Managing Knowledgein

Business Intelligence Systems

Dr. Jan Mrazek

Market Conditions

Customer Opens an Account

Customer Transacts

Relationship Mapping

Profitability Calculation Business Performance AnalysisCustomer Segmentation

Customer Relationship Analysis

Cross/UP Sell

Modeling Behavior

Prospects

Model Scoring

Channels and Organization

Our mission is to optimize the business process(CVM, BPM)

Mech

Information Warehouse

IPS NCCS

BI M

eta

data

Rep

osi

tory

Un

iform

BI Tech

nic

al A

rch

itect

ure

Uniform BI Data Architecture

Divisional LeadersPOS Mortgages MBANX Direct

HRInvestment Products

Retail & Commercial

MIND

Exploratory Data MartCustomer based flat file with more than 1,000

variablesSample of 1.5 mil. customers

CKDB

Query Server

Web Server

CVM

Analytical Database

(taking the role of a customer centric

marketing database)

CustomerSegmentation

Exploratory Data Mart

TreatmentSelection

TreatmentAuthoring

Decisionabout offers

Feedback

Assessment(Analysis)

Model Development

Scoring

CRM DatabaseContact Management

Models(PMML)

Sampleset of

variables

Cust. Serv.Profile

Feedbackdata

OCIF

Customering

Householding

DW + DMs CCAPSRaw Data

Primary sources (operational systems)

CRM Front End System

OCIF & Householding System

DW + Profitability System = CVM Base

CVM Core Analytical System

CVM Exploratory System (Advanced Analytics)

Account Profitability

Customer Aggregations

Household Aggregations

VariablesValue Creation

Acc/C

ust/H

H Key

sRaw account level data in monthly

aggregates

Campaign ManagementTransactional

ODS(Holds only “special” transactions)

Detailed transactions

in a daily batch load

ODS System (“Special” transactions)

Event driven filter of transactions right

during the load

Legend:Data Warehousing/Business Intelligence Environment

Monthly run on all custom

ersD

aily re-run for customers w

ith “special” transactions

OCIF SystemOperational Systems

Offer Selection

CVM Architecture

Key objective

At the Bank of Montreal one of our key objectives is to excel in our service to our customers.

To be able to achieve this key objective, we have to learn how to anticipate our customers’ preferences in a timely manner.

Since only a timely understanding can deliver true service excellence, we are focussed on streamlining knowledge discovery processes along an integrated system architecture so, that the time needed from knowledge discovery to knowledge application is minimized.

Overview of the Knowledge Discovery Process

DataAcquisition

DataPreparation

ModelDevelopment

ModelExecution(Scoring)

ScoresDeployment

ResultsAnalysis

Identificationof

Objectives

data data data data

Data Warehouse

•Data preparation•Model development•?Model execution (Scoring)•? Scores deployment•? Results analysis

Knowledge Discovery Executed in a Non-integrated Environment

DB2 UDB EEE

DM technology A DM technology B DM technology C DM technology D

Disadvantages of the Non-integrated Knowledge Discovery Environment

•Data preparation responsibility of analysts/modelers•Not optimal HW/SW for data preparation•Data about all customers need to be moved to place of model execution•Limited capabilities for model execution in the DW environment•Scores not automatically stored in systems with general availability and access•Limited ability to analyze results, quality of models

•That all results in lost of precious time to apply the discovered knowledge

ExploratoryData Mart

data datadata

data

Data Warehouse

•Model development

IM Scoring

model (PMML)

data

scores

•Data preparation •Model execution (Scoring)

Knowledge Discovery Executed in a Highly Integrated Environment

DB2 UDB EEE

(Large sampleof data)

DM technology A DM technology B DM technology C DM technology D

•Model validation and results analysis

•Mass scores deployment

model (PMML)

model (PMML)

model (PMML)

Advantages of the Integrated Knowledge Discovery Environment

•Data preparation executed by DW transformation professionals•Robust DW HW/SW utilized for data preparation•Modelers concentrate on actual model development•Only samples of data moved to modelers’ environments•Models delivered to IM Scoring in PMML format from different data mining technologies•IM Scoring executes models utilizing all robust DW HW/SW processing power•Scores immediately stored in the DW environment where they can be accessed and used by many applications and users•Full ability to analyze results, quality of models

•That all results in:•Reduction of time needed for knowledge discovery and knowledge deployment•Optimal use of HW/SW and professional resources•Improved process quality

Maintaining Model Version Control - DM Metadata

> Model built when, by whom> What tool, algorithm> Variables (links to Metadata repository)> Variables’ transformation rule - link to ETL Metadata> When last time re-balanced, by whom> Since when in production> Who is the owner, contact> QA of PMML translation, who > Treat as slow moving dimension

2001 Best Practices In Data Warehousing Award (TDWI)

2000 Best Data Warehouse Award (RealWare Awards)

2000 ADT 2000 Software Innovator Award for Data Warehousing(Application Development Trends)

1999 DCI Excellence in Business Information Award

Where you can meet me

•August 15 in Anaheim, California on TDWI World Conference Summer 2001 and Best Practices Summit•IBM Webcast on Enhancing CRM with IBM's DB2 Intelligent Miner Scoring http://webevents.broadcast.com/ibm/datamining/home.asp•Adastra Prague: call +420-2-7173 3303 to arrange for a meeting