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In the tech world, we often talk about how big data is remaking big business. But photojournalist Rick Smolan wants us to pay attention to how it’s transforming everything else.

The former National Geographic and TIME photographer, who is best known for the Day in the Life book series, has spent his career using imagery to document -– and inspire reflection about -– the changing world.

In his latest project, The Human Face of Big Data, he shares more than 100 stories (culled from an initial list of 1,000) that reveal the concrete, mind-blowingly powerful ways big data is changing how we consume energy, receive healthcare, monitor animal migration, track the weather and more.

As my colleague Derrick Harris recently wrote, the project doesn’t just include a book, but iOS and Android apps that collect data from people around the world and then let users compare information on beliefs and aspirations. (When the book comes out on Nov. 20, people will be able to search and filter the data to see global patterns.) This week, the project also hosted events around the world to bring big data innovators and journalists together.

In looking at the landscape, Smolan told me that he thinks big data is where the Internet was in 1993, but it stands to have an even greater impact on society. Part of the project, he said, was meant to help a wider audience of people understand why big data matters.

“I’m worried that the people thinking about this are corporations and the government and not ordinary citizens,” he said. “One of the goals was to get people thinking and talking about this world of big data while it’s still in its early formation stages.”

Take a look at five examples of how big data is improving everyday life:

Risk-screening for heart attack patients

Using discarded EKG data from heart attack patients, researchers from MIT, the University of Michigan and Brigham and Women’s Hospital created a computer model that can help predict which heart attack patients are at risk of experiencing a second heart attack within the year. Through machine learning and data mining they found three EKG abnormalities that correlate with a higher risk of a second attack.

The key is that while traditional screening techniques (which miss about 70 percent of repeat heart attack cases) look at just 30 seconds of a patient’s EKG, the researchers’ model enables doctors to examine hours of EKG recordings to spot the high-risk indicators.

‘Magic Carpet’ patient monitoring

Developed by researchers at GE and Intel, the Magic Carpet prototype uses sensors in a home carpet to monitor the activity of seniors. It creates a baseline of normal movement -– from the usual time people get out of bed to the speed and pressure with which they walk -– and when it senses an abnormality, it sends an alert to loved ones. For now, wireless sensor-enabled system is too expensive for most people, but Smolan said that the basic idea is already evident in the Quantified Self-type gadgets that are gaining popularity. While some might think of self-monitoring devices as narcissistic naval-gazing, he said, they make people more aware and willing to change their behavior, which can improve their health in the long term.

Appliance-level home energy monitoring

As the presidential candidates debate how to rethink energy, perhaps they should consider this: the DVR consumes 11 percent of the average American household’s total power. That’s what MacArthur Fellow and University of Washington computer science professor Shwetak Patel learned through a device he developed.

Once it’s plugged in anywhere in a home, the sensor, called ElectriSense, can infer the unique digital signatures (the frequency of the electromagnetic interference) for different appliances to help homeowners see which appliances are the biggest energy hogs and learn how to conserve.

The technology isn’t yet available for purchase, but Belkin International has acquired the technology and the researchers expect them to release a commercial product soon.

Understanding traffic patterns with GPS data

When it rains in Singapore, taxicabs are nearly impossible to come by. But it isn’t because they’re all full carting people around, it’s because the drivers have pulled over.

Through a study from the Singapore-MIT Alliance for Research and Technology (SMART) comparing weather patterns and taxi activity in the city, researcher Oliver Senn realized that, counter-intuitively, cab drivers pull to the side of the road in inclement weather.

When he dug deeper, he realized that the reason stemmed from a decades-old taxi company policy that requires drivers to put up a $1,000 bond when they’re in an accident. When the policy was initially implemented, drivers could be cleared and returned the money the next day. But now that it can take months for drivers to get their money back, they choose to play it safe and stay off the roads when the risk is highest.

The policy is now being questioned in Singapore, and it shows how powerful data can be in helping cities around the world better understand and improve urban movement.

Early weather warnings

Weather alerts on the television and, increasingly, our smartphones are common in the U.S. But Earth Networks, the company behind the WeatherBug App, says that of the 7 billion people on the planet, 6 billion have never received an alert that could help them avoid severe weather.

Through the company’s tens of thousands of sensors around the world, it monitors temperature changes, wind and lightning to give people early alerts about inclement weather. Lightning, in particular, the company said, is a valuable leading indicator of severe weather and, through its growing network of sensors, it’s working to bring alerts to places like Africa, South America and Asia that don’t currently receive the same level of advance notice.