1. The Course

• Logistics, Materials & Course Expectations
• Agile & Integrated (A&Iâ„¢) set of Tools and Best Practices
• References & Resources

Practice Sessions – Individuals prepare a brief Challenges & Interests List. Everyone will introduce themselves and the instructor will consolidate and standardize terms for our Challenges & Interests List used to further tailor the delivery. The group will debrief on areas of interest and if needed, take on homework to research topics and report back to class.

2. Introduction to Data Analysis and Analytics
This module reviews the history and evolution of the field of business intelligence. It shows the critical need for best practices in data analysis, especially as the volume of data grows, and the time available to make decisions and remain competitive continues to shrink.

• Definition and history
• Current technology, the growing availability of data, and increasing challenges
• Applications for gaining competitive advantages
  • Fact-based decision making
  • Process tracking and control

Practice Sessions – Individuals prepare a brief addition to their job description to cover their new duties using data analysis. A group exercise will review each job description portion and construct a comprehensive data analysis job description from each team.

3. Rethinking the Value and Usage of Data
Accurate and relevant data is the essence of any organization's ability to act decisively. The realities of the modern workplace have dramatically altered the quantity of data the organizations uses, generates, and dispositions. The pace, and dynamics of our work have changed markedly in the last ten years, but attitudes and practices for working with critical organization data has not really kept up. Success requires that the right data and only the right data is used to make important decisions. To get there, we need to expect more of our data - and the people and processes that provide it.

• Key concepts and essentials
  • The Impact of Vast Volumes of Available Data especially for decision making
  • Data Difficulties and Limitations: ROI vs. Effort/Expense, Incomplete & Inconclusive Data
  • Dealing with Data Uncertainty
  • Getting Real Value out of your Data: The Data Continuum
  • Effective and responsible Data Ownership
  • Advantages and disadvantages of Qualitative and Quantitative Data Types
  • Solutions and Best Practices to transform the way your Organization Accesses and Uses Data
  • Organizing the Entire Organization's Data for maximum efficiency using easily available tools
  • Taking advantage of the Expertise of the Entire Organization

Practice Sessions - Learners discuss specific data challenges they commonly deal with in their organizations.

4. Introduction to Data Mining and Data Warehousing
This module outlines the scope of the field of business intelligence and introduces two topics that compliment and expand the concepts of analytics to a full implementation.

• Data Mining concepts and application
• Introduction to application benefits of Data Warehousing

Practice Sessions – Individuals discuss Data Mining and Data Warehousing practices ongoing in their areas. Best practices and tools are noted where they are used.

5. Data Distribution and Variance
Effective decision-making requires a determination and assessment of the relative or expected value and uncertainty of future events. Valid decisions come from knowledge of the probable impact of different controllable and uncontrollable variables. Probability theory provides the knowledge and tools to determine the uncertainty, relative accuracy, and risks inherent in making decisions. Variance is also an essential consideration as the relative accuracy of the data should weigh heavily when a decision is being made.

• Key Concepts and Essentials
  • Decision Making Under Uncertainty
  • Probability Overview
  • Data Distribution: Normal and Other Distribution Types
  • Variance: Confidence Intervals and Confidence Limits
  • Standard Deviation

Practice Sessions – Use spreadsheet functions to estimate parameters of a given probability distribution. From these results, establish the expected value and standard deviation.

6. Describing Information Needs
This module covers the background for and best practices of information requirements for various levels of management needed to make decisions and review operational performance. Application of analytics is a key part of building systems to effectively provide the information required by all levels of management.

• Identify Operational and Executive Information Classes
  • Describing Key functional Transactions and Documents
  • Map Information Needs to Underlying Data
• Executive Information Needs and the Balanced Scorecard
• Role of the Business Analyst and Data Analyst
• How to use Simple Pivot Tables in Excel or Google Sheets to Analyze and Present your Data
• Tracking and Managing Business Process Performance
  • Modeling Key Decisions and the Needs for Information
  • Selecting Measures and Targets
  • Measuring Performance and Finding Performance Gaps
  • Process Assessment and Improvement
• Learning From Data: Experience Provides The Best Data
  • Historical Data
  • Root Cause Analysis
  • Lessons Learned - Retrospective on how our analysis & presentations went
  • Best Practices

Practice Sessions – Learners work with a Pivot table Exercise to gain basic skills with Excel or Google Sheets.

7. Data Exploration Concepts and Methods
This module discusses how to apply a number of tools to extract information from a set of observations by calculating key parameters and summarizing the data in graphs and tables. The relevance and validity of the sample information extracted from a population is confirmed by making inferences that apply to the whole population.

• Basic Concepts
  • Types of Variables
  • Selecting Dependent and Independent Variables
  • Sampling Error and Biases
• Descriptive Measures of a Sample
  • Randomness
  • Key Sample Parameters
  • Variability
  • Sample vs. Population
  • Sampling Distributions
  • Sample Size and Errors
  • Sampling Best Practices
• Histograms
• Statistical Hypothesis and Inference
• Dependence and Correlation
  • Correlation vs. Causality
  • Establishing Correlation Among Different Variables
  • Limits of Statistical Methods and Assessment Bias
• Moving Beyond Data and Decision Uncertainty: Managing Risk
  • Risk Awareness
  • Risk Culture and Risk Tolerance
  • Qualitative vs. Quantitative Risk Analysis

Practice Sessions - Learners practice using the "rand()" function in Excel or Google Sheets. Learners discuss their current Risk Management and Analysis techniques.

8. Forecasting
Decision making depends on the forecasting of future events and results. Accurate forecasting depends on discovering patterns in historical data and on the assumption that those patterns will hold over time. Optimal forecast methods rely on the historical patterns and the knowledge provided by subject matter experts and even sometimes, on publicly available data. Different methods and techniques can be used, including the need for incorporating the input from subject matter experts.

• Forecasting Methods and Models
  • History of Forecasting
  • Simple and Proven Forecasting Methods
  • Long and Short Term Forecasts
  • Heuristics
• Time Series Analysis
• Linear Regression
• Establishing Trends and Business Cycles (i.e. seasonality)
• Selecting Independent Variables for Predictive Models including Regression Techniques

Practice Sessions - Individuals outline their current and desired approach to Forecasting as it relates to the course material.

9. Review, Best Practices, and Next Steps

• Data Analysis and Transformation
• Best Practices Revisited
• Next Steps Options: Short-Term vs. Long-View Strategic Changes. Low Hanging Fruit and High ROI Options

Practice Sessions – ABQ! (Adopt, Bright Spots, Quit) – Declare your intent topics from this course in your work or volunteer work in three ways: Adopt - What you will start to do; Bright Spots - What you will continue to do that has been proven to work in your organization; Quit - What you will stop doing.

10.Course Closeout: Putting It All Together. The Value of Powerful Data

11. Additional Resources and Exercises