Corporate Training By Zack Academy - Big Data Rhetoric and Reality: Fiction, Friction, Fundamentals and Facts

Big Data is a big deal in business today. But if you were to ask a hundred people what Big Data is – and more importantly, to state its business value – you’d probably get a hundred different answers. Dan Ariely, Professor at Duke University’s Center for Advanced Hindsight, summed it up well in his famous quote, “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

Big Data is too important and too interesting to be so elusive. This Big Data Rhetoric and Reality: Fiction, Friction, Fundamentals & Facts seminar remedies that. It walks participants through the most powerful concepts of Big Data and defines its business value, while simplifying and explaining the technology behind it. The purpose is to distinguish rhetoric from reality, cut through the market buzz surrounding Big Data and boil it down to its essential concepts and applications.

The seminar documents real-world usage and ROI of Big Data. It delineates the successes and the failures of Big Data, and the reasons underlying both. It turns odd-sounding technical terms into fundamental understanding. It characterizes what a data scientist is, and what s/he does all day. It peels away the complexities and rhetoric surrounding Big Data, boiling it down to its essence, presented in a style that all can understand.

Topics Covered

• What is Big Data?
• The Official Definition
• The Unofficial Definition
• Some Executives’ Definitions
• The “Real” Definition
• A Strategic Definition
• A Working Definition
  
  
• What is the Business Value of Big Data?
• Two High Value Use Cases
• The ROI of Analytics
• Analytic Stages and ROI
  
• The Relationship of Big Data and High ROI Analytics
  
• Top Three Sources of High ROI
  
  
• How is Big Data Analytics Different from “Regular Analytics”?
• A Short History of Analytics
• Three Types of Analytics
  
• Descriptive Analytics
  
• Predictive Analytics
  
• Discovery
  
• Big Data Analytic Methods, the Same but Different
  
• Statistics
  
• Data Mining
  
• Machine Learning
  
• Comparison and Cautions of Big Data Analytics vs. Regular Analytics
  
  
• What are the Risks of Big Data?
  
• Big Data Data Issues
  
• The Truth about Social Media Data
  
• Big Data People Issues
  
• Big Data Technology Issues
  
• The Top 5 Risks of Big Data
  
• A Big Big Data Failure
  
  
• What are Big Data Technologies? A Layman’s View
  
• Data and Analytics Technology – Old Rules
  
• Data and Analytics Technology – New Rules
  
• Newcomers: Who Are They and What Do They Do?
  
• Hadoop and Map/Reduce
  
• Open Source Code – Python, R, Pig, Hive, and More
  
• Hadoop Realities
  
• Licensed Software Realities
  
• Total Cost of Ownership of Big Data Realities
  
• How to Decide: The Data Part
  
• How to Decide: The Analytics Part
  
  
• What are the Skills Needed for Big Data?
  
• Data Science Professionals
  
• Data Architect
  
• Data Engineer
  
• Data Scientist
  
• Subject Matter Expert
  
  
• What Does a Data Scientist Do All Day?
  
• Data Scientist Fundamental Skills
  
• Characteristics of Data Scientists
  
  
• How do You Organize Big Data in Your Company?
  
• Historic Data and Analytics Organization
  
• Big Data Organizational Paradox
  
• 5 Types of Organizational Structures
  
  
• The Future of Big Data and Advanced Analytics
  
• From Rhetoric to Reality
  
• Market Facts and Figures – Reality
  
• Biggest Driver of Business Innovation
  
• Continually Improving Productivity and Profitability
  
• Predicting Problems Before They Happen Becomes the New Norm
  
• Changing Ever More Business Models
  
• What’s Next in Big Data?
  
  
• Picking Through the Rhetoric to Define Your Organization’s Big Data Reality
  
• A High Level Big Data Plan
  
• Prologue
  
• My Top Rhetorics (and Associated Realities) Summarized

Who Should Attend

• Line of business executive and functional managers struggling to understand the reality, the business value, the challenges, and the rewards of Big Data
• IT executives seeking business rationalization for Big Data initiatives
  Analytic professionals trying to understand the differences in “regular data” and Big Data
• Data analysts, statisticians, engineers, and computer scientists who aspire to become data scientists
• The curious who are tired of being bombarded by the Big Data market buzz and frustrated at not understanding it sufficiently to make reasoned decisions about its use

Credentials Earned

The Modeling Agency, LLC, is registered with INFORMS (the INstitute For Operations Research and the Management Sciences) as a Recognized Analytics Continuing Educational Provider for the CAP® (Certified Analytics Professional) program. The CAP® credential provides analytics professionals with a means to distinguish themselves and demonstrate to employers, colleagues, and the public that they are knowledgeable analytics professionals. Courses provided by The Modeling Agency, LLC, are automatically accepted by INFORMS when claimed by credential holders as evidence of continuing education. For more information about the CAP® program, including requirements, eligibility, benefits, preparation, and exam dates, please visit the INFORMS website at www.informs.org/certification

What's Included

Each student will receive comprehensive electronic documentation, with the option to order a hard copy. At the conclusion of the course, students will have the option to receive either a high-quality .pdf or hard copy Certificate of Completion.

Syllabus

What is Big Data?

• The Official Definition
• The Unofficial Definition
• Some Executives’ Definitions
• The “Real” Definition
• A Strategic Definition
• My Working Definition

What is the Business Value of Big Data?

• Two High Value Use Cases
• The ROI of Analytics
• Analytic Stages and ROI
• The Relationship of Big Data and High ROI Analytics
• Top Three Sources of High ROI
  

How is Big Data Analytics Different from “Regular Analytics”?

• A Short History of Analytics
• Three Types of Analytics
• Descriptive Analytics
• Predictive Analytics
• Discovery
• Big Data Analytic Methods, the Same but Different
• Statistics
• Data Mining
• Machine Learning
• Comparison and Cautions of Big Data Analytics vs. Regular Analytics

What are the Risks of Big Data?

• Big Data Data Issues
• The Truth about Social Media Data
• Big Data People Issues
• Big Data Technology Issues
• The Top 5 Risks of Big Data
• A Big Big Data Failure

What are Big Data Technologies? A Layman’s View

• Data and Analytics Technology – Old Rules
  
• Data and Analytics Technology – New Rules
• Newcomers: Who Are They and What Do They Do?
• Hadoop and Map/Reduce
• Open Source Code – Python, R, Pig, Hive, and More
• Hadoop Realities
• Licensed Software Realities
• Total Cost of Ownership of Big Data Realities
• How to Decide: The Data Part
• How to Decide: The Analytics Part

• Data Science Professionals
• Data Architect
• Data Engineer
• Data Scientist
• Subject Matter Expert

What Does a Data Scientist Do All Day?

• Data Scientist Fundamental Skills
• Characteristics of Data Scientists
  

How do You Organize Big Data in Your Company?

• Historic Data and Analytics Organization
• Big Data Organizational Paradox
• 5 Types of Organizational Structures
  

The Future of Big Data and Advanced Analytics

• From Rhetoric to Reality
• Market Facts and Figures – Reality
• Biggest Driver of Business Innovation
• Continually Improving Productivity and Profitability
• Predicting Problems Before They Happen Becomes the New Norm
• Changing Ever More Business Models
• What’s Next in Big Data?

Picking Through the Rhetoric to Define Your Organization’s Big Data Reality

• A High Level Big Data Plan

Prologue

• My Top Rhetorics (and Associated Realities) Summarized