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Operations, BI, and Analytics

Operations, BI, and Analytics . Stefano Grazioli. Critical Thinking. Doing well No lab Easy meter. Using the SmallBank DB for Business. Operations, BI & Analytics . Data Model: SmallBank,Ltd. Legend “zero/none” “one” “many”. LO id f name l name phone.

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Operations, BI, and Analytics

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  1. Operations,BI, and Analytics Stefano Grazioli

  2. Critical Thinking • Doing well • No lab • Easy meter

  3. Using the SmallBank DB for Business Operations, BI & Analytics

  4. Data Model: SmallBank,Ltd. Legend “zero/none” “one” “many” LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  5. Enrolling a New Customer LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  6. Selling an I.P. to a Customer LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  7. Changing an Address LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  8. Granting a New Loan LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  9. The Previous Queries Implement Operational Transactions • Directly related to business operations • Single customer, single contract, deal, service… • “Real time” • Often INSERTs • “Small” amount of data • Large numbers of fast, “simple” queries

  10. Finding our TX Exposure LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  11. Finding our Top Three Customers LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  12. Finding the Average Interest Rate by City LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  13. The Previous Queries Generate Reports and Answer Aggregate Questions (BI) • Relate to decision making more than business operations • Aggregate customers, contracts, deals, services… • Not necessarily “Real time” • Mostly Selects • “Large” amount of data • Small number of “large”, “complex” queries

  14. Assessing the Relationship between Loan Rate and Loan Size LO id f name l name phone InsurancePlan C_id coverage premium Loanofficer L id principal rate date due LO_id C id f name l name city state Loan Customer C_idL_id Customer inLoan

  15. Analytics is more sophisticated stats (typically non-SQL) • Questions relate to decision making, more than business operations • SQL provides the input, but is not sufficient. Require additional software (SPSS, SAS, R, Data miner…) • More similar to BI queries than operational queries.

  16. BI and Analytics Queries Slow Down the Systems that Run our Businesses • Idea: create a separate copy of the data, including historical to perform analysis • The DB that contains this offline data is called a Data Warehouse (aka data mart, data hub…)

  17. BACK TO The Big Picture… Informational (BI/Analytics) Historical, aggregate, decision Transactional (Ops) Right now, individual, action Operational environment Source: TDWI Smart Companies Report 2003 + sg edits

  18. WINIT What Is NewIn Technology?

  19. Homework Demo

  20. You do the talking • Name, major • Learning objectives • Things you like about the class • Things that can be improved • Attitude towards the Tournament

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