This post was originally posted on the Lab Without Benches blog by Boaz Vilozny.

Early in April I found myself at a hotel in Downtown San Francisco, surrounded by experts in the field of internet marketing. I was not lost; I went there to learn how to be a better scientist. Let me explain.

Lab Without Benches exists to promote good communication as a career skill for scientists. This has been made easier with a growing number of resources on communicating science and presenting data. I’ve been amazed at the dedicated community of scientists and journalists that work to make science relevant to a diverse audience. Most resources, however, are focused on communicating academic research. Looking for industry examples of communicating with data led me to digital analytics. This branch of internet marketing relies heavily on data analysis and hypothesis-testing, which sounds like a scientist’s bread and butter. Unlike most scientists, digital analysts routinely present quantitative data to non-technical audiences. And being part of  the marketing world, they take their presentations seriously.

Of the talks at the eMetrics conference, nearly half were on methods for becoming a “data-driven” organization. Essentially, this means getting an entire company to objectively evaluate itself: Identify meaningful metrics that indicate the group’s progress, use observations to generate hypotheses on how to achieve a desired outcome, then test those using the agreed-upon metrics. Scientists will recognize this as the scientific method: observation, hypothesis, experiment. Still, few in the digital analytics community would call themselves evangelists for science. They are simply trying to get an advantage in a competitive field by using data effectively.

At first, most marketers I met were confused as to why a chemist  would be interested in digital analytics. When I told them I came to learn about good communication skills, their faces lit up: “Oh, wouldn’t it be great if scientists could learn to talk about what they do to normal people?” One woman told me her brother in law was in graduate school for biochemistry: “I’d love to know what he does, but he’s never been able to explain it.” I couldn’t help but feel a little embarrassed every time I heard stories like that. And I heard them a lot.

The marketers again became confused when I explained my main problem is teaching scientists to present data effectively, mostly to other scientists. This was something they couldn’t understand. Why would scientists have trouble talking to other scientists? There was an assumption that because they are smart and data-oriented, scientists had long ago figured out how to effectively share ideas and data amongst themselves. The scenario of a bunch of people looking at a confusing graph and trying to make sense of it is very familiar to a marketer. But marketers could not imagine this happening among a group of scientists.

The problems in communication a scientist faces are in many ways similar to those of a digital analyst. An analyst might struggle to explain to a marketing team why more web page views don’t necessarily lead to more sales. A chemist may have to explain to the manufacturing team why a certain material won’t have the durability they need. Each can present lots of data to support their argument, but no one really has time for that. How does one get beyond the argument of “Trust me, I’m an expert.”? In both cases, the challenge is in taking specialized knowledge and making it meaningful to the nonspecialist.

Analysts have an edge over most scientists in that communication is part of their job. Scientists often struggle to explain their own work to someone without the same background. In the case of PhD scientists, the problem is further complicated by hyper-specialization. I recently spoke with a graduate student studying kidney physiology in diabetic mice. His colleague in the same lab also studied kidney physiology in diabetic mice, but he couldn’t tell me any more than that; they worked on different parts of the kidney.

In contrast to scientists, marketers are expected to explain what they do to people in all fields. That was why I came to a meeting of digital analysts working in marketing. I expected to find data storytellers, experts at crafting knowledge into narratives to drive good business decisions. Actually, most analysts admitted struggling to communicate effectively. They were frustrated that their insights were not apparent to others, or often not implemented. The difference between this crowd and that at a typical scientific meeting was this: The digital analysts knew that their professional value depended on good communication, and they were there to learn the best techniques.

Digital analysts recognize that making an organization data-driven cannot be primarily about data. Instead, they focus on the insights learned by analyzing the data. For example, to learn how to increase conversion rate (the portion of visitors to a website who take the desired action, like subscribing to a newsletter) analysts may look at time spent on the site, the path a visitor took to get there, and lots of other metrics. Like many scientists, they may love the technical challenge of  collecting, analyzing, and making sense of the data. But in the end, the outcome that matters is telling a company what actions to take. An analyst will be successful if they can demonstrate their approach is better than a gut feeling.

As a scientist, there is a lot to gain from thinking like a digital analyst. Most people will never understand the details of our work, and that’s fine. Insights are how we create value. How many PhDs invest time helping nonscientists understand the insights from their work? How often do we give up and say, “They just don’t get it.”? The analysts at the conference took time they could have spent learning analytical methods, and invested it in learning to communicate the impact of their work. I can’t imagine that happening at a typical chemistry conference. I learned many, many valuable lessons from my two days at eMetrics. The links below show just a few examples. If these take you out of your comfort zone as a scientist, that’s probably a good thing. As for myself, I’ll continue looking for ways to share insights from data. Maybe I’ll see you at the next convention.

Here are a few highlights from my adventures in the digital analytics community. I consider these to be key resources on professional science communication. I’d love to hear from scientists: What are your favorite techniques when it comes to delivering insights?

Presenting Insights

Lea Pica (@LeaPica) hosts a great podcast on data visualization and presentation skills. It’s geared toward digital analysts, but I learn something useful every time I listen. When it comes to presenting data, scientists and analysts face the same challenges. In this episode, digital analytics pioneer Jim Sterne (@jimsterne) talks about the common mistakes analysts make when presenting data. To scientists, this will sound very familiar; and the take-home message is relevant to anyone who works with data.

Becoming Data-Driven

Tim Wilson (@tgwilson) talked about solving a common problem: how do we know what is important to measure in order to achieve business goals? This problem should be on the front of every professional scientist’s mind, and the slide deck here shows many of the key points from the talk. Tim also co-hosts a podcast called The Digital Analytics Power Hour.

Visualizing Data

Ryan Sleeper (@OSMGuy) showed some of the features of Tableau, a data visualization software that is widely used in the marketing analytics field. Compared to the science tools I’m familiar with, the graphs are visually dynamic and designed for interaction. Check out this graphic to show, both visually and statistically, the cause of concussions in pro football. Science visualizations have been improving recently, but we would do well to take some lessons from the marketing world.

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