Part one of a six-part series on the application of design thinking for data practitioners, business intelligence analysts, researchers, and anyone working with data.
Jason comes from a design background. Mary comes from a user experience background. Jason is a ‘data IS’ person. Mary is a ‘data ARE’ person. Inevitably, during our work with Nightingale, we share thoughts on our day jobs and the perspectives of our authors. It was in these conversations that this theory began to form.
In the spirit of collaboration, we want to hear from you! We’d love to hear your thoughts via the three questions at the bottom of this article!
Humans are culturally conditioned to absorb a lot of information, combining different types of media and interaction patterns. Often accompanied by illustrations, photos, videos, and text, this information is intended to communicate complex ideas in a way that is clear and potentially interesting.
Many of us use data to find some kind of objective truth. The collection and analysis of data has become a huge part of our business environment and culture, but collecting data is only as good as how we communicate it, so we regularly turn to data visualization to give shape and meaning to the information.
The relationship between data and information can mean many different things to different people. We use data to inform across a variety of instruments and media, from dashboards and findings reports, to data journalism, digital platforms, and smartphone apps–all of these leverage data to create information.
Despite the growing prominence of data visualization, our practice is surprisingly light on process. While there are many methodologies across industry, discipline, and design, they are mostly siloed by functional area and expertise. In order to understand how to communicate data in a meaningful way, an end-to-end process needs to be adopted to present the full context of the information to its intended audience. We call this inclusive process Information Empowerment, but let’s take a step back first to explore some background.
If data is a foundational currency, how do you spend it responsibly?
Data is the foundation to gaining wisdom as a basic unit of information. In order for knowledge to manifest itself and influence outcomes, it needs to be understandable and easily communicated. Therefore, to evaluate, understand, and engage with our digital reality, we must improve our ability to interpret and communicate data. Our impact is rooted in data.
In order to communicate our data to an audience, we need to know who they are and speak to them in a language they understand. As Brookings Institute’s Alex Engler explains, “Even when well-articulated, the private sector applications of data science can sound quite alien to public servants. This is understandable, as the problems that Netflix and Google strive to solve are very different than those government agencies, think tanks, and nonprofit service providers are focused on.” In other words, there is no such thing as a general audience of “everyone.”
Converging principles
Across sectors and functional roles, there are many different principles that propose paths to sense-making. Among these are design thinking, co-creation, design for all, storytelling, and on, and on. Additionally, there have been many approaches for more equitable and intentional data handling often rolled into the heading of data literacy.
Around the world, we see a commitment to user and customer-centric practices, and likewise, among data practitioners, recognition and acceptance of the complexity of data ethics have advanced. This sentiment is well expressed by visual storyteller Catherine Madden, “Data are people. Paid for by people. Collected by people. Analyzed by people. Shaped by people. Even with the best intentions data can’t be 100 percent objective, but we can prioritize equity at every step of the process and be transparent about the limitations.” So prioritizing for communication means putting people at the center of our ecosystems (customers, employees, residents, etc.) and appreciating the value of our human capital.
One of the groundbreaking ideas in Giorgia Lupi’s Data Humanism manifesto is the relationship between the data collected and the action taken by a human that is being recorded in the data. She elaborates, “Data represents real life. It is a snapshot of the world in the same way that a picture catches a small moment in time. Numbers are always placeholders for something else, a way to capture a point of view—but sometimes this can get lost.”
Likewise, anyone who has worked in data can tell you it can easily be misrepresented. Advances in data ethics, such as the intersectional approach by Catherine D’Ignazio and Lauren F. Klein in their book Data Feminism, are helping to expand our attitude towards data collection and to inform more inclusive data science practices based on core data literacy tenets.
Data literacy poses a particularly stubborn obstacle to realizing many of these principles. Some data literacy challenges can be traced to deficiencies in physical, technological, and cognitive areas. Infrastructure problems arise from legacy systems, ever-changing technologies, and the demands of rapid upskilling which can lead to significant difficulty in comprehension and accessibility for the intended audience.
One way to address physical and cognitive accessibility is to borrow from architects and urban planners who strive to design for all. Data journalist, Mona Chalabi, practices this approach by “designing for the least-informed reader first.” And, economist Raj Chetty “goes to great lengths to make his research accessible. He’s not just speaking to other researchers…He’s presenting information in plain language, in a visual format that one can understand within seconds.”
Borrowing from other disciplines and sectors like these is a means to achieving a broader perspective. Jason and Mary each have experience employing a method called combinational creativity, which involves sourcing existing ideas, and our own consulting experiences, to develop a practical process we refer to as Information Empowerment.
In our next article, we’ll outline this process for combining these various approaches into a high-level framework to guide teams towards mitigating these challenges–so stay tuned–but first, we’d like to know…
What do you think?
- How do you think about making an impact in your work? To what extent is data communication a goal for you?
- What kinds of challenges have you encountered trying to make an impact in your work?
- How have you borrowed from other industries or ideas to meet your objectives?
Share your thoughts with us at nightingale@datavisualizationsociety.org.
For 20 years, Mary Aviles has stewarded projects driving strategy and content, human experience, concept development, and systems change. A graduate of the University of Michigan, her work has spanned the business-to-business, health care, and nonprofit sectors. Mary is a mixed-method UX researcher at Detroit Labs and the managing editor of Nightingale. She writes about dataviz in real life (IRL) in an effort to help practitioners and “non-data” people enjoy better understanding and experiences in their shared ecosystems.
Jason Forrest is a data visualization designer and writer living in New York City. He is the director of the Data Visualization Lab for McKinsey and Company. In addition to being on the board of directors of the Data Visualization Society, he is also the editor-in-chief of Nightingale: The Journal of the Data Visualization Society. He writes about the intersection of culture and information design and is currently working on a book about pictorial statistics.
