For my final class this year at the University of San Francisco School of Management, I taught the students using nothing but infographics. Not only was it fun for me, but I think the students enjoyed being able to summarize their learnings from the semester through group discussions centered around the infographics. The infographics provide a visual opportunity to meld the three fundamental concepts that I believe every business leader needs to understand to be successful going forward: Big Data, Data Science and Design Thinking.
So I’m going to use this blog to provide a curriculum that any university can adopt to bring the power of Big Data, Data Science and Design Thinking into their curriculum. These infographics were designed to drive a discussion and debate, so grab your coffee and some friends and start the learning process!
Any good curriculum needs to start with a class roadmap, and Figure 1 does just that. It provides an overview of the journey that the students will experience as they travel the Big Data, Data Science and Design Thinking path to enlightenment.
Figure 1: Big Data, Data Science and Design Curriculum Overview
To begin the path, students will need to understand:
See the blog “How Millennials Should View the World of Data Science” for more details.
Many folks, especially folks who come from a Business Intelligence reporting and dashboard background, are confused by Data Science.
Business Intelligence uses data and analytics to create retrospective reports and dashboards on what happened; Data Science uses data and analytics to predict what’s likely to happen and prescribe actions.
The infographic in Figure 2 highlights some of the key differences, most important being the highly-interactive, fail fast / learn faster data science engagement process because as the famous American philosopher Yogi Berra said: “Predictions are hard, especially predictions about the future.”
Figure 2: Difference Between Business Intelligence and Data Science
See the blog “Difference Between Business Intelligence and Data Science” for more details.
Data Science is about identifying those variables and metrics that might be better predictors of performance.
The “Thinking Like a Data Scientist” process outlined in Figure 3 is critical to your Big Data, Data Science and Design success because it provides a process for bringing all key stakeholders together to thoroughly identify, validate, vet, value and priority the use cases that are key to the organization’s business and operational success.
Figure 3: “Thinking Like A Data Scientist” process
The result of the Thinking Like A Data Scientist process will culminate in a Hypothesis Development Canvasthat identifies all the key requirements for a successful Data Science project (see Figure 4).
Figure 4: Hypothesis Development Canvas
See the blog “Refined Thinking like a Data Scientist Series” for more details on the Thinking Like A Data Scientist process.
There is probably no more over-hyped, less understood and more powerful concept than “Artificial Intelligence.” What organizations have been doing and will be doing using “Artificial Intelligence” will be staggering. Unfortunately, everyone – and I mean everyone – has their own definition of “Artificial Intelligence” (from the TI-84 to the Terminator). So to be no different, Figure 5 contains my definition of “Artificial Intelligence.”
Figure 5: What is Artificial Intelligence
Probably the biggest difference with my definition of Artificial Intelligence is I believe that 1) Artificial Intelligence is a category, not a mathematical algorithm and 2) that there are multiple levels of Artificial Intelligence that build upon each other. And if you thought that a lesson in Artificial Intelligence meant never opening a Stats book, guess again.
See the blog “Artificial Intelligence is not ‘Fake’ Intelligence” for more details.
I’m a big believer in the power of integrating Data Science with Design Thinking because I have seen the successful results, that is, if you define success around the analytic model’s business relevance and corresponding adoption of the analytic outcomes. Unfortunately, too many folks (including myself originally) think of Design Thinking as too “Foofoo” and not really able to drive material business impact. Oh boy, was I wrong!
Data Science and Design Thinking aren’t different sides of the same coin, they are the same sides of the same coin in the interactive, outcomes-centric approach that they take to ensure 1) you are focused on fleshing out the critical details on the organization’s most important business and operational problems and 2) you have aligned all the right resources and stakeholders to drive successful execution and the resulting organizational alignment and adoption (see Figure 6).
Figure 6: The Melding of Data Science and Design Thinking
I’ll tell you how much I believe in Design Thinking—I’m making my daughter attend Design Thinking Meetups over the holidays while she is home. That’s probably my most important present to her this holiday season.
See the blog “Design Thinking: Future-proof Yourself from AI” for more details.
So there you have it. Colorful, fun and engaging infographics make several complex topics easier to digest and understand. Any university of any size can adopt this curriculum and start ensuring that they are properly preparing tomorrow’s business and society leaders for a world increasingly being dominated by data science and artificial intelligence.
Yep, the key to good design is tricking people into learning something new.