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Building a BI Solution Leveraging Analytical Reporting

Building a BI Solution Leveraging Analytical Reporting. Arunachalam T, IM Group, SETLabs, Infosys. Speaker Profile – Arunachalam. Certifications: Dr. Bill Inmon certified CIF Architect TDWI Certified Business Intelligence Professional (CBIP) DAMA Certified Data Management Professional (CDMP)

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Building a BI Solution Leveraging Analytical Reporting

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  1. Building a BI Solution Leveraging Analytical Reporting Arunachalam T, IM Group, SETLabs, Infosys.

  2. Speaker Profile – Arunachalam Certifications: • Dr. Bill Inmon certified CIF Architect • TDWI Certified Business Intelligence Professional (CBIP) • DAMA Certified Data Management Professional (CDMP) • TOGAF 8 certified Enterprise Architect Thought Leadership: • BI Consulting Methodology, B-eye-Network • IM – Key for creating Business Value, TDAN • Convergence of BI and Search, Information-Management.com

  3. Part I :- Analytics Demystified.

  4. Analytics – The Buzz Word

  5. Analytics – An Enabler Analytics is the science of analysis. - Wikipedia.

  6. Analytics vs. Analysis Analytics Analysis ? Analytics Analysis Analytics Analysis

  7. Analytics vs. Analysis • Interactive • Features Analytics

  8. Evolution of Analytics Capability

  9. Levels of Analytical Reporting

  10. Analytics – Maturity Model

  11. Part II :- Development Techniques.

  12. Components of Analytics Application

  13. Characteristics of Analytics Application

  14. Development Approaches

  15. Packaged Analytics Courtesy: Wayne W. Eckerson, Beyond Reporting, TDWI Best Practices Report, Third Quarter 2009, p 17.

  16. Analytics is part of Larger System

  17. Challenges Following are some of the challenges faced by the businesses today : • Creates a stove-piped system/ information silo • Requires rare skill sets – statistical analysts, miners and predictive modelers • Lack of standard analytical tools/ approaches across organization • Data requirements of Operational, Tactical & Strategic Analytics are complex • Resource Intensive – CPU, RAM, I/O • Data movement from operational & DW systems to ADM can hog bandwidth.

  18. Best Practices To overcome the challenges, enterprise can follow best practices including : • Perform user defined analytical functions (UDFs) at DB level/ In DB Analytics • Migrate from RDBMS to ADBMS – columnar database • Embrace 64 bit OS and MPP architecture • Adopt in-memory storage/ query processing • Leverage technological advancements such as clustering, grid computing • Familiarize yourself with analytics frameworks such as MapReduce, Hadoop • Plan for soft data/ text analytics.

  19. Q & A Questions?

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