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Analytics Transformed™
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Project Design And Practical Use Workshops To Discover What Really Works


Data mining is essentially a discovery process — a process riddled with common yet elusive strategic pitfalls. Project failure is rarely due to poor model development. Rather, data mining projects often fall short of their potential due to flawed or overlooked assessment, business understanding, project definition and strategic planning specifically for information discovery.

If you are looking for an intensive vendor-neutral and non-promotional introduction to data mining best practices and an approach to predictive analytics which is critical to modeling success, then this course is designed for you. There are no prerequisites for this course. However, participants will benefit by reviewing the CRISP-DM guide ahead of the training.

“Predictive Analytics & Data Mining: Strategic Implementation” offers a concentrated presentation of capabilities, limitations, risks, rewards, use cases, best practices, strategy and lifecycle management. Those in attendance will actively step through the industry standard process for data mining and realize why an advanced degree in statistics, mathematics or computer science is no longer needed to succeed in predictive analytics. Live working sessions reveal real-world obstacles and breakthroughs from which to interpret, learn and apply.

Practitioners seeking to drill down into the tactical implementation of predictive analytics methods may also attend TMA’s “Predictive Analytics & Data Mining: Model Development course. The “Model Development” course is the counterpart to this production within the series, two days immediately preceding this course at the same public venue.

Make sure to view the course series overview page to compare the two primary orientations and target the most fitting agenda for your experience, situation and objectives.


IT/IS EXECUTIVES AND MANAGERS: CIOs, CKOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers

LINE-OF-BUSINESS EXECUTIVES AND FUNCTIONAL MANAGERS: Risk Managers, Customer Relationship Managers, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Direct Marketing Analysts, Medical Diagnostic Analysts, eCommerce Company Executives

TECHNOLOGY PLANNERS: Who survey emerging technologies in order to prioritize corporate investment

CONSULTANTS: Whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies


  • Make better business decisions based on information hidden within your data
  • Develop a strong vocabulary and understanding of data mining terminology
  • Communicate with confidence among your developers and consultants
  • Plan and manage your data mining projects effectively from the start
  • Experience firsthand that actual model-building is not as complicated as it
    might have seemed through the lecture segments
  • Leave with resources, contacts and actionable plans to substantially reduce
    your project preparation time, costs and risks


Traditionally, organizations use data tactically – to manage operations. For competitive edge, leading organizations use data strategically – to expand the business, to improve profitability, to reduce costs, anticipate behavior, and market more effectively. The mining of data for predictive indicators creates information assets that an organization can leverage to achieve these strategic objectives.

Predictive analytics is a data-driven extension to an enterprise’s decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, online analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective.

The predictive aspect of data mining may be defined as “the data-driven discovery and modeling of hidden patterns in large volumes of data.” Predictive analytics differs from the retrospective technologies above because it produces models — models that capture and represent hidden patterns and interactions in the data. Via data mining, a user can discover patterns and build models automatically, without knowing exactly what s/he’s looking for.

The resulting models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose “what-if” questions to a data-mining model that cannot be queried directly from the database or warehouse. Examples include: “What is the expected lifetime value of every customer account,” “Which customers are likely to open a money market account,” or “How will production quality be affected if various resources are adjusted?”


      After completing this course, you will be able to:

      • Identify the six primary phases of the date mining process
      • Outline the general implementation of the three strategic phases
      • Define and prioritize primary business objectives and detail the criteria for a successful outcome to the project
      • Acquire and examine the properties and quality of the data and evaluate whether the environment and data satisfies the relevant requirements
      • Recognize and avoid pitfalls and misleading approaches that cause most projects to fail or fall short of their potential
      • Summarize deployment, monitoring, and model maintenance strategies
      • Chart your path forward with a clear road-map and wealth of resources


This course offers a balanced and non-promotional presentation of data mining topics and its role in enterprise decision support. The instructor has been deeply involved with the design, development and deployment of real-world data mining solutions.

