Data is a necessary but insufficient ingredient to make strategic decisions. On its own, data is simply recorded observations, often reflected in numbers on a spreadsheet. In order to bridge the gap between data and decision-making, it is necessary to leverage analytics to derive value and insight from the data. That bridge is the focus of Jordan Morrow’s book, Be Data Analytical: How to Use Analytics to Turn Data Into Value, which focuses on how data analytics combined with data visualization can help us make better decisions in both our personal and professional lives.
This book focuses on how data storytelling can influence decision making. As the figure below from the book illustrates, data is the foundational first step in the process but by itself it cannot drive or influence the decision. The middle circle reflects the key bridge whereby data is turned into valuable insight through the analytical process. This insight is then what ultimately helps drive a decision. This book does not provide technical instructions on each of these steps but focuses on the framework and process geared towards professionals who work with data or are interested in working more with data.
The book is structured along the four different stages of data analytics: descriptive, diagnostic, predictive and prescriptive (see Figure 4.1 from the book below). Using the example from the medical profession, Morrow refers to descriptive analytics as a doctor telling you what your symptoms are. Diagnostic analytics takes it a step further and focuses on why your symptoms are occuring. Predictive analytics reflects the medical profession’s research, which tells the doctor which treatments will lead to different outcomes. The final stage of prescriptive analytics is when the doctor would prescribe you with medication to treat the symptoms. Just as with a visit to the doctor, this process is rarely a linear pathway and is often an iterative process where earlier stages are revisited as part of the analysis.
Rather than providing technical instruction or code recommendations, Morrow focuses his book on providing a high-level framework for how to understand the key questions and value from each of these stages and how it relates to different types of occupations from data analysts to data engineers. Through weaving in both personal and professional examples, the book strikes an effective balance of providing a clear foundation for anyone new to using data while also highlighting critical insights that will be valuable for more seasoned experts.
While the book is primarily focused on data analytics, Morrow also weaves in discussion on data visualization throughout. One key quote from the book related to data viz is: “Let’s remember that data visualizations are an important part of data and analytics, but they are not the end goal. The data visualizations should be there to help end users get the insight they need to do their jobs better” (pg. 60). Within this framework, the book provides helpful recommendations on how data visualization can enhance the analytics process while maintaining a clear focus on the bridge between data and decision-making so that data viz is a value-add rather than a superfluous distraction.
This book provides a clear focus on the ultimate purpose of data and how it can be useful in driving decisions. I often fall into the trap of assuming that making a data analysis or visualization more technically complex will naturally lead to it being more valuable. Morrow does a great job of deconstructing this mindset and focusing on how different parts of the data analytics process from initial descriptive analytics to more complex prescriptive analytics all have a critical function to play in driving decision making. If you are interested in having a strategic framework to guide how to use data better in your professional and personal life then I would highly recommend giving this book a read.
To learn more about this book, I had the chance to interview Jordan Morrow to ask several questions. See a synopsis of that conversation below:
Joshua Pine (JP): Could you give us a brief introduction to the book from your perspective? What do you see as some of the key insights?
Jordan Morrow (JM): I don’t think I would have ever thought I would write three books and am now writing a fourth. For this book I wanted to continue on the trajectory from my first book which was focused on data literacy and focus on the world of data and analytics. For most people, they don’t need another book about formulas or statistics. I wanted to weave in more than just business examples and share personal anecdotes which people can relate to more. I want people to see themselves in the world of data analytics and provide a conceptual framework.
JP: What do you see as the overlaps between the worlds of data analytics and data visualization? How can data viz specifically work within the four stages of data analytics (descriptive, diagnostic, predictive, and prescriptive)?
JM: When you have a dataset to analyze with 50 columns and 100,000 rows, you don’t want to have to manually look through that to find insights. Data visualization is a powerful tool that can spark curiosity, questions, and discussions around diagnostic analytics. Visualizations can bring these analytics to life and can also be part of later steps in the analytics process including predictive analytics models.
How data visualization plays out within the four stages is highly dependent on context and needs. While Excel often gets a bad reputation, it is often sufficient for a lot of visualizations. Sometimes we’re just focused on what is happening and our data visualization can stay within the descriptive analytics space. Other times as we grow in data literacy, we may need a self-serving dashboard that targets the diagnostic or predictive analytics stages. As we know, our visual perception is often the most powerful and when that is harnessed as part of prescriptive analytics or generative AI tools it can help illustrate some really complex topics.
JP: How does data storytelling and data literacy intersect? How can you craft data visualizations that both meet audience members where they are at in their literacy journey while also pushing them to grow and mature?
JM: First of all, we need to get to know our audience members really really well. After that, we need to explore how to integrate education into data visualization, whether that’s in the form of tooltips with additional information or links to guide the user through the process and explain any new or foreign concepts. Another important piece can be to find an accountability partner to bounce ideas off of and to gut check whether the visualization you are creating accomplishes what you’re trying to get at.
JP: In your book, you emphasize the “human factor” that can foster creativity and contextualize our analytics work. How do we balance the positive aspects of our human contributions while avoiding the dangers of bias?
JM: With the rise of generative AI tools, it is more important than ever to lean into the human element of our analytics and visualization work. As more mundane and routine tasks get automated, we should embrace our creative human side to shape the direction of those tools. In order to do this well, however—while minimizing the dangers of human bias—is where data literacy comes into play. Continuing to grow your data literacy will enable you to better understand what type of insights you’re able to derive from the data and where potential biases may emerge. Another strategy that can help is going back to an accountability partner or mentor who is able to provide candid feedback based on mutual trust and respect. Finding someone in your life who can fill that role can be really powerful in your data journey.
“With the rise of generative AI tools, it is more important than ever to lean into the human element of our analytics and visualization work. As more mundane and routine tasks get automated, we should embrace our creative human side to shape the direction of those tools.”
JP: How should we view the shifts that will happen in the data analytics and data visualization fields due to generative AI? How can practitioners prepare themselves for this new reality?
JM: The reality is that generative AI tools will be disruptive and will inevitably lead to job displacement or, at the very least, it will replace certain tasks within job portfolios. We should embrace this new reality and recognize that it will free us up to engage in deep work and focus on our creative human potential. We should view generative AI as our partner and leverage its technical capacity so we can flourish as data analysts, data scientists, data engineers, and other roles. We should compete with, rather than compete against, these tools.
With regards to the four stages of data analytics, generative AI seems best suited to support the descriptive, predictive, and prescriptive processes. Based on its current capabilities, it does not seem able to fully fulfill the diagnostic phase with a focus on deciphering insights and answering key questions regarding the why behind observations. From a career perspective, it seems that that diagnostic phase may be most valuable for us to lean into right now as we continue to partner with generative AI tools.
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Joshua works on the Urban Innovation team at the National League of Cities (NLC) where he leads the organization’s data visualization portfolio. He specializes in leveraging data to inform local policymaking and in amplifying best practices through data storytelling. Based in Cincinnati OH, he is an electric bike enthusiast and passionate advocate for active transportation.
