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  • Create – Data Modeling (Relational, Dimensional) – Conceptual, Logical & Physical Models

Free One Day Business Intelligence Workshop Free One Day Business Intelligence Workshop Presentation Transcript

  • Free One Day Business Intelligence Workshop for IT Professionals (Together we can fight recession by helping each other in the community) May 09 2009 (9.30 AM – 3.30 PM) Welcome
  • Agenda
    • 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)
    • 12.30 - 1.30 PM - Lunch
    • 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)
  • Welcome and Introductions View slide
  • SME Contacts
    • Sri Tripurani (Data Warehousing, ETL, ODS)
    • Wachovia Securities
    • Brokerage Data Management - ODS Team
    • Office - 312.574.5933
    • Cell - 630.803.4231
    • Email - sri.tripurani@wachovia.com
    • Pager- [email_address]
    • Srikanth Danda (Cognos 8.x)
    • Cognos BI Consultant
    • Blue Cross Blue Shield
    • Chicago, IL
    • Desk: 312-653-6591
    • Mobile: 405-473-1544
    • [email_address]
    Gayatri Kalluri (Data Warehousing/BI) 630.205.3069 Sashi Palavalla (Business Objects & Informatica) Cell: (924) 635 9885 [email_address] View slide
  • 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 nd Evening (keeravani Musical Night) and July 3 rd Day 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
    • around Chicago
  • Information Age Demo Role of Information – Future State
  • Introduction Data/Information Lifecycle Management & Business Intelligence Concepts Praveen Moturu (10.00 – 10.30 AM)
  • Information/Data
    • 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 which information and
    • Knowledge are derived.
    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, XML Un Structured Content: ex: TIFF, GIF, IMG, etc
  • Information/Data Management – Big Picture External Information Internal Reporting External Reporting Metrics MDM Governance/Stewardship Data Access EDW ETL MetaData .) External Information** DM ODS ECM OM Applications Business Purpose Business Purpose Source Systems External Information**
  • 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 be independent , 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.
  • Data Management Lifecycle Activities Data Analysis & Minning Data Profiling Data Quality Data Auditing Data Metrics Data Extraction, Transformation, Loading CREATE, READ, UPDATE, DELETE (CRUD) Data Backup, Retention, Purge, Data Modeling (Relational/Dimensional Models) (Conceptual, Logical & Physical Models)
  • 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.
    • BI leads to:
      • fact-based decision making
      • “ single version of the truth”
    • BI includes reporting and analytics.
    • What happened?
    • What is happening?
    • Why did it happen?
    • What will happen?
    • What do I want to happen?
    • PAST – CURRENT- FUTURE
  •  
  • Business Intelligence Applications
    • Ad Hoc Query & Reporting
    • Enterprise Reporting
    • OLAP – Online Analytical Processing
      • Desktop Cubes
      • MOLAP
      • ROLAP
    • Statistical Analysis
    • 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
  • BI Concepts
    • BI Types
      • Scheduled & Adhoc Reports based on users
      • Dashboards, Scorecards, Alerts, Query, & Analysis with drilldown Capability, lineage derivation capabilities
  • Information/Data Big Picture
  • ETL ETL ETL Information/Data Lifecycle End to End
  • Introduction Data Warehouse Lifecycle & Concepts Sri Tripurani (10.30 – 11.00 AM)
  • 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.
  • Data Warehouse Life cycle
    • According to W.H. Inmon
      • 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
  • 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.
  • 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.
  • 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.
  • Data Warehouse Life cycle
    • Design phase
    • 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
    • Key activities
    • 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.
  • Data Warehouse Life cycle
    • Prototype phase
    • 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.
    • Key activities
    • 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.
  • Data Warehouse Life cycle
    • Deploy phase
    • 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 .
    • Key activities
    • Typically involves at least two environments.
    • Deployment into Non-Production environment (Test)
    • Deployment into Production environment
    • Production support and End user documentation is completed.