This course does not drill deeply into specific algorithms or technical implementation issues. For a comprehensive presentation of model development methodology and techniques, refer to the “Predictive Analytics & Data Mining: Model Development” course which directly precedes this event at public venues. This level in the series presents strategic and process challenges that are critical to the success of deploying applied models in real world business environments.

Leading commercial and open-source products will be used from a vendor-neutral perspective to illustrate and compare methods — not to showcase tools. Results are drawn from actual data mining applications and interpreted in the context of business impact. Attendees will depart with a binder full of slides, supporting notes, hands-on experience, a valuable index of data mining resources and certification upon attending the full series and passing an online exam.


“This course opened my eyes to the big picture in a practical way. The content of the “Predictive Analytics & Data Mining II: Strategic Implementation” course was very clear and responsive to my needs. My questions were answered directly and clearly. Exceeded my expectations!”

Bill Scharffenberg
ITS – Business Solutions
Surewest Communications

“Great presentation and summarization of predictive analytics. I sat through two days of the Predictive Analytics World conference and got less from that than I received if the first two hours of this course. Thanks!


“TMA was exactly what I needed to get me started in data mining. The instructor is very passionate about data mining and has excellent real world experiences he shares as a supplement to the teaching material.”

Heather Mitchell
Project Engineer
NASA Ames Research Center

“I would recommend TMA’s “Strategic Implementation” course to executives weighing the costs and benefits of such projects within their organizations. The instructor approaches the course from a business management perspective and presents the concepts in real-world cases making the task of visualizing use of the process in one’s own business a snap!”

Kelli R. Schultz
AVP, Information Technology
iPay, LLC

“Thanks for the ‘shock’ in the way I used to think about Predictive Analytics! My thoughts about how to approach a predictive modeling project have changed totally. This was absolutely the best course or conference track I attended all year. Tony Rathburn is the best.”


“Statisticians and Analysts alike can benefit from this Predictive Analytics course. It is interesting to view the business objective from the other side of the coin. Exploratory Data Analysis in Data Mining is fun because the causality constraint of classical statistics is relaxed. Take this course and open up to another way of dealing with large data sets.”

Raymond D. Mooring, PhD
Wage and Investment Research
Internal Revenue Service

“The ‘Strategic Implementation’ course successfully takes the broad and complex subject of data mining and organizes and explains it in a very logical and understandable way. The training provides real-life examples of the various aspects of data mining and a proven approach to successfully achieving desired results. I can highly recommend TMA’s Data Mining courses to anyone interested in understanding the broad landscape of data mining.”

Dillon Ridguard
Principal, Technology Services Group
Computer Sciences Corporation

“This course gave me just what I needed: a clear conceptual idea of how a data mining project is designed.”

Dotty Korsey
Market Information Manager
Bank of Hawaii

“This class, by far, is the most interesting, motivating and applicable class I have taken in a very long time. The instructor provides a refreshingly different perspective on predictive modeling and approach methodology. Not only would I absolutely recommend this course to any colleague or anyone interested in the practical, yet powerful insights into predictive modeling, but I may look into additional learning and or professional services opportunities. I can’t wait to get back to work and jump right into applying the concepts and learnings.”


“If you want a thorough introduction to data mining at the project level with a wealth of real world experience solving problems, then Tony’s your guy.”

Elies Koudier
Professor of Marketing
Ferris State University

Both instructors did a fantastic job of getting me up to speed much faster than any book (or probably any other training class or conference) available.”

Raymond G. Henderson
Knowledge-Based Systems Engineer
Compliance Technologies, Inc.

“A great experience. I would recommend this course for anyone interested in Predictive Analytics.”

Maisam Salehi
Analyst, Customer Insights
Giant Food Stores

“Attending The Modeling Agency’s predictive analytics training series was a tremendously rewarding experience, helping me to ‘de-mystify data mining’ and interface with exceptionally intelligent people who live in the data mining world.”

Dr. Joan L. Anderson
Apparel, Merchandising, and Textiles
Washington State University