During our first week of school, our Data Visualization class collaborated on a dictionary of related terms in an attempt to create a common language to talk about data and journalism. One of those words was “sensor,” defined by students as: “a device that is used to pick up and respond to various physical inputs in the environment, including pressure, moisture, heat, light, etc. Thermometers, motion detectors, smoke detectors and microphones are all examples of sensors. Sensors can be used to gather a wide array of data, and are becoming a more common tool used by journalists, paving the way for Sensor Journalism, a now up and coming form of news reporting.”

For me, that word was very intimidating. Overall, I just saw it as “science,” which is a scary field to any Emerson College student who relied on their lab partner to pass high school chemistry. However, in our class discussions, presentations, and our latest experiment with water conductivity sensors, I’ve become more open to the idea of me (a writer, a “right side of the brain” person) using “left side of the brain” skills in order to collect and analyze data, and ultimately create stories out of them.

In fact, this pairing of seemingly opposite skill sets makes sense. According to the Tow Center Report on Sensors and Journalism, “Sensors are a way of collecting information about the world. Journalists trade in acquiring information, analyzing it, organizing it, and distributing it. That alone suggests a natural fit.”

And really, whether we like it or not, journalists are going to have to use data in their reporting to keep up with competition in a market where the reader craves data-driven stories and is increasingly reliant on visuals.

The Tow Report suggests that now more than ever, “There are more stories to be found in databases and more journalists working in the profession with the interest and skills to find them.”

So, what is the promise of storytelling using data collected with sensors? The opportunities are endless. Sensors can help detect and measure air and water quality, public health, deforestation, drought, and even noise pollution (as Lily Bui mentioned out in her presentation to our class in the Emerson Engagement Lab).

Matt White also points out in his piece on sensor journalism for Poynter that, “One of the things that intrigues me about sensor journalism is its potential for crowdsourcing — letting people help sense changes in their local environment. We can build ideas that let people know data about their own location, and feed that data into a greater whole.”

With instructions on how to make sensors becoming readily available online, it creates a shift in power. Where citizens and journalists were once limited to the information the government was willing to invest research in and publish, the power is now in the hands of curious people investigating and measuring anything they’re interested in. This means that the public can hold others accountable with support from their own data research.

But of course, it’s not so simple. Science can be botched by scientists themselves, and while some sensors (like the water conductivity sensors we created in Emerson’s Data Visualization class), are not extremely difficult to construct, it’s possible that DIY sensors could be faulty, and therefore the data made unreliable or even inaccurate. For instance, our water samples yielded different results with different water conductivity sensors. If we had neglected to use a controlled probe to test each water sample, the results would’ve been useless. It’s also important to note that it’s not always clear how much data is enough to make a claim about, for example, water quality. While we tested conductivity, one element of water quality, there are several other elements involved with water quality like specific contaminants and temperature. Length of experiment and the extent to which it is controlled are also aspects to consider.

Also, as we have discussed in class, even if the data is solid, it can be interpreted many ways based on how the data sets are presented and the journalist’s own biases.

In terms of journalistic and technological history, sensor journalism is a very new field, and we should proceed with caution when reporting assertions based on self-collected data. Ideally (and as we’ve seen in case studies in Data Visualization), there would be a team of people working on a data-driven story, including journalists, researchers, topic-specific experts, scientists, graphic designers, and more. While data journalists are using both sides of their brains, two heads are still better than one, and five heads are even better when it comes to complex issues. I believe data journalism should be a collaborative process with checks and balances because while beautiful data visualizations can evoke a lot of authority and be enjoyable for readers, it is crucial that the information being presented is vetted and fact-checked just as it would in a print news story.

—Christina Jedra