It is easy to think of data-driven storytelling as being comprised solely of numbers and figures, and sometimes that is the case.
However, many of the exemplary data-driven pieces out there also contain a strong human component. In fact, several outstanding pieces make humans the centerpiece of the story.
Journalists can utilize humans in data-driven stories in at least three important ways: to exemplify data, to bridge gaps in the data, and to contextualize a journalist’s analysis. Often, data journalists will incorporate all three of those into their reporting.
Statistics, and especially large numbers, often seem very abstract to people. This might lead audiences to gloss over that number. In contrast, a powerful anecdote about how a person, and the people around them, have been affected by some phenomenon can do a far better job of capturing the attention of the reader, viewer, or listener.
The best data-driven stories do not just focus on anecdotes or the quantitative information. Instead, they aim to blend data analysis and the human experience seamlessly, using one to illustrate the other.
For example, an analysis indicating that 5.1 million Americans will be suffering from Alzheimer’s disease this year might give a reader a momentary pause. “That’s a big number!” they might think. However, it may fail to register beyond that (and it often does). Consequently, audiences will forget that statistic in short order, and the story’s impact ends up being limited.
In contrast, a compelling story about how one family became closer as they relocated to help their father cope with Alzheimer’s and another family fell apart as the responsibilities became too big a burden is far more likely to draw an emotional response from different audiences. That emotional response, in turn, is more likely to leave an imprint on a person.
However, by introducing those two compelling stories and highlighting that they are just two of over 5 million such stories in the U.S., the journalist is able to both capture the attention, create a human connection, and underscore the massive scale of the issue. The blending of anecdotes and data analysis thus drives home the point that Alzheimer’s is a serious disease with wide impact. That point can be further illustrated by the use of other data points that capture the broader scope and impacts of dementia.
There are a great many things that have not yet been systematically quantified into publicly-accessible data, and a great deal of data that have become outdated. In such instances, human sources can be especially effective bridging gaps in a journalist’s data analysis.
For example, a journalist may be working with data on opioid overdoses and, in the course of their reporting, identify new drug treatment programs that have been launched by some of the towns most affected by overdoses.
Because those programs are so new, there may not be any data on their effectiveness. However, interviewing people who have been through the program can provide some helpful context to the gap in the story regarding whether or not these programs are likely to have a meaningful impact on the high incidence of opioid abuse in those communities.
It is important to recall, however, that the plural of anecdote is not data. Put another way, it is important that the journalist contextualize these anecdotes properly, and that they do not treat them as systematically collected and representative data. Nevertheless, such anecdotes can be used to fill in gaps in the data that journalists do have access to.
Humans can also be very helpful in contextualizing the fruits of a data analysis. This is especially true if the journalist intends to claim that some relationship between two entities (or variables) exists. It is doubly true if the journalist intends to assert that relationship to be causal in nature.
Journalists should always be careful when making relational claims simply because their data analysis appears to paint a particular picture. That’s because journalists are often asked to write stories about subjects they know of but lack intimate knowledge about. Thus, while they may learn a great deal over the course of the reporting, it is unlikely the journalist will become an expert on that subject by their deadline. Any relational claim made by the journalist may thus fail to account for important explanatory variables or confounding factors.
Human sources can thus serve as the experts that evaluate a journalist’s logic, challenge their assumptions, and point them to important variables the journalist may not have considered. Such sources may not even make it into the final story, but they can have a big impact on the way it shapes up. (This is often the case, when experts offer a background primer to a journalist.)
For example, a journalist may be working on a story on the cost of tuition at a large, public university in New England and observe that both tuition and the amount of money paid to retirees at that institution have gone up over the past decade. It may be tempting to create a connection between the two: Tuition is going up in part because of the cost of paying for the retirement benefits of university employees.
However, in chatting with an expert on retirement benefits, the journalist may learn that university employees at that institution belong to a state retirement system, and that the university only makes a small contribution to that system, the percentage of which has remained unchanged.
In using their subject expertise to identify a flaw in the journalist’s assumptions and logic, the human source has effectively spared the journalist from producing a misleading story. Additionally, that human source might be able to point the journalist to new story ideas about the challenges being faced by the state’s retirement system. (Similarly, an expert on higher education funding may be able to point to tuition pressures arising from declining state support for public universities nationwide.)
While data should be at the center of the data-driven reporting process, it can get lonely on its own. This course emphasizes data-driven storytelling, and the human component is often the most important part of a great story.
Additionally, while many great examples of data journalism use a few strong anecdotes to illustrate a compelling data analysis, several others use just a few findings from a sophisticated analysis to illustrate a handful of anecdotes.
Regardless of whether you choose to emphasize the data analysis or the humans in your story, be sure to engage with human sources throughout the stages reporting in order to not only refine your story idea but also make your story more impactful.