Overview

This course brings the benefit of predictive analytics to real business problems, allowing you to build custom applications tailored to your customers or industry. While applications are typically configured to solve very specific problems, all are based on a common set of capabilities:  Automate decisions using business rules; add insight using predictive models; and use prioritization, optimization, or simulation to reach the best decision based on the above.  A number of packaged applications are available, tailored to solving specific business problems. 
The course will not only cover how to use the packaged applications, but also how to create your own applications, how Analytical Decision Management interplays with IBM SPSS Modeler (v18) and IBM SPSS Collaboration and Deployment Services (v8), and how to deploy results for real-time.

Audience

This course is intended for:
• Anyone with little or no experience in using Analytical Decision Management v18
• Anyone who is interested in using Decision Management techniques to help them make business decisions
• Anyone who is considering purchasing Analytical Decision Management v18

Prerequisites

You should have:
• Experience using applications, such as word processors or spreadsheets, in the Microsoft Windows, Macintosh or Linux environment
•  Experience with Analytical Decision Management v18 is not necessary, though a basic understanding of Decision Management theory and techniques is helpful
• Some familiarity with IBM SPSS Modeler and with Predictive, Clustering, and Association modeling is helpful
 

Objective

Please refer to course overview

Course Outline

1. Introduction to Decision Management
• What is Decision Management?
• Why Use Decision Management?
• Analytical Decision Management
• Five Steps of Decision Management
• Use of Data
• Historical and Operational Data
• Classification Models
• User Defined Rules
• Deploying Models
2. A Sample Session:  Managing Customer Interactions
• Five Steps in Decision Management
• Demonstration:  A Marketing Call Center Business Case
3. Defining Data Sources
• Data Structure
• Field Storage
• Field Measurement Level
• Data Step
• Project Data Source
• Derived Tab
• Secondary Data Sources
• Compatibility of Data Sources
4. Defining Global Selections
• Adding Rules to Global Selections
• Defining and Sharing Rules
• Evaluating Rules
5.  Creating Rules from Models
• Predictive Models
• Predictive Rule Models
• Clustering Models
• Association Models
• Automated Data Preparation & Partitioning
• Evaluating Models
6.  Defining Outcomes
• Specify Project Duration
• Include / Exclude Cases from Project
• Define Action Categories
• Create Allocation Rules
7. Prioritize, Optimize and Combine Outcomes
• Selecting From Alternative Actions
• Prioritizing Outcomes
• Optimizing Outcomes
• Combining Outcomes
8. Deploying Models for Scoring
• Why Deploy the Project?
• Real Time Scoring Panel
• Batch Scoring Panel
• Scoring Configurations
• Using the Scoring View
9. Building a Custom Application
• Application Configuration
• Creating a New Application
10. Using Modeler Streams in ADM
• Using Modeler Streams
• Minimum Requirements for a Stream
• Using a Stream in a Project