total cost of storing data over its life cycle.
- The ideal solution is to manage data stored in relational databases as part of an overall enterprise data management solution.
33. Data Warehouse Life cycle
- Data are put out to pasture once they are no longer active. i.e. there are no longer needed for critical business tasks or analysis.
- Prior to the mid-nineties, most enterprises achieved datain Microfilms and tape back-ups.
- There are now technologies for data archival such asStorage Area Networks (SAN), Network Attached Storage (NAS ) andHierarchical Storage Management
34. Business IntelligenceETL and Business ObjectsTechnical Overview and Case Study Sashi Palavalla ( 11.00 - 12.30 PM) 35. What is Business Intelligence?
- Business intelligence is a way of exploring data to improve business performance, whether to drive profitability or to manage costs.
- Business intelligence often aims to support better business decision-making, so BI system can be called as Decision Support System (DSS)
36. What is Data Warehouse
- Data warehouse is a repository of an organization's electronically stored data.
- Data warehouses are designed to facilitate reporting and analysis
- Data warehouse was the biggest enabler for successful BI implementation
- Data warehouse extracts information from the transactional/ERP systems and aggregates it to allow for fast analysis of vast amounts of data
37. 38. Difference b/w transaction & DW Read only; tuned for fast queries Fast inputs, but slow queries Denormalized star and snowflake schemas with fewer tables Normalized tables in thousands (3NF) Larger amount of history allow multiyear trend analysis Current Information with very little history Goal is to provide access to information Goal to process the day to day transactional data Data Warehouse / Data Mart ERP/Transaction System 39. Informatica Power Center Data Integration/ETL Tool 40. Informatica Power Center
- Power Center is an ETL/Data Integration tool
- Power Center utilizes mappings to perform ETL
41. Power Center Sources
- PowerCenter accesses the following sources:
- Relational:Oracle, Sybase ASE, Informix, IBM DB2, Microsoft SQL Server, andTeradata
- File:Fixed and delimited flat file, COBOL file, XML file, and web log
- Application:Hyperion Essbase, WebSphere MQ, IBM DB2 OLAP Server, JMS, Microsoft Message Queue, PeopleSoft, SAP NetWeaver, SAS, Siebel, TIBCO, and webMethods
- Mainframe:Mainframe databases such as Adabas, Datacom, IBM DB2 OS/390, IBM DB2 OS/400, IDMS, IDMSX, IMS, and VSAM.
- Other:Microsoft Excel, Microsoft Access, and external web services.
42. Power Center Targets
- PowerCenter can load data into the following targets:
- Relational:Oracle, Sybase ASE, Sybase IQ, Informix, IBM DB2, Microsoft SQL Server, and Teradata.
- File:Fixed and delimited flat file and XML.
- Application:You can purchase additional PowerExchange products to load data into business sources such as Hyperion Essbase, WebSphere MQ, IBM DB2 OLAP Server, JMS, Microsoft Message Queue, mySAP, PeopleSoft EPM, SAP BW, SAS, Siebel, TIBCO, and webMethods.
- Mainframe:You can purchase PowerExchange to load data into mainframe databases such as IBM DB2 for z/OS, IMS, and VSAM.
- Other:Microsoft Access and external web services.