Molly Poppie: Bringing People and Data Together
<!–tasks—from–>It shouldn’t come as a surprise that people at Nielsen have a healthy obsession with data. And Molly Poppie, our vice president of data science, is a prime example. She and her team of 50 data scientists play a crucial role in developing new methodologies for data collection.
“You really need to understand the math and the statistics behind everything in order to figure out how we can use the data,” Molly says. While this sounds clinical, Molly stresses that there’s also a major sociological component that’s a critical piece of the measurement puzzle. “At the same time, you really need to think about what’s important—how people behave and what sort of behavior is actually going to be useful for us to measure.”
“You really need to think about what’s important—how people behave and what sort of behavior is actually going to be useful for us to measure.”
At Nielsen, we believe that data is more than just numbers. We measure the behavior of real people in the real world, and as new trends emerge and technology evolves, that behavior is constantly shifting. The data we analyze everyday is dynamic. As a result, our vast team of data scientists need to focus on the humans behind the numbers.
Molly has proven herself to be quite adept at understanding both data and people, whether it’s those we measure or the employees she leads.
The Intersection of People and Data
The media world is changing. Media fragmentation is spreading viewers across devices, and the amount and kinds of data sources are growing. So understanding the diverse people underpinning this data is increasingly important in this quickly evolving environment.
For Molly and her team, that means constantly thinking about how people and data are interconnected—even outside of work. “If I’m walking around the mall, I think: ‘Are we capturing that exposure?’ Or, ‘Do we understand why people are buying that item?’ And if we’re not doing that, could we and should we?’”
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Molly’s nearly 10-year career at Nielsen has helped train her to focus on the points where data meet real life, from measuring consumer purchasing behavior to media measurement.
“Right now, I’m focused on how people sell advertising,” she explains. “But having a background in who’s buying those products and what metrics are important is huge.”
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Those are all clear wins, but the real success is what you can’t tangibly measure. Smaller devices and shorter installations improve panel cooperation and boost data quality. They also make it more cost-effective to increase the size of television panels where and when needed—a crucial piece of the solution to address media fragmentation today and into the future.
The Nano meter is built for the future, which is increasingly connected, especially throughout our homes. Using Bluetooth and Wi-Fi technology, the Nano meter can communicate with other elements around the house—such as wearables, smartphones and even a new breed of streaming meters developed by Nielsen to capture over-the-top (OTT) and broadband content consumption.
Building Data Models and Careers
Molly’s career path at Nielsen has also encouraged her to build her team somewhat unconventionally. Rather than scouting out purely media-focused data scientists, Molly has blown up the traditional hiring process and expanded the scope of our applicant pool.
“Within data science, as well as within the company as a whole, we’re focusing on bringing in people with different backgrounds,” Molly says. “That’s really important to me—to have a diversity of mindsets and skill sets on my team….We’re really finding a lot of people that can bring a lot of value from a lot of different places.”
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Molly’s team now includes associates who have worked at places like CERN (the European Organization for Nuclear Research) and the U.S. Department of Defense. She even hired someone whose past life was in fishery sciences. But that’s exactly what Molly wants—people who come to the table with different perspectives and ideas. This diversity of thought is essential when considering how to measure consumer behaviors in today’s fragmenting media environment.<!–
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For example, when brainstorming Total Audience Measurement model improvements recently, one team member suggested incorporating information about which room people are watching content in, raising the question: “Do consumers’ media behaviors differ if they’re in their bedroom or a common area?” As a result of this new perspective, Molly explains that the team was able to make a stronger model—one that will ultimately make Nielsen’s media measurement solution stronger.
The Eureka Data Moment
This symbiosis between data and people that underpins all Molly does at Nielsen is something she also works hard to help clients understand.
“When we’ve talked to clients about our methodologies, what we’ve seen is that a lot of them have had moments where all of a sudden they say, ‘Oh, that’s really cool.”
“When we’ve talked to clients about our methodologies, what we’ve seen is that a lot of them have had moments where all of a sudden they say, ‘Oh, that’s really cool. We never thought that would matter, and it really does.’ And so I think it gives them a sense of confidence in what we’re doing. We’re putting a lot of thought behind it and making sure that we’re really thorough in our research.”
Molly says it also encourages our clients to seek more insight into the people underpinning the data.
“They want to know ‘Does that person shop at a certain store? What does that person watch on TV? When do they use their mobile phone versus their TV? Do they consume content on their tablet? What content is that? That additional level of detail is something that they find really powerful.”
“Thinking about the amount and the different sources of data that we could potentially harness makes me excited to get up in the morning and go to work.”
As more and more data sources, devices and services crop up, Molly is thrilled by the possibilities: “There’s so much data out there. People have even started to ask, ‘Are we measuring if somebody can surf the internet using their refrigerator or their dishwasher?’ Thinking about the amount and the different sources of data that we could potentially harness makes me excited to get up in the morning and go to work.”
So what’s next? Data, sure, but perhaps more importantly, the right people—whether they’re from a fishery or a fashion company—to work as a cohesive team with a diversity of thought.<!–
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