누구나 사용할 수 있는 분석 도구
누구나 사용할 수 있는 데이터 준비 도구
조직을 위한 분석
조직을 위한 클라우드 분석
Dr. Hannah Fry has a confession to make. She has not always loved data.
Fry is a lecturer in the mathematics of cities at the Centre for Advanced Spatial Analysis. And it makes sense that she would be drawn to math’s clear logical proofs.
“Mathematics is clean, and neat, and ordered, and elegant. Data? Not so much,” said Fry during her keynote speech at Tableau Conference.
Fry admitted that for much of her career, she was happy to stay inside with her equations and not venture out into the measured world of data.
“I was still quite skeptical about getting my hands dirty,” said Fry.
But then she changed departments, and her new colleagues were visualizing data and gaining insights into complex social patterns. Fry’s work with her new team changed her outlook on data, math, and their relationship to each other.
Fry describes two universes: the physical universe and the mathematical universe. Data and the visualization of data is the bridge that allows for insights to be found that the use of mathematics alone would not find.
“Data can tell you a story. The right data looked at in the right way lets you pick up on the traces of ourselves that we leave behind and glimpse the patterns of human behavior,” she said.
She looked at bicycle usage across London and visualized the information to identify some odd spikes in the data. As it turns out, bicycle stations at the top of a hill resulted in bikes abandoned at the bottom of that hill, and would be periodically retrieved by truck. The bottom line here is to not put a bicycle station at the top of a hill.
In another project, Fry observed transportation usage in London. She identified one remote station that had considerably more trouble than the other stations if there was a problem in the morning commute. It was a relatively small station with high train traffic, but few alternatives for commuters who may be stuck. Worse, the flood of underground riders descending on the next stop would then cause problems at that station, and so on down the line.
“The data itself may be kind of messy, but the insights it can offer certainly aren’t. You have to imagine how valuable that piece of information is to the people who run the transport for London,” she said.
Fry’s initial misgivings about data were overcome at this point.
“This is when I realized how profound data could be if you examined it properly," she said. "Data is the starting point for a deeper mathematical analysis. Data isn’t the end of the story; it is just the beginning.”