    • End-user training
  • Data Warehouse Life cycle
    • Operation phase
    • 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
    • Key activities
    • Management of ETL process.
    • From batch processing to real-time data processing
    • Keep Data Warehouse current with respect to source system
  • Data Warehouse Life cycle
    • Enhancement phase
    • The modification of logical schema designs in response to changing business requirements, operations and management processes (ETL and Scheduling), and physical technological components.
    • Key activities
    • External business conditions 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.
  • Data Warehouse Life cycle
    • Data Life Cycle
    • Data becomes active as soon as it is of interest to an organization.
    • Data life cycle begins with a business need for acquiring data.
    • Active data are referenced on a regular basis during day-to-day business operations.
    • Over time, this data loses its importance and is accessed less often, gradually losing its business value, and ending with its archival or disposal.
  • Data Warehouse Life cycle
  • Data Warehouse Life cycle
    • Active Data
    • 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 .
  • Data Warehouse Life cycle
    • Active Data
    • 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.
  • Data Warehouse Life cycle
    • Inactive Data
    • 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 data in Microfilms and tape back-ups.
    • There are now technologies for data archival such as Storage Area Networks (SAN), Network Attached Storage (NAS ) and Hierarchical Storage Management
  • Business Intelligence ETL and Business Objects Technical Overview and Case Study Sashi Palavalla ( 11.00 - 12.30 PM)
  • 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)
  • 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
  •  
  • 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
  • Informatica Power Center Data Integration/ETL Tool
  • Informatica Power Center
    • Power Center is an ETL/Data Integration tool
    • ‘E’  Extraction
    • ‘T’  Transformation
    • ‘L’  Load
    • Power Center utilizes mappings to perform ETL
  • Power Center Sources
    • PowerCenter accesses the following sources:
    • Relational: Oracle, Sybase ASE, Informix, IBM DB2, Microsoft SQL Server, and Teradata
    • 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, IDMS‑X, IMS, and VSAM.
    • Other: Microsoft Excel, Microsoft Access, and external web services.
  • 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.
  • Transformations in Informatica
    • Source qualifier 
    • Aggregate  
    • Sequence generator
    • Sorter
    • Router
    • Filter
    • Expression
    • Stored Procedure
    • Lookup
    • Update strategy 
    • Joiner
    • Normalizer
    • HTTP Transformation
    • XML Parser
    • XML Generator
    • Java Transformation
  •  
  • SAP Business objects Business Intelligence Reporting Tool
  • What is Business Objects ?
    • BO is an integrated query, reporting and analysis solution
    • Allows you to access the data in your corporate data bases
    • Present and analyze this information in a BUSINESS OBJECTS reports
  • Why Business Objects popular?
    • 16 years ago Business Objects came up with a simple but profound idea: “semantic layer” or “Universe”
  • BO Universe
    • Universe is a business representation of your data warehouse or transaction database
    • It shields users from the underlying complexities of the database schema
    • It is the single greatest component which will make your implementation succeed or fail.
    • To build a successful universe, keep it simple
  •  
  •  
  • Universe Components
    • Parameters that define database connectivity and SQL options
    • Classes and objects that users see when building queries and reports
    • Tables that are pointers to the physical tables in the database
    • Joins that define the relationships between the tables
  • Classes and Objects
    • A class is a logical grouping of objects within a universe.
    • An object is a named component that maps to data or a derivation of data in the database.
    • There are 3 types of objects in BO
  • Types of Objects
    • Dimension: Parameters for analysis Dimensions typically relate to a hierarchy such as geography, product, or time. For example: Last Name and City_Id
    • Description: Provide a description of a dimension example: Month name
    • Measure: Convey numeric information which is used to quantify a dimension object. For example, Sales Revenue
  •  
  •  
  • Important Modules in Business objects
    • Designer
    • Info view
    • Webi
    • Deski
    • Central Management Console
  • Designer
    • Tool to create and manage Universes
    • This tool is for Universe developers
  • Info view
    • It is portal in BO.
