Do you struggle with conversations about data?
Are you hesitant to ask data questions for fear of looking dumb?
Do you struggle with not knowing where to start with data?
Are you overwhelmed with the data terms and information available?
We hear so many buzzwords every day, like big data, machine learning, artificial intelligence, data-driven decision making, data insights, and many more. Take the word insights. Everyone uses insights these days, but without a common definition or understanding. The assumption seems to be that if we collect and use data, this magical insight will show up. Machine learning carries a similar mystique—people are liable to think of it as a very advanced field only a select few can understand. In addition, there are also so many data tools and the list keep growing, making it confusing to understand the starting point.
Can you name something that is part of your daily life that uses machine learning? Think supervised learning algorithms. Can you guess what I’m talking about? If you’ve ever viewed a notification on your smartphone like iPhone that reads, “You have a new memory,” you’ve seen machine learning at work. A decade ago, I never would have thought my phone would identify all my food-related photos and group them together with a title like “Bon Appétit Over the Years.” It is refreshing to see the chef side of me making so many mouthwatering dishes over the years, but I did nothing to create this memory. The data side of me knew the answer. My phone used on-device machine learning to analyze every photo in my library in a variety of ways, including scene classification (grouping my food pictures), people and pet identification, and audio classification.1
Data is embedded in so many aspects of modern society, and integrated seamlessly, that we often don’t even recognize it as artificial intelligence. Our entertainment recommendations, modern cars—with drowsiness detection!—and fitness trackers are all driven by data and artificial intelligence.
Over the past decade, and especially during the COVID-19 pandemic, the data landscape has changed drastically. In 1880, it took the United States Census an estimated eight years to process census data for a population of 50 million. By 1890, the German American statistician Herman Hollerith had introduced the electric tabulating system, improving processing speeds significantly. The system was the first of its kind data processing machine to replace manual processing by hand.2
We have come a long way in terms of data processing speed, from several years in the past to the point where we now sometimes demand data available in close to real time. Today, every company, no matter its size, is data driven in one way or another. Companies are using data to improve customer experience, create a new value stream, and stay competitive. So, understanding data is a critical necessity for everyone.
I have worked with Fortune 500 organizations, not-for-profits, and everything in between. And within various organizations, I have repeatedly seen a gap between data teams on the one hand and leadership or business stakeholders on the other. These parties are not aligned in their data goals or in their understanding of the uses and capabilities of data. This book arose from my desire to close this gap and provide an easy-to-follow, technology-agnostic reference guide with relatable examples.
Many business professionals and students—such as business analysts, UI/UX designers, project managers, marketing teams, finance teams, and executives—are forced to interact with data and those who generate it. Few have been taught the general competencies needed to feel comfortable having these conversations. There are tons of information about getting into the field of data and working with data. How many articles, books, blogs, or videos have you read or watched in the past six months? This data information overload actually makes people less likely to retain knowledge. Our brains do not retain what we read unless we use and experiment with it. My aim is to enable people to want to learn more about data, to be curious about data, and who to reach the Data as a Hobby stage and wish to level up to analytical thinking. This book is arranged with the information you need to thrive in your organization—and nothing more. (No information overload!)
Working with data requires experimentation and questioning. It is a quest to discover the unknown, which are elements of a curious mindset. Unfortunately, curiosity is not considered an essential part of the recruitment process; neither is it encouraged nor promoted as a value in most organizations. But thriving with data is not about knowing a bunch of coding languages and technical tools. It is about maintaining a curious mindset, gaining a foundational data understanding, and seeking out answers to questions asked—and questions not yet asked in addition to the requested business requirements. If you develop a curious mindset around data, finding the right tool for the job will be easier (and will help you avoid learning a tool only to realize it is not the one you need most). This book focuses on developing that necessary core understanding about data.
This book is the go-to guide for any business reader who wants to understand the language of organizational data and feel comfortable conversing the language of data. What’s more, Thriving in a Data World is unprecedented in its accessibility and readability for the nontechnical reader.
This book focuses on the foundations you need to successfully manage and engage with data analytics initiatives, and to bridge the gap between the creators and users of data. As a management reference guide, it discusses the different types of data strategies needed to succeed with data, and it covers topics such as data team composition, types of data analytics, the importance of data storytelling, and identifying data ROI.
In psychology, Picture Superiority effect—or the famous saying “a picture is worth a thousand words”—refers to people’s propensity to remember pictures better than text. Data is no different, and it is therefore essential to gain the skill to tell stories with data. I have worked with several people who were technically skilled, but who suffered when it came to presenting the data findings. No one can act on data if no one follows the data analysis and insights. As a result, this book includes a chapter about how to persuade with data, irrespective of the tool you use for visualizations. Even if you follow and understand data, if your company lacks a data culture, it will resist making any data-driven decisions. This book discusses how to encourage a data culture and how to overcome challenges to data culture. It also takes a practical approach to Data Analytics: each chapter contains simple, straightforward actions you can take immediately to start implementing your newfound knowledge. It’s an enjoyable, engaging way to learn how to confidently interact, manage, and work with data analytics teams today.