This video provides examples on how data can be visualized through the use of stories.
- [Instructor] Google's chief economist, Doctor Hal Varian, has stated, "The ability to take data, to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it, that's going to be a hugely important skill in the next decades." And I'll discuss some qualities that are important to consider beyond good visualization techniques and the combination of data, visuals, and a narrative. Here are three considerations. First, make sure you're using the right data.
Seems obvious, right? Not always. Perhaps you don't have the right data or the complete data to tell the story. Be judicious in evaluating whether the data is appropriate. For example, is the data too old? Is our interpretation open to question? Sometimes you may need to find a different data set. Having the right data is always the first step before creating a data story. Second, synthesize. This means that we might use a combination of data sets, contemporary ideas, history, and other characteristics to reach some conclusions.
Taken individually, one quality may not be enough to successfully tell a story. Synthesizing means taking the individual parts to make the whole. For example, if the story that is being told is about how something has changed over a period of time, we may want to use historical data that is then contrasted with current data. Let's say, if we wanna show that income has increased between two time periods, but buying power has not increased. We could use the example of how home prices have increased faster than income.
Using a mix of housing pictures and income as a percentage of the monthly mortgage, this may more easily communicate this complex information. The final consideration for data story is to make it personal and real. A data story that is abstract, say relies too heavily on hypotheticals, won't resonate with many people. Let's use the example of open data on transportation issues. Rather than building the story about a typical city or a big famous city, keep the data story exclusively connected to the city that the data belongs to.
To add additional impact, illustrate the consequence of that data on an individual family in that community. This makes it personal and real. And it'll be so much more valuable. To summarize, visualizations take data and make it into a compelling picture. Data storytelling takes visuals, data, and a narrative and uses a structured approach to communicate an important insight. It's highly useful and the marketplace will have a growing need for these skills. In open data, particularly when it's used in a community context, we're going to have to tell a lot of stories to persuade, to elicit change, to make decisions, and to enrich our democracy.
With open data and data storytelling, we can really begin to change the world.
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