The Predictive Analytics Operation - Design

Upon completion of The Predictive Analytics Operation Design Course, students will be able to:

• Plan and manage your predictive modeling projects effectively from the start
• Identify, qualify and prioritize actionable analytic opportunities
• Understand the purpose, function and impact of an analytic process model
• Outline the implementation tasks of the Assess and Plan Phases of the Modeling Practice Framework
• Define a project roadmap with modeling objectives that lead to measurable project gains
• Qualify the downstream organizational and environmental requirements for model deployment and operation
• Recognize dead-end approaches in advance that lead to wasted resources on doomed approaches
• Broaden experience through active problem-solving and guided discussion in realistic project scenario
• Certify data resources and plan for an efficient analytic sandbox
• Engage with confidence among your developers, analysts and consultants
• Develop the rare analytic leadership traits to design and oversee actionable analytics projects
• Leave with resources, contacts and plans to substantially reduce your project preparation time, costs and risks

Note that this course does not drill into specific algorithms or tactical implementation topics. For a deeper presentation of modeling methods, algorithms and techniques, refer to the Development Course which directly follows this event. To step through all phases of The Modeling Practice Framework™, view the Comprehensive Experience course description
 

The Predictive Analytics Operation Design Course is intended for:

• It Executives And Big Data Directors: CIOs, CAOs, CTOs, Stakeholders, Functional Officers, Technical Directors and Project Managers who desire to extract and apply the true value hidden within their deluge of data
• Line-Of-Business Executives And Functional Managers: Risk Managers, CRM Managers, Public Sector Directors, Business Forecasters, Inventory Flow Analysts, Financial Forecasters, Medical Diagnostic Analysts, Fraud and Loss Prevention Managers, eCommerce Company Executives
• Data Scientists: Who recognize the importance of complementing their tactical proficiency with a strategic planning and design approach to advanced analytics
• 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

While this course is designed to be taken independently, it is important to understand its place and function within the overall ‘The Predictive Analytics Operation. Registrants will be required to view a three-hour asynchronous “Core Concepts” orientation prior to attending this event. Access details for the Core Concepts prerequisite will be shared with participants prior to the start of the course. Prior education or experience in data analytics or statistics is helpful, but not required.
 

Core Concepts

• Prerequisite Three-Hour Preparatory Orientation
• View the full Core Concepts Topic Outline

Terms Used in Today’s Analytics Environment

• Big Data Analytics
• Predictive Analytics
• Data Science
• Business Intelligence
• Data Analysis
• Machine Learning
• Dashboards
• Applied Statistics
• Prescriptive Analytics
• Predictive Modeling
• Internet of Things ( IoT )
• The Current Landscape of Analytics Software

Assess Phase

• Assemble Team
• Leadership, Analysts, Subject Experts, Data Support, Stakeholders, etc
• Determine Whether External Talent is Needed
  • Examine Culture & Mindset
  • List Candidate Projects
• Place Projects on a Benefits / Challenges Quadrant Plot
• Guided Discussion Breakout Session
  • Define Performance Benchmarks
  • Identify Data Sources
  • Itemize Existing Analytic Resources
  • Describe Operational Environments
  • Initial Report of Overall Practice Readiness
• What Should an Assess Phase Report Contain?
• Exercise Breakout Session

Plan Phase

• Pull & Recon Data
• Explore Data & Verify Quality
• Do We Have Enough Data?
• Which Data are Relevant?
• Make a First Look at Data Quality
• Exercise Breakout Session
  • Design Analytic Sandbox
  • Qualify Team
  • Qualify Tools
  • Define Operational Environment(s)
  • Establish Performance Benchmarks & Targets
• What are the current metrics (KPIs)?
• What is the Role of Technical Metrics vs. KPIs?
• Benchmark Demonstration
  • Consider Deployment Options
  • Prioritize Viable Projects

Wrap-Up and Next Steps

• Supplementary Materials and Resources
• Conferences and Communities
• Get Started on a Project!
• Options for Implementation Oversight and Collaborative Development