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Executive Education
Clariden Leadership Institute
Data Mining (Singapore)
Richard Boire
Boire Analytics
Richard Boire, Boire Analytics


  • CMA Board Chair 2009-2013:

Customer Insights and Analytics Council (Canadian Marketing Association)

  • Program Advisory Committee Member

- University of Toronto Continuing Education
- Seneca College - Toronto
- Centennial College - Toronto
- George Brown College - Toronto

  • Author of Data Mining for Managers: How to Use Data (Big and Small) to Solve Business Problems, published by Palgrave Macmillan
Richard’s initial experience at organizations such as Reader’s Digest and American Express allowed him to become a pioneer in the application of predictive modelling technology for all direct marketing programs. This extended to the introduction of models which targeted the acquisition of new customers based on return on investment.
A recognized authority on predictive analytics, Richard is counted among the top five experts in the analytics field in Canada. He gives seminars on segmentation and predictive analytics for organizations such as Canadian Marketing Association (CMA), Direct Marketing News and Predictive Analytics World and his articles have appeared in numerous publications including Direct Marketing News, Strategy Magazine, Marketing Magazine and Predictive Analytics Times. He has also taught applied statistics, data mining and database marketing at a variety of institutions across Canada, including the University of Toronto, George Brown College and Seneca College and sat on the CMA’s Board of Directors between 2009 – 2013.
In 2014, he authored Data Mining for Managers – How to Use Data (Big and Small) to Solve Business Problems, published by Palgrave Macmillan.
“Richard has provided my clients with expert analytic insight into their business. He has helped streamline Data Mining plans and identified un-tapped opportunities. I would highly recommend Richard.”
“Richard co-chaired the database marketing and business intelligence council of the CMA while I was a member of said council. His contribution of effort and energy was and continues to be enormous; Richard is a large factor in the success of the council.”
"Richard has developed and facilitated workshops for the CMA for over a decade with highly positive feedback. He is a wealth of knowledge when in the area of analytics and database marketing. His easy going presentation style is welcomed by participants which allows for greater audience participation. I would highly recommend Richard to speak on any database related topic.”
"Richard Boire has been an authoritative, popular and well-read contributor to Direct Marketing magazine in Canada for more than 15 years. He's one of the most-respected and sought-out speakers and consultants on analytics in all sectors of the marketplace. We've enjoyed working with him on articles and seminars and he is a frequent invited speaker for any and all events we produce in this area."

Program Summary


The world’s most valuable resource is no longer oil, but data. With the rise of data, it is becoming critical for managers to mine these information and discover patterns and relationships to make better decisions.


Led by Richard Boire, award winning international author of Data Mining for Managers: How to Use Data (Big & Small) to Solve Business Problems, Richard will show you how data can be mined to enhance businesses and how data patterns can be visualized to understand the data better, including the process, tools, and its future by modern standards. The program will cover the concepts, methodology, techniques and applications of data mining. Several popular data mining techniques, such as Cluster Analysis, Association Analysis and Decision Trees will also be introduced and delegates will be shown how these techniques can be applied to business analytics problems. 


At the end of the two day program, you will discover how to extract patterns from large data sets by connecting methods from statistics and convert them useful insights. You will also realize the increasing importance of transforming unprecedented quantities of digital data into business intelligence that gives users an informational advantage.


Programs, dates and locations are subject to change. In accordance with Clariden Global policy, we do not discriminate against any person on the basis of race, color, sex, religion, age, national or disability in admission to our programs.



Companies used to answer questions about their customers and the latest trends through market research, but a more effective resource is now available - data mining. The success of any business today largely lies in its ability to search through and discover patterns in large datasets and thanks to the emergence of Big Data and AI, the need for skills and proper practices in this discipline is booming.


In these sessions, author of Data Mining for Managers: How to Use Data (Big & Small) to Solve Business Problems Richard Boire will guide you to a more solid understanding of how these two areas complement each other. This course is not intended to turn you into a hardcore mathematician, but to equip you with a better comprehension of the impact of math outputs on overall business problems, as well as the knowledge and tools to build more meaningful data mining solutions.


