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Managing Knowledge in Business Intelligence Systems Dr. Jan Mrazek Our mission is to optimize the business process (CVM, BPM) Relationship Mapping Channels and Organization Market Conditions Customer Transacts Modeling Behavior Profitability Calculation Customer Segmentation Business Performance Analysis Customer Relationship Analysis Customer Opens an Account Model Scoring Cross/UP Sell Prospects Query Server Retail & Commercial Divisional Leaders POS Mortgages CKDB MIND HR Investment Products MBANX Direct Exploratory Data Mart Customer based flat file with more than 1,000 variables Sample of 1.5 mil. customers Information Warehouse Mech IPS Uniform BI Data Architecture NCCS BI Metadata Repository Uniform BI Technical Architecture Web Server CVM Architecture CVM Exploratory System (Advanced Analytics) Model Development Exploratory Data Mart Sample set of variables Models (PMML) CVM Core Analytical System Scoring Customer Segmentation Decision about offers Campaign Management Treatment Selection Feedback CRM Front End System data Cust. Serv. Profile CRM Database Contact Management ODS System (“Special” transactions) CVM Analytical Database (taking the role of a customer centric marketing database) Transactional ODS (Holds only “special” transactions) Event driven filter of transactions right during the load OCIF & Householding System Customering OCIF Feedback Assessment (Analysis) Householding Raw account level data in monthly aggregates Primary sources (operational systems) Variables Value Creation DW + Profitability System = CVM Base Account Profitability Customer Aggregations Household Aggregations DW + DMs Raw Data CCAPS Detailed transactions in a daily batch load Treatment Authoring Monthly run on all customers Daily re-run for customers with “special” transactions Offer Selection Legend: Data Warehousing/Business Intelligence Environment OCIF System Operational Systems 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 Identification of Objectives Data Acquisition Data Preparation Model Development Model Execution (Scoring) Scores Deployment Results Analysis Knowledge Discovery Executed in a Non-integrated Environment DM technology A DM technology B data data DM technology C data DB2 UDB EEE Data Warehouse data DM technology D •Data preparation •Model development •?Model execution (Scoring) •? Scores deployment •? Results analysis 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 Knowledge Discovery Executed in a Highly Integrated Environment DM technology A DM technology B DM technology C DM technology D •Model development model (PMML) model (PMML) model (PMML) data data data •Data preparation model (PMML) •Model execution (Scoring) IM Scoring Exploratory Data Mart (Large sample of data) data scores data •Model validation and results analysis •Mass scores deployment DB2 UDB EEE Data Warehouse 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 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 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