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The Data Warehouse Toolkit, 3 rd Edition CH.10 Financial Services UOS.DML. KIM JEONG RAE 2015.10.23.

The Data Warehouse Toolkit,datamining.uos.ac.kr/wp-content/uploads/2015/09/10... · Introduction u The financial services industry u A wide variety of businesses, including credit

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Page 1: The Data Warehouse Toolkit,datamining.uos.ac.kr/wp-content/uploads/2015/09/10... · Introduction u The financial services industry u A wide variety of businesses, including credit

The Data Warehouse Toolkit, 3rd Edition

CH.10 Financial Services

UOS.DML.KIM JEONG RAE2015.10.23.

Page 2: The Data Warehouse Toolkit,datamining.uos.ac.kr/wp-content/uploads/2015/09/10... · Introduction u The financial services industry u A wide variety of businesses, including credit

Introduction

u The financial services industry u A wide variety of businesses, including credit card companies, brokerage firms, and mortgage providers.

u A full-service banku checking accounts, savings accounts, mortgage loans, personal loans, credit cards, and safe deposit boxes.

u In this chapter Areau Focusing on the retail bank

u This chapter begins with a very simplistic schema.

u We then explore several schema extensions, including the handling of the bank’s broad portfolio of heterogeneous products that vary significantly by line of business.

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u Banking Case Studyu The bank’s initial goal is to better analyze the bank’s accounts.

u Business users want the ability to slice and dice individual accounts,

as well as the residential household groupings to which they belong.

u One of the bank’s major objectives u To market more effectively by offering additional products to households

that already have one or more accounts with the bank.

Banking Case Study and Bus Matrix3 /17

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u Banking Bus Matrix

Banking Case Study and Bus Matrix

Business Processes

Dimensions

4 /17

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u Balance Snapshotu Dimensions : Date(Month), Account

u Process : Account Monthly Snapshot

u Problem

u 각 계좌는한 개의 Household(세대)와Branch(지점), Product(상품)과관련하여구축되었음

u 거대한 Account Dimension에서Product(상품)과 Branch(지점)같은추가적인 분석Dimension을반영요구

Dimension Triage to Avoid Too Few Dimensions5 /17

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u 다차원모델의 Dimension 수 : 대부분 5 ~ 20 개 Dimensions 로구성

u Kinds of dimensionsu Causal dimensions : 계약, 거래, 매장 상태, 날씨등 어떤사건의 원인에대한 통찰제공 프로모션 (In

Chapter 3)

u Multiple date dimensions : 특히 Fact Table이점진적스냅샷인 경우 (In Chapter 4)

u Degenerate dimensions : 주문, 송장, 선하증권, 티켓과 같은운영 트랜잭션통제 번호 (In Chapter 3)

u Role-playing dimensions : 하나의 트랜잭션에해당 Dimension과연관된여러 Business entities를갖는 경우, 각각은 서로분리된 Dimension으로표현 (In Chapter 6)

u Status dimensions : 계정 상태와같이 약간큰 데이터내에서 트랜잭션이나월별 스냅샷의현재 상태를식별하게 함.

u An audit dimension : 데이터 연결추적성과 품질을추적 (In Chapter 6)

u Junk dimensions : 상관도높은 분류값과 플래그들의 Dimension (In Chapter 6)

Dimension Triage to Avoid Too Few Dimensions6 /17

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u Solution : Supertype Snapshot F.T.

Dimension Triage to Avoid Too Few Dimensions

활성, 휴면상태,신규계좌개설, 해지 등

상품명상품유형, 분류정보 등

경제단위와의관계세대수입집소유은퇴여부자녀등

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u Multivalued(다중값) Dimensionsu 계좌는계좌와연관된하나, 둘그이상의소유주또는고객을가질수있음.u Problem

u 고객은 계좌속성(attribute)으로 추가될 수없다.u 한계좌에한명이상의개인이연관되는것 : Dimension table의그래뉼래러티위반

u Fact Table에별도의 Dimension으로고객을 추가할 수없다.u 한계좌에한명이상의개인이연관되는것 : Fact table의그래뉼래러티위반

