Verona (Italy), September 2004. I was about to start my first class at the university. The subject: Spanish literature. During high school, I was fascinated by humanistic studies such as history, arts, and literature. During my spare time, I was also trying to produce small “pieces of art.” For example, I tried to start a novel about my young life, write poems remembering a past love, or do a painting applying the principles of perspective when we were studying Leon Battista Alberti. After three years, however, I started to look for new challenges and think more seriously about the job I wanted to do. Unfortunately, what I was studying was not leading me toward my goal, and I decided to change my country and my education. I went to Switzerland to study economics. My interest in economics, math, and statistics kept on increasing, to the point that even after completing university, I continued studying these subjects on Coursera and acquired a master’s in Business Intelligence and Big Data in Madrid, Spain. In the beginning, I regretted all the time I had “wasted” on humanistic studies. I was lagging behind other people who were competing for the jobs I wanted. I wished I had studied statistics and programming when I was younger. But then I realized that even if I had a lot to catch up in those skills, I had something that made me somehow different in a positive way. I had a better grasp of business problems, I was always thinking strategically, and I could easily and effectively present results. I finally understood that humanistic and “soft skills” studies helped me tremendously to be a better analyst. After this epiphany, I started to complement my continuous training in statistics and programming with training related to communication, creativity, public speaking, or design—aware that these skills make me a better analyst than I would have been without them.
It’s a pity that the focus of data analysis is almost exclusively on statistics and programming. Yes, you may read that analysts need to know the business, be creative, communicate results well, and so forth, but these skills are typically viewed as “nice to have” skills. That’s why I decided to write a book about the most insightful “soft” skills I’ve learned and that have proven to be very helpful in data analysis. I’m not pretending to reveal any new or extraordinary information. I’m only meaningfully organizing what I’ve learned from many authors far more expert than me in their respective domains—I sincerely thank them for their work. I encourage you not to stop at the content of this book but to investigate further through the resources I’ve used.
Who is this book for?
This book is for all data analysts who want to learn or improve nontechnical skills to make a difference in their jobs. If you are a technically oriented analyst, this book will help you learn the fundamentals of nontechnical skills. If you are a business-oriented analyst, this book will help you consolidate your nontechnical skills and probably learn some new ones.
To illustrate the usefulness of this book, I’d like to use the famous Pareto rule, which states 20% of effort is needed to obtain the knowledge to solve 80% of problems.
This book is about the core 20% of nontechnical skills that represent the basic knowledge needed in 80% of the cases. If you are a data scientist, this book will help you achieve this 80%. If you are a business-oriented analyst, however, this only represents the basics and you will have to spend the other 80% of effort to get the extra 20% of knowledge in nontechnical skills to excel in them.
First, we explore the principles of creativity and how to use it in data analytics by adopting several techniques (“Be Creative”). Then we look at the importance of knowing your business, accompanied by a short overview of the main business concepts, as well as how to implement a sound data strategy aligned with your company’s objectives (“Know the Business” and “Think Strategically”). In “Understand the Human Mind,” I help you understand why humans seem to behave “irrationally” and how to use these concepts to improve data analysis. In the next chapter, “Go Beyond Statistics,” I explain several “nontechnical” data analysis techniques, such as heuristics and analytics. Even if statistics is the backbone of data analysis, thanks to these techniques, the quality of your work can improve exponentially. Finally, the last part of the book is dedicated to communication-related skills. Since data analysts have to deal with both providers and other departments, they have to be good at negotiating (“Be a Negotiator”). Further, we look at the basic principles of communication and storytelling (“Be a Communicator”) that will help you become a good writer, public speaker and designer (“Be a Writer,” “Be a Public Speaker,” “Be a Designer”). I will especially emphasize the content of the last chapter because the ability to create good data visualizations and dashboards is a major core competence of any analyst.