To show how the visualization of data can bring it to life
- [Narrator] I often look at great visualizations of data, and I'm impressed by the creativity and beauty of them. Designing these visualizations is a wonderful skill. It's a combination of art and science. While most of us will never have to create an infographic or elaborate data story, we might have to work with those that do, and help guide them, and provide feedback. In this video, I'd like to briefly describe five core concepts to a visualization. The first concept is integrity. Essentially, this means that the graphic should be an accurate reflection of the underlying data.
When converting the data to a graph, there is a risk that the audience can be mislead. Usually, this is unintended. For example, let's imagine we're trying to convey that there are less doctors available to serve a population as it grows. On a chart, this could be represented by showing the number of doctors as a percentage of the population, serving each year over a period of years. If the graph emits that the population is growing, the graph now looks like the number of doctors is dropping. This will be incorrect.
The real integrity of the data is that the number of doctors has remained the same, but the population has grown. Ensuring this is communicated is called visualization integrity. The next concept is for a visualization to be meaningful. This means that the manner in which we create a visualization from data must resonate with the audience. For example, when we use pictures to help people understand types of data, those pictures should be relevant. For example, and infographic about the banking industry should be replete with typical banking images.
A bar chart, for example, should be made up of stacks of coins. Likewise, a visualization about social networking should include a variety of images of connectivity. Finally, for a visualization to be meaningful, it should try to connect with the audience both emotionally and intellectually. These can both be achieved through understanding the audience, focusing on what really matters, and by provoking a reaction. The third concept is simplicity. We already discusses that one of the main purposes for a visualization is to make complex data easier to interpret.
The last thing we want to do is create a graphic that makes it harder to understand the data. We should avoid including too many graphical metaphors, and being too clever by communicating too much in one graph. Let's keep it simple and clean. We can always create another graph if there is another idea we feel obligated to communicate. The next concept is ensuring the visualization is relevant. As a concept, this is quite literal. A visualization should not slice the data as to almost communicate the irrelevant.
Put another way, data can be communicated in many different ways, but it doesn't mean it is relevant to the audience. For example, let's say we were communicating growing revenue received in each quarter over a year. As an international business, we may be getting payment in different currencies. For this graph, we would chart the increases in revenue of each quarter, and we may even display it by its country of origin. What would be less meaningful in a visualization will be to put focus on the different currencies.
Sure, that's a fact, but it's not the core message. And it's really not that relevant. Finally, let's make our visualizations beautiful. They should be great to look at. An excellent visualization is close to a piece of art. Use boldness in color. And equally have lots of white space and clean lines. This is the tough work. And really what distinguishes good visualizations from great visualizations. Get lots of feedback, and then ask, "Is this visualization beautiful?"
Dr. Jonathan Reichental introduces real-world use cases for open data, as well as the steps you need to take to develop and operationalize an open data program. He also explains how data scientists use open data to tell stories and drive data visualizations. Along the way, he provides numerous examples of open data in action: improving government, empowering citizens, creating opportunity, and solving public problems.
- Understanding what open data really is
- Current open data efforts around the globe
- Open data in action
- Designing an open data governance process, including policies
- Monetizing open data
- Storytelling with open data
- Selling the value of open data
- Measuring the value of open data