What You Can Expect


What You Will Obtain After This Program

  • JUSTIFY Data Mining as a key competitive business advantage
  • IDENTIFY the four steps in building a Data Mining solution with proven results
  • EXPLORE the appropriate tools in developing optimally productive Data Mining solutions
  • GAIN INSIGHT on aligning solutions with business objectives
  • FORMULATE Data Mining as part of corporate culture to ensure performance sustainability
  • ENHANCE ROI and business dollar benefits with Data Mining
  • REVIEW stakeholder roles and responsibilities from a Data Mining perspective
  • MASTER the top 10 tips in building successful data mining solutions with well-recognized tools and practices

Who Will Benefit Most


This Executive Program Is Designed For:


Directors, GMs, VPs, Senior Managers, Managers and Executives with responsibilities in the following functional areas:

  • FUNCTIONAL ANALYSTS: Customer Analytics, Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Database/ Market Research, Inventory Flow Analysts, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers, E-commerce System Architects and Web Data Analysts
  • BUSINESS/ DATA ANALYSTS who must develop and interpret the models, communicate the results and make actionable recommendations
  • DATA MINING, DATA WAREHOUSING and DATABASE PRACTITIONERS/ MODELERS/ SEGMENTATION who wish to expand their skills and analytical toolbox as well as hone proficiencies in maneuvering elusive data mining obstacles that stand in the way of superior model accuracy
  • BUSINESS & MARKET INTELLIGENCE, IT & MIS PROFESSIONALS who wish to expand their skills in this increasingly visible area within the corporate IT agenda
  • DECISION SUPPORT SYSTEM/ SOFTWARE ARCHITECTS and DEVELOPERS who require a solid understanding of the infrastructures required for supporting a data mining solution
  • PROJECT LEADERS and those in Portfolio/ Project Management Unit who must report on development progress, resource requirements and system performance
  • ACADEMIA: Statisticians/ Qualitative Experts, Computer Science, Bioinformatics and those in academia who utilize statistical, predictive modeling and data mining techniques for research and development


And those who work with data and wish to understand and use recent developments in Predictive Analytics and Business Intelligence/ Business Analytics, from cross industries especially: Banking & Financial Institutions, Telecommunications, Consumer Products, Manufacturing, Conglomerate, Retail, Education, Oil & Gas, Logistics and Utilities.


Program Outline


Day 1


Session 1: Overview Of Data Mining As A Key Business Imperative

  • Defining data mining
  • Identifying the role and impact of data mining within the different business disciplines
  • How data mining evolves within a given business
  • A brief overview of the difference between data mining vs. business intelligence

Case Study: American Express

  • Background and history that led to their major challenges
  • How data mining was introduced to the organization
  • How overall performance was improved
  • What other challenges remained after the first solution was developed
  • What were the subsequent challenges that continued to arise and how data mining evolved to meet these challenges

Session 2: Creating The Discipline Of Data Mining Within Your Organization

  • What are the 4 required steps/phases
  • Roles and responsibilities of key stakeholders within this process
  • Building the right organization structure with data mining as a core discipline
  • Establishing the right balance between software/hardware and people in building tools
  • Building the data mining team and identifying the appropriate skill sets
  • Understanding what are the business expectations of data analytics solutions

- Use of gains charts/decile tables to demonstrate the impact of advanced analytics solutions

  • Championing the C-Suite through the creation of quick wins 

- Examples using high value and RFM techniques


Case Study: Retailer  

  • What is the process used to identify the analytical requirements of an organization
  • How do we use both the existing organization’s  human resources as well as potential outside human resources to address these above analytical requirements
  • Who are the key stakeholders both within and outside the organization that will be our key partners
  • What kind of software currently exists and how does it  address our analytical requirements
  • Identifying other tools that will be required to fill any analytical requirement gaps

Session 3: The Digital Environment And Automation And Its Impact On Data Mining 

  • Increasing automation of analytics reporting - advantages/disadvantages

- What are the advantages/disadvantages of increasing automation
- Empowering the business end user through increased data visualization and reporting tools  

  • Using data mining within the online environment

- Using log data and how it differs from the off-line structured environment
- The key information that is extracted from a page click
- Open rates/click through rates and understanding its impact on consumer behavior
- Integrating offline behavior and online behavior to maximize the performance of a given predictive analytics solution
- Evaluating solutions within the online environment
- Ecommerce and the use of recommender engines

  • Text Mining: Using analytics within an unstructured data environment

- What is the  difference between structured and unstructured information
- What is the process used to build a given solution
- What kind of statistical analysis is deployed in this process
- How do we integrate the unstructured information solution within an overall predictive analytics solution
- The use of sentiment analysis as another analytics tool


Session 4: Identifying And Managing Business Problem

  • How to gather the right information
  • Increasing one’s understanding of the domain knowledge of the business
  • Prioritizing the analytics solutions’ options

Case Studies: Telecommunications Company/ Courier Organization/ Retail Organization

  • Defining the process
  • What are the right questions to ask
  • Analyzing historical results to obtain a better understanding of the business
  • Using analytics to acquire more domain knowledge of the business  


Day 2


Session 5: Gain Practical Insight On Data Auditing

  • Review on The Extract, Transform and Load (ETL) processes
  • The data audit process and its importance in better understanding the data environment
  • Creating  source vs. derived variables
  • Creating the dependent variable vs. independent variable

