Financial Information Management Operations, BI, and Analytics Stefano Grazioli

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Financial Information Management Using the SmallBank DB for Business Operations, BI & Analytics

Data Model: SmallBank,Ltd. Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id Legend “zero/none” “one” “many” Legend “zero/none” “one” “many”

Enrolling a New Customer Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

Selling an I.P. to a Customer Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

Changing an Address Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

Granting a New Loan Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

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

Finding our TX Exposure Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

Finding our Top Three Customers Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

Finding the Average Interest Rate by City Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

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

Assessing the Relationship between Loan Rate and Loan Size Loan officer LoanLoan Insurance Plan CustomerCustomer Customer in Loan LO id f name l name phone L id principal rate date due LO_id C id f name l name city state C_id coverage premium C_id L_id

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.

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…)

BACK TO The Big Picture… Source: TDWI Smart Companies Report sg edits Transactional (Ops) Right now, individual, action Informational (BI/Analytics) Historical, aggregate, decision O PERATIONAL ENVIRONMENT

Financial Information Management WINIT What Is New In Technology?

Financial Information Management Homework Demo