u Solutionu 개별 Customer Dimension과Account Dimension을 grain인 Fact Table에연결하기위해

Account와 Customer Dimension간에 Bridge Table이요구됨

Multivalued Dimensions and Weighting Factors8 /17

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u Solution : Account–to–Customer Bridge table

u 한 계좌가두 계좌소유주를 갖는다면, 브리지테이블은 두개의 rows를갖음

u Account Key와 Customer Key를갖고 Bridge table을활용하여 해결

Multivalued Dimensions and Weighting Factors9 /17

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u Weighting Factoru 계좌소유주에대해합산가능 Fact를배부하는간단한방법임

u 각계좌소유주에게가중치수치를배정하여가중치의합이정확히 1.00이되게할수있음

u 가중치는계좌소유주에대해합산가능 Fact를배부하는데쓰임

u 소유주별로모든수치 Fact를합산할수있고, 총합은정확한총금액

Multivalued Dimensions and Weighting Factors10 /17

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u Problemu 은행 계좌, 고객, 세대를설명하는 속성(attribute)들은 매우많음

u 매월신용평가등급, 외부인구통계학데이터, 고객행동, 유지, 수익성, 체납특성을식별하기위한계산된점수등

u 시간이 지남에따라 변화하는속성(attribute)들에 대응 요구됨

u Solutionu 자주 조회되고잘 변하는속성들은

여러 Mini Dimension들로분리

u 신용평가, 인구통계학 속성들은매달 갱신

Mini Dimension으로 Fact Table에 FK를추가

Mini-Dimensions Revisited

Mini Dimension

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u Problemu 너무 많은 Mini- Dimension을포함해서는 안되며, 적절한 Mini Dimension의 row 수를요구

u Solutionu 속성(attribute)의 구간 값을활용하여 Mini Dimension의 row 수를적절히 유지

u 구간 값설정

u 세분화된수익금 31,257,98달러 → 30,000달러 < 수익금액 <= 34,999달러

u 수익성점수가 1 ~ 1200 범위 → 고정된범위로표현 : 100이하, 101~150, 151~200

u 유의사항

u 데이터마이닝분석에는개별값이더효율적

u 구간값이적절한결정여부분석이요구됨

Mini-Dimensions Revisited12 /17

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u Problemu RCD(Rapidly Changing Dimension)빨리변하는괴물 디멘션 : Customer Dimension

u Account-to-Customer Bridge table이 매우커질 수있음

u Solutionu 빨리 변하는인구통계학 속성과상태속성들을 Mini Dimension으로분리 : Demographics Dimension

Adding a Mini-Dimension to a Bridge Table

Mini Dimension

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u Problem(상황 : 비즈니스사용자요구사항)u 계좌 잔액과같은 기본숫자 Fact를대상으로 구간값 리포팅을요구

u Dimension table에정의된구간값만으로만족하지않음

u Solutionu 구간 정의 Table : Band Definition Table

Dynamic Value Banding of Facts14 /17

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u Problemu 기업이 제공하는상품과 서비스의이질적인 특성들로인해 딜레마발생

u 동일 고객에게예금계좌에서 신용카드까지많은 상품을제공

u Solutionu SubType 특별한팩트 : 예금계좌 Fact Table

u SuperType과 SubType의 Dimension에서

상품대체 key(키)는동일하여야함.

Subtype Schemas for Heterogeneous Products15 /17

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u Problem(상황 : 주식시장다양한고객의요청대응위한 F.T.)u 증권사는 고가-저가-종가의 일단위 주가를저장하는 동일 Fact Table에접근한다. 그러나

각고객은 각주식의 설명속성들을 개인별로설정

u Solutionu 다양한 고객의요청에 대응하기위해, 쿼리 시하나의 Fact Table과조인하는 별도의복제한 주식

Dimension들을보유할수있음.(Hot Swappable Dimension)

u Fact Table과다양한 Dimension table간의참조무결성제약은해제

Hot Swappable Dimension16 /17

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감사합니다.

17 /17

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Reference

u http://www.databaseanswers.org/data_models/banking_data_warehouses/index.htm

u Magic quadrant