This course is designed to explain and demystify Big Data in non-technical terms. It bridges the gap between market buzz about Big Data and business realities. It documents real-world usage and ROI of Big Data, delineates successes and failures of Big Data, and the reasons for both. It characterizes what a data scientist is, and what s/he does all day. It discusses the pros and cons of various organizational structures for Big Data and Analytics teams. In short, the course peels away the complexities surrounding Big Data, boiling it down to the essence that managers need to know to make optimal decisions about the use, resourcing, risks, and value of Big Data.
Sandra Hendren, the instructor, is an energetic and entertaining speaker. She is a 30-year veteran of data and analytics, immersed in the evolution from “small data” and “analyses and reporting” in the 80’s to “big data” and predictive analytics today. Her experience is 1) hands-on - she still writes code and develops predictive models and machine learning algorithms -2) functional management in charge of multiple data and analytic development teams, and 3) at the executive level - most recently as Chief Data and Analytics Strategist for UnitedHealth Group, the 12th largest company in the nation.
This course is guaranteed to be different. It is an honest, technology-agnostic, vendor-neutral explanation of the rhetoric and the realities of Big Data. Ms. Hendren is sometimes blunt, often funny, and always clear. You're invited to learn a lot and enjoy yourself while doing so in this 4-hour course.
What am I going to get from this course?
- Understand what’s real and what’s not – the rhetoric and realities of Big Data
- Have a working knowledge of the business challenges and strategic rewards of Big Data initiatives
- Be conversant in real world case studies, both successful and unsuccessful ones, and the reasons for each
- Distinguish different organizational structures for Big Data and Analytics teams, and the pros and cons of each
- Understand the skills needed for professionals of a Big Data and Analytics team, including what is really important to look for in a Data Scientist
- Empower managers with the knowledge necessary to make optimal decisions about the use, resourcing, risks, and value of Big Data
Module 1: Big Data Defined
Multiple definitions of Big Data are given, then compared and contrasted for management usefulness. First, the “official”, technical definition that started all the buzz. Second, the "unofficial" definition. Then some executives’ definitions that I’ve heard in private interviews. Next, a strategic definition that I have developed. Fourth, a working definition specifically created to be actionable for managers. This chapter concludes with the results of two executive surveys on their understanding, use, investments, and results from Big Data in their organizations.
Module 2: The Business Value of Big Data
A legitimate complaint about Big Data is the lack of validation of its business value. This section addresses that. Both breakthrough business cases and everyday business cases are given and discussed. These are followed by several meta-studies of the ROI of Big Data. Finally, several management surveys of the use and ROI of Big Data are shown.
Module 3: The Advent of Data Science
The Advent of Data Science
The term 'data science' is defined and explained. The evolution of a new professional, the Data Scientist, is discussed and compared to an existing position known as a Data Analyst. Three types of analytics are explained, along with the professional who does each. Examples of all three types of analytics are shown, and the business value of each is discussed. Finally, Big Data Analytics - its similarities and its differences to 'regular' analytics, is listed and explained.
Module 4: The Tools of Data Science
The Tools of Data Science
This section is an overview of the tools of data science. Two terms that you're probably heard a lot - data mining and machine learning - is explained in sufficient detail that you no longer have to wonder what the blogs, the press, and the 'techies' in your company are talking about. These newer tools are compared to traditional statistics, and a discussion of whether or not "statistics is dead - long live data mining and machine learning" is rhetoric or reality.
Module 5: The Skills Needed for Big Data
What are the Skills Needed for Big Data?
This is a fairly detailed review of best practice / best methodology for Big Data projects and the professionals who must perform them. Emphasis is given to the skills needed to be a good Data Scientist. Not just any Data Scientist, but a good one; one who is successful at business-driven advanced analyses and predictive modeling. The section ends with a list of characteristics to look for - and what to avoid - when hiring Data Scientists.
Module 6: The Risks of Big Data
First Type of Risks - Big Data Data Issues
This lecture largely turns popular beliefs about Big Data on its head. It explains why so many of the purported strengths of Big Data - the ability to discover "hidden", complex relationships, to predict rare events, and to use loads of data to improve predictions - often fail. It takes managers for "a walk" behind the scene to become knowledgeable of the statistical fallacies - and business risks - of these claims.
Other Risks - Big Data People Issues and Big Data Technology Issues
This lecture summarizes the author's observation and oversight of data and analytic teams across multiple companies in multiple industries over multiple decades. It is an often humorous, sometimes sad, but always exacting description of risks the author has witnessed; risks such as shiny object syndrome, belief in black box magic, and fascination with esoteric analyses. All these risks are avoidable, if only managers were knowledgeable enough to recognize them. This lecture solves that problem.
Module 7: How Do You Organize Big Data in Your Company?
How Do You Organize Big Data in Your Company?
This lecture begins with a discussion of, what the author calls,'The Paradox of Big Data'. This paradox revolves around the inherent conflicting organizational goals and reward systems of Data Science and Analytic teams vs. Information Technology teams. Then five different organizational structures are delineated, and the strengths and weaknesses of each discussed. The author relays personal experience stories for each one, explaining how the weaknesses can sometimes be avoided.
Module 8: The Future of Big Data
The Future of Big Data
This section gives an overview of things to come in the near future related to Big Data - things you hear about in the press, such as the Internet of Things and deep learning, and cognitive learning. Once again, the intent is to explain these concepts in sufficient detail to remove the mystique. It also covers the size of the market for Big Data in the future, cutting edge businesses already shown to improve productivity and profitability, and completely new business models in both profit and nonprofit sectors arising from the analysis of Big Data.
Module 9: Summary - The Rhetoric and Reality of Big Data
Summary: The Rhetoric and Reality of Big Data
This lecture condenses the first eight sections into a pithy summary of the most salient features of the course. It delineates the market buzz you hear most often, and reciprocates with the unvarnished truth of each claim.
Module 10: Template for a Big Data Strategic Plan
Template for a Big Data Strategic Plan
This section gives managers a template for a Big Data strategic plan. It is intended to be a working document that managers take back to the office to complete, resulting in their own seasoned-looking plan in record time. It steps managers through each task and sub-task involved, and explains how to fill in the details themselves. It is a great finish to the course, pulling it all together into an impressive working document for participants' use.