Case Study: Retail Photography Company

  • Defining the process
  • Identifying your source files
  • Conducting the data audit

- Creating frequency distribution reports and data diagnostic reports to gain better insight on the data

  • From the above reports, determine how to organize, summarize and manipulate the data into the analytical file

Session 6: Implementing Advanced Analytics Tools 

  • Correlation analysis
  • Exploratory data analysis reports
  • Value/ behavior based segmentation vs. cluster segmentation
  • Factor analysis
  • Decision-tree analysis CHAID (Chi-Square Automatic Interaction Detector)
  • Logistic regression vs. multiple regression vs. neural nets
  • Comparing the advantages and disadvantages of the above techniques
  • Evaluating the business benefit of a given solution and its ultimate ROI

Case Studies From The Following Sectors: Banking/Finance, Insurance, Non Profit, Etc

  • What is the process in actually creating the solution
  • How these above tools are used to build the solution
  • Integration of segmentation and modeling into overall predictive analytics solution
  • Eliminating the ‘black box’ of predictive analytics and providing information regarding the solution that is meaningful to the business user
  • How to clearly demonstrate the $ impact of a given solution  

Session 7: Establish The Optimum Measurement Framework

  • What is the quality control process in ensuring that solutions are being correctly implemented

- Identifying your measurement objectives
- Creating the right measurement framework

  • Marketing Attribution

- How to measure business results in a multi-media environment

  • Marketing Mix Modelling

- Using measurement results to determine assignment of marketing dollars to channel


Case Studies And Examples To Demonstrate Actual Payback Of Predictive Analytics Solution

  • Finance/banking
  • Property and Auto Insurance
  • Travel

The Case Studies Will Examine The Following:

  • With a developed solution, what are the reports that need to be created to help ensure effective implementation
  • What does the analyst need to do when results between implementation and when the solution was developed are drastically different
  • Given the specific environment of the organization, what is the process for identifying the measurement objectives
  • How does the analyst marry the data to create the appropriate testing framework and matrix
  • How does the analyst use the data to create the appropriate measurement reports

The Case Studies Will Also Cover The Various Functional Areas Such As:

  • Marketing and such tools as profiles, response models, attrition models,  profitability models
  • Insurance risk models that can be used to predict the price or premium for automobiles and property
  • Analytical solutions that can be used to improve operational processes within a given organization   

For Previous Attendees To The Course, Additional Case Studies Will Focus On The Following:

  • Building solutions that improve the operation effectiveness of the organization
  • Insurance pricing solutions that are more business-oriented towards the actuarial community
  • Data discovery exercise that provides a strategic roadmap for the organization
  • How an ongoing analytics process and culture provides solutions to a wealth management organization

Session 8: Demystifying The Confusion Of Big Data In Data Mining

  • What are the similarities between data mining in big data vs. small data
  • What are the differences between data mining in big data vs. small data
  • How do we leverage data mining in Big Data alongside Small Data for better decision-making
  • Defining Strategies vs. Tactics using data mining within Big Data   
  • The impact of Big Data and data mining into other disciplines:

- Health Care
- Government and Cities
- Manufacturing

  • The Open Source environment and what it means to Data Miners 
  • Case Studies in Developing Big Data Analytics Solutions using Twitter Data, Mobile Data and weather data, location data

Session 9: Artificial Intelligence (AI) 

  • What is the definition of artificial intelligence(AI)
  • What is machine learning and what are the unique advantages of AI over other forms of machine learning
  • Why has AI catapulted to the forefront as an analytics tool Applications of AI within our world today

- Natural Language Processing
- Image Recognition

  • Comparison of AI techniques vs. traditional machine learning techniques in predicting consumer behavior

Session 10: Mastering The Top 10 Tips Of Building Successful Data Mining Solutions

  • Key pieces of learning to consider when building predictive analytics solutions

- How to create quick wins
- Identifying when results are overstated
- Where to emphasize efforts: Statistics vs. the Data


CFOs Leadership :
Experience Clariden
Discover how our leadership program has shaped the perspectives of CFOs across Asia
Venue: Shangri-La Hotel Singapore
Date: 16 – 17 July 2020
Faculty: Richard Boire
Early Bird 1: S$2,795 (by 22 May 2020)
Early Bird 2: S$2,895 (by 19 Jun 2020)
Regular Fee: S$3,095
Group Discount: 2nd participant get 10%, or register 3 participants and 4th participant get a complimentary seat
(1 discount scheme applies)
Note: GST is applicable to participants from Singapore registered companies.
Contact: [email protected]
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