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Operations,BI, and Analytics

Stefano Grazioli

Critical Thinking

Doing well Easy meter

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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Using the SmallBank DB

for BusinessOperations, BI & Analytics

Data Model: SmallBank,Ltd.Loan

officer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_id

Legend

“zero/none” “one” “many”

Enrolling a New Customer

Loanofficer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_id

Selling an I.P. to a Customer

Loanofficer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_id

Changing an AddressLoan

officer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_id

Granting a New LoanLoan

officer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_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 ExposureLoan

officer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_id

Finding our Top Three Customers

Loanofficer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_id

Finding the Average Interest Rate by City

Loanofficer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_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

Loanofficer

Loan

InsurancePlan

Customer

Customer inLoan

LO idf namel namephone

L idprincipal

ratedate due

LO_id

C idf namel namecitystate

C_idcoverag

epremiu

m

C_idL_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 2003 + sg edits

Transactional (Ops)Right now, individual, action

Informational (BI/Analytics)Historical, aggregate, decision

OPERATIONAL ENVIRONMENT

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In Technology?

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