If one were to create a simulation to illustrate how 1,000 Americans spent 24 hours, what might that look like? How might the information be used to design informed marketing strategies?
Nathan Yau, a statistician, conducted this experiment and generated a stunning visual accompanied by an interesting narrative on Flowing Data. Data visualization is what converts raw data into an comprehensive story. It helps uncover patterns and insights that are otherwise challenging to decipher. Food safety data coupled with data visualization tools can help decision makers pivot their safety and quality strategies, and effectively respond to extrinsic variables, such as social, environmental, economic, or even political changes.
Let’s explore a few data visualization best practices to leverage food safety initiatives.
Skip the Pie
While the effective use of pie charts remains a debatable subject, my recommendation is to use an alternative instead, and here’s why: It is challenging to comprehend the difference between anything with more than five categories. Our brain’s ability to compare the difference between angles at close range, such as the difference between 35 degrees and 40 degrees, is quite limited. This explains why comparing two or more pie charts is more frustrating than comparing two or more graphs. Here’s a great resource to dive deeper into the science of using the right visuals.
Scrub Your Data
Data is the foundation upon which rests key information, and how that information influences strategy development. Cleaning the data to eliminate errors or identifying missing pieces are critical first steps. Establishing the right data points are equally important too. For example, in the world of compliance and safety trainings, more organizations must now choose between learning management systems and in-person training providers.
Traditionally, the factors that determined which training service provider to work with would have been based on the learners’ experience, instructor capabilities, content design, price points, etc. In the world of virtual work, other factors must be taken into consideration, such as platform compatibility, security features, and proctored exams. Not measuring the right elements could also result in making miscalculated decisions.
Know Your Audience Better than Your Subject
The difference between data visualization content that is engaging and content that is ambiguous is the depth of connection an audience can build with the material. Food safety data coupled with storytelling skills help the audience members connect at a personal level. In fact, not understanding the needs of the audience members thoroughly and not being able to convert complex subjects in to relatable and easy-to-digest bites of knowledge are a few reasons why there exists a disconnect between the scientific and the non-scientific communities.
Share, Share, and Share Some More
Learning happens best when the learning experience is shared. An example of shared learning (and service development) is the collaboration between Twitter and the Chicago Department of Public Health. Foodborne Chicago is an app that utilizes a machine learning algorithm to identify and track users who tweet about possible cases of food poisoning. The rationale behind this approach is simple: People may be hesitant to see a doctor about their ailment, but they are quite prompt to share their experiences on social media, particularly on platforms that encourage establishment reviews. This example illustrates how a food safety initiative was built by bridging the world of data analytics and social media.