    • Information consumers who want to view and refresh standard reports but who don’t need to build their own.
    • Reports can include those built in Crystal Reports, Web Intelligence, Desktop Intelligence
  • Web Intelligence (webi)
    • This is thin client to create and manage reports
    • Can create reports only from Universes
    • Business authors uses this tool to design shared reports or ad hoc queries and analyses
  • Desktop Intelligence (Deski)
    • This is thick client installed on desktop
    • Can create reports from universe, free hand sql, stored procedure, personal files
    • Power users who want to design more complex reports for disconnected access
  • Central management console
    • For creating users , user groups and privileges
  • Some other BI Tools
    • Cognos
    • MicroStrategy
    • Oracle Business Intelligence
    • Hyperion
  • Required skills to BO Developer
    • Business Objects, Crystal Reports
    • Oracle or DB2
    • SQL, PL/SQL, SQL query tunning
    • Knowledge of 3NF, Dimensional Modeling (Star schema and Snowflake schema)
    • Knowledge of Data warehousing
  • Lunch 12.30 – 1.30 PM
  • Business Intelligence & Reporting Cognos Technical Overview and Case Study Srikanth Danda (1.30 - 3.00 PM)
  • Introduction Cognos 8 is a solution designed to address the challenges of enterprise-scale reporting, analysis, scorecarding, and event notification. The Web-based Cognos 8 architecture was designed for scalability, availability, and openness. It uses platform independent, industry proven technology, such as Extensible Markup Language (XML), Simple Object Access Protocol (SOAP), and Web Services Definition Language (WSDL). For this reason, Cognos 8 can integrate with and leverage your existing technology infrastructure on multiple platforms. The Cognos 8 architecture features a consistent, zero footprint, Web-based user interface for viewing, creating, and administering reports, analyses, scorecards, and events. It has a common dispatcher and supports leading relational databases as well as OLAP (Online Analytical Processing) and dimensionally modeled relational cubes. It ensures dynamic load balancing and provides failover recovery for 24 x 7 operation. It also provides a single point of administration, as well as Web-based delegated administration. Cognos 8 is fully open to third-party products and custom development. It also integrates with Web farms and supports multilingual reporting and scorecarding.
  • Cognos Architecture Packages, cubes and Reports Frame Work Manager, Power Play Transformer, Metrics Manager Report Studio, Query Studio, Analysis Studio ETL OLTP ODS ODS ODS
  • Cognos Development Work Flow
  • Cognos Security
  • Cognos Connection Home Page
  • Frame Work Manager
  •  
  •  
  •  
  • Query Studio
  • Query Studio
  • Query Studio
  • Query Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Analysis Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Report Studio
  • Job Counseling and Expert Advise - 3.00 to 4.00 PM (Shree, Raj Kavuru, Subba Rao Inampudi, Srikanth Danda & Sashi Palavalla)
  • Useful Tips for Interviews
    • Job Finding Tips
    • Build network using LinkedIn, Facebook, etc..
    • Prepare to extend your boundaries and explore for all possible opportunities. (No opportunity is small)
    • Communicate and keep in touch with your friends and never burn relationships/bridges with current company when you leave a job.
    • Resume Tips
    • Create your resume to be not more than 3 pages.
    • First page is very important and remember your resume says a lot about you and will help you to be shortlisted.
    • Prepare to talk on all content on your resume.
    • Interview Tips
    • Make sure to carry copies of your resume to the interview.
    • Never go too early, if you arrive early do not rush to the interviewer rather wait for your time and then contact them at the mentioned time.
    • Dressing is key and dress well.
    • Remember that the current interview is not your last interview in life so be patient and settled rather than getting tensed.
    • Answer all questions with honesty and give examples related to your experience as required.
    • Also follow up after your interview with an email and thank the interviewer for the opportunity and time.
  • Thank You Please send your feedback & questions to info@chicagoteluguassociation.org Please visit our web site for more info on all future events and volunteer opportunities www.chicagoteluguassociation.org