Data Mining for Business

This Live Instructor-Led Training course is currently under development but you can pre-register your company or team by clicking HERE.

You can also purchase the self-pace online version of the course by clicking HERE.


Data mining, often referred to as data science is the  hot new  discipline providing a unique  competitive  advantage for businesses  today. Its  growing importance is recognized by  Harvard Business professor Thomas Davenport’s best selling business book ‘Competing on Analytics’ which is a testimonial to the ever-growing importance of this discipline. Intuitive-based decisions and one’s prior experience is becoming more the exception rather than the rule. Businesses are now adopting the discipline of data mining  to provide a more quantitative approach in their decision-making.

With the explosion of information, businesses are now able to develop  solutions  and evaluate their performance after their deployment. But how is data mining used to develop solutions and more importantly how do companies both action and evaluate these  solutions. As with any discipline, though, there is a process and approach that is critical in creating the necessary steps for building successful solutions. This course is about how institutions become entrenched in this discipline utilizing the four step data mining methodology. Adopting this four step approach, both simple as well as advanced solutions are presented as valid business outcomes depending on the business problem. But the course’ focus on data reinforces the notion that data is the key in building successful data mining solutions.

Within this data mining process and through many years of experience in building analytical solutions, much learning has amassed on what works and what does not work. Through this course, this prior learning is leveraged as learners are provided with a comprehensive perspective on how to both build and deploy predictive analytics within their respective organizations. Numerous case studies within organizations demonstrate the increasing significance of data mining  as a core business discipline.

Who should take this course

·         Heads of Technology

·         Heads of Analytics

·         Marketing Managers

·         Risk Executives

·         C-Suite Executives: CEOs, CFOs, CMOs, COOs, CIOs etc

·         Business Unit Heads

·         Any manager with an interest in strategy and innovation

·         Analytics practitioners

·         Business Analysts

·         Data Mining/Data Science Analysts

To capitalize on the expert knowledge in order to gain maximum value of these vital issues (Key Benefits):

  • JUSTIFY  Data Mining  as a key corporate competitive advantage in order to stay on top within organizations
  • IDENTIFY the four steps approach in building a data mining  solution with proven results
  • EXPLORE the appropriate tools in developing  data mining analytics solutions to achieve optimum productivity
  • GAIN INSIGHT on aligning the solutions with the organization’s business objectives from a whole new perspective
  • FORMULATE data mining  as part of the corporate culture to ensure performance sustainability
  • ENHANCE Return On Investment (ROI) and business dollar benefits with data mining and determine $ benefits of using Big Data vs. Small Data
  • REVIEW   roles and responsibilities from a data mining  perspective within multiple sectors
  • COMPARE BIG Data And SMALL Data and determine what is really new and unique within the data mining discipline and within business
  • MASTER the top 10 key tips in building successful predictive analytics solutions with well-recognized tools and practices.

Instructor: Richard Boire

Richard’s experience in database marketing and predictive analytics dates back to 1983, when he received an MBA from Concordia University in Finance and Statistics. His 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.
Richard is a recognized authority on predictive analytics and is among a very few, select top 5 experts in this field in Canada, with expertise and knowledge that is difficult, if not impossible to replicate in Canada. This expertise has evolved into international speaking assignments and workshop seminars in the U.S. , England, Eastern Europe, and Southeast Asia.
He has co-authored white papers on the following topics: ‘Best Practices in Data Mining’ as well as ‘Customer Profitability: The State of Evolution among Canadian Companies’. In Oct. of 2014, his new book on “Data Mining for Managers – How to use Data (Big and Small) to Solve Business Problems” was published by Palgrave Macmillian.


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