Data visualization is widely used in nutrition to summarize and communicate nutrition data. There are several aspects of data visualization that make it a viable strategy to neatly collate information and (hopefully) provoke action: (1) humans more rapidly process visual information compared to text; (2) design principles can be applied to attune with humans’ short-term memory; and (3) research has established connections with data visualization and sensemaking. However, a seemingly thought-provoking and well-designed graph, dashboard, or scorecard does not necessarily translate into evidence-based decision-making. Many of these products can quickly become unused because they do not meet the needs of targeted users, providing more support that “the graphic is only as useful as the audience finds it.”
How can we avoid a data visualization graveyard? Putting user needs at the forefront and designing with empathy are key, including understanding data visualization literacy and data literacy of targeted users. This blog post provides an overview of data visualization literacy and a preview of a study that DataDENT is currently designing to explore data visualization literacy and preferences among nutrition stakeholders in Nigeria.
Defining data visualization literacy
While a common definition for “data visualization literacy” does not exist, there are two primarily components: (1) producing data visualizations from data (“construction”) and (2) interpreting and translating information from data visualizations (“interpretation”) [1]. Data visualization literacy is distinct from data literacy, which is broader and includes skills of data collection and management [2]. Data visualization literacy and data literacy are often overlooked when visualizing nutrition data. However, this is hardly a surprise given how little we know about data visualization literacy and data literacy. Existing studies on data visualization literacy have only been conducted in high-income countries with data that has little relevance to the nutrition community. Furthermore, the only available estimate of data literacy in any low- and middle-income (LMIC) country is led by the Data Literacy Project, which has quantified data literacy among professionals in India.
Data visualization and the Nigeria nutrition data value chain
In Nigeria, the nutrition data value chain has strengthened over the last several years with investments in new data collection (e.g. annual National Nutrition and Health surveys, new nutrition DHIS2 indicators, Food Consumption & Micronutrient Survey) and development of new/existing monitoring and accountability tools (e.g. Nigeria Governor’s Forum nutrition scorecard, Nigeria Food Systems Dashboard, Federal Ministry of Health Multi-Source Data Analytics and Triangulation Platform (MSDAT)). These monitoring and accountability tools are at various stages of development and rely on data visualization strategies to organize and communicate data.
Overview of proposed study
Given the importance of data visualization to the Nigeria nutrition data value chain and limited evidence on whether data visualization approaches align with targeted users data visualization literacy and preferences, DataDENT is currently designing a study to explore data visualization literacy and preferences among Nigerian stakeholders that work in nutrition. Findings will help inform how accountability tools and communications products for decision makers in Nigeria can be optimized to meet audience needs. Modelled after the Johns Hopkins University team’s study on data visualization preferences among health decision-makers in Tanzania, the proposed study in Nigeria will task respondents with interpreting different examples of visualized Nigeria nutrition data against data visualization literacy competencies identified by data visualization researchers. We also hope to gather insight on preferences for certain data visualization types—especially approaches to visualizing new nutrition indicators. We plan to disseminate findings in a webinar open to the public and do targeted outreach to stakeholders to encourage uptake of findings. If you are interested in learning more about the study, please contact Tricia Aung (taung4@jh.edu).
[1]. Börner K, Bueckle A, Ginda M. Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments. Proceedings of the National Academy of Sciences. 2019 Feb 5;116(6):1857-64.
[2] Wolff A, Gooch D, Montaner JJ, Rashid U, Kortuem G. Creating an understanding of data literacy for a data-driven society. The Journal of Community Informatics. 2016 Aug 9;12(3).