1. Managing Knowledge in Business Intelligence Systems Dr. Jan Mrazek

2. Market Conditions Customer Opens an Account Customer Transacts Relationship Mapping Profitability Calculation Business Performance Analysis Customer 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)

3. Mech Information Warehouse IPSNCCS BI Metadata RepositoryUniform BI Technical Architecture Uniform BI Data Architecture Divisional Leaders POSMortgagesMBANX Direct HR Investment Products Retail & Commercial MIND Exploratory Data Mart Customer based flat file with more than 1,000 variables Sample of 1.5 mil. customers CKDB Query Server Web Server

4. CVM Analytical Database (taking the role of a customer centric marketing database) Customer Segmentation Exploratory Data Mart Treatment Selection Treatment Authoring Decision about offers Feedback Assessment (Analysis) Model Development Scoring CRM Database Contact Management Models (PMML) Sample set of variables Cust. Serv. Profile Feedback data OCIF Customering Householding DW + DMs CCAPS Raw 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 Variables Value Creation Acc/Cust/HH Keys Raw account level data in monthly aggregates Campaign Management Transactional 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 customers Daily re-run for customers with “special” transactions OCIF System Operational Systems Offer Selection CVM Architecture

5. 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.

6. Overview of the Knowledge Discovery Process Data Acquisition Data Preparation Model Development Model Execution (Scoring) Scores Deployment Results Analysis Identification of Objectives

7. 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 ADM technology BDM technology CDM technology D

8. 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

9. Exploratory Data Mart 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 sample of data) DM technology ADM technology BDM technology CDM technology D Model validation and results analysis Mass scores deployment model (PMML)

10. 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

11. 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

12. 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