Review of Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed
I wrote this book review of Be Data Literate: The Data Literacy Skills Everyone Needs to Succeed by Jordan Morrow after reading it as part of Women in Data’s June book club. I’ll include a summary of the book, what I liked and didn’t like about it and my final recommendation.
Summary
Be Data Literate aims to teach readers with less formal technical experience about data and analytics so that they can incorporate such tools into their business.
It goes over introductory concepts in the world of data (such as the four levels of analytics) and includes examples for how these play out in the real world. Each chapter also includes a summary and gives readers a preview to content found later in the book.
The book starts by telling the reader about the future of data and explaining that a foundational knowledge of literacy is essential. Morrow also describes the urgency of addressing the data skills gap- the book’s major call to action.
“To be blunt: we must close this skills gap!” (Morrow, 14).
If people don’t know how to understand and make sense of data, Morrow argues, the organizations they work for won’t survive.
The framework for much of the discussion is the “four levels of analytics.” Each level plays an important role in understanding data and using that insight to drive business goals.
“We have seen that descriptive analytics is describing what happened in the past, diagnostic analytics is finding out why something happened, predictive analytics is predicting the future, and prescriptive analytics is allowing the machines to help us know what to do” (Morrow, 34).
In the following chapters, he defines data literacy as the ability to read, work with, analyze and communicate with data. He also describes the “Data literacy umbrella” or crucial elements that go into building a successful data and analytics strategy. These include data science, executive team buy-in and culture.
He continues by describing case studies using the definition of data literacy and the four levels of analytics.
For example, he provides a map of the 1854 London cholera epidemic and explains how John Snow used descriptive and diagnostic analytics to identify the source of the contaminated water (Morrow, 102).
He also explains how individuals can become data literate using the three c’s of learning: curiosity, creativity and critical thinking. In Morrow’s grand finale, he describes how data literacy can be used to drive insight and data informed decision-making. He also lists out data and analytics strategy and topics as well as tips for people looking to get started in the field.
What I liked
The author does a good job in teaching the reader how to frame the high-level value of analytics - one of the biggest barriers to getting the support needed to launch ambitious data infrastructure projects.
I appreciated learning about how stakeholders relate to these efforts. Thinking about the role of executive buy-in was helpful for someone early on in their data journey.
For example, this book helped me consider how data literacy efforts could be strengthened at my organization (a criminal justice advocacy nonprofit). I serve as an analyst as part of a three person department called Systems, Tools and Technology. For people who wear multiple hats on a data team (for example, are both developers and project managers), this book offers insight into how to develop an analytics strategy and communicate its value up the chain of command.
The examples were also helpful. Morrow uses meteorology to explain how weather apps allow users to make choices based on descriptive, diagnostic and predictive analytics.
“We see descriptive analytics in place (the forecast), diagnostic analytics in place (why the weather will be what it is), and predictive analytics being modeled (predicting the forecast forward)” (Morrow, 106).
These types of explanations were easy to understand and showed practical ways people use data in their day to day lives. They were also helpful for data practitioners who seek to better support business teams.
The author’s ability to explain concepts on a high level and consider the strategy behind that communication was helpful for data practitioners who are looking to bring in a new wave and support for data literacy at their organizations.
What I didn’t like
Since Be Data Literate is so high level, the more practical considerations for implementing a data analytics strategy are left out. Morrow also includes almost nothing about his own practical experience implementing these projects.
For organizations looking to expand their data analytics efforts, it’s not clear that the author’s recommendations would translate into dollars wisely spent.
For example, in his chapter about data informed decision-making, Morrow includes a six step plan for implementing a data informed decision-making framework: decide, integrate, analyze, acquire, ask and iterate (Morrow, 159).
On a high-level, he describes each step and how they relate to the four levels of analytics and the three c’s of data literacy but leaves out other major aspects. Within the first step (ask) he includes three examples of bad ways to ask questions of data, writing:
“With each of these headlines, we should be asking questions- they are funny after all” (Morrow, 161).
However, he doesn’t include ways of reframing these questions to form insight based on that data.
Another gap in the book is the importance of context. Thinking back to my own experience as a nonprofit analyst, not all users have access to the same resources before starting in their roles. In fact, some people (such as those from historically marginalized communities) have been negatively impacted by analytics and data.
The identity and individual needs of the users are critical to a successful data literacy program since, at the end of the day, businesses rely on user success to meet their goals.
Data literacy efforts should be framed with the goal of empowering users and making them excited about data.
I would have liked to see a chapter devoted to understanding the business context and strategies for how to relate to different users.
In one of the few real life examples the author does provide, he explains a time when a user didn’t refer to his team’s data dictionary and used the wrong metric:
“The individual did not consult the data dictionary we had built to find out how we were defining certain metrics. By doing this, they put up a roadblock to getting the right answer, because they did not get the information pulled according to the correct definition” (Morrow, 86).
The author doesn’t see this as an opportunity to include the user as part of the solution.
For example, if this individual used a different metric, why was it different from his team’s data dictionary? What does this discrepancy say about the individuals’ experience with data? Does it indicate that the implementation of the data dictionary has room for improvement?
I would have liked to see a greater compassion for the experience of end users and self-reflection from the author in terms of his own identity.
If the goal is to democratize access to data, Morrow would have benefited by including a portion on how the identity and experience of the end user (as well as the practitioner) impacts their own learning.
Final recommendation
Overall, Be Data Literate helps readers new to the world of data analytics learn high-level strategies for framing their work and making data literacy efforts accessible to a wider audience.
I found the book’s structure and repetition helpful for understanding foundational concepts such as the four levels of analytics.
However, Morrow doesn’t focus on implementation or the needs of the user.
The content would have been stronger if the author had explicitly addressed his own limitations and the additional considerations needed to develop a successful analytics strategy.
I give this book a 6/10.
