Sensors in the Age of Industrial IoT

By Catherine
Share on LinkedInTweet about this on TwitterShare on FacebookShare on Google+

By Catherine Bolgar

The use of sensors, especially in the Internet of Things, is creating a mountain of big data. This infographic illustrates how to manage the data generated from sensors, including best practices for identifying which data streams are useful as the volume of data continues to explode.

Click here to see a larger view


Catherine Bolgar is a former managing editor of The Wall Street Journal Europe, now working as a freelance writer and editor with WSJ. Custom Studios in EMEA. For more from Catherine Bolgar, along with other industry experts, join the Future Realities discussion on LinkedIn.

Harvesting data to feed the world

By Catherine
Share on LinkedInTweet about this on TwitterShare on FacebookShare on Google+

Written by Catherine Bolgar


In the 1950s and ‘60s, the green revolution sharply increased crop yields, thanks to fertilizers, pesticides and new seed varieties. But with a billion more mouths to feed by 2025, how will we reap more food without harming the environment? Big data might help.

The global agriculture biotechnology market is forecast to grow to $46.8 billion by 2019, with the bulk focused on transgenic seeds and synthetic biology products such as DNA synthesis and biofuels.

“Technology could improve yields and reduce waste,” says David Lobell, associate professor of earth system science at Stanford University in California. “One of the biggest impacts will be to bring down input costs. That will help not so much in terms of yields but in the price of food and the environmental impact—bringing down water use and fertilizer use.”

As you have better knowledge of what you need, you can reduce the margin of error.”

Genetics: Just as big data has helped scientists tease apart genetic traits in humans, so it is doing for agriculture.

Researchers are mapping the genomes of fungi, parasites, pathogens and plants, which can speed up breeding for traits such as salt tolerance. (About three hectares per minute become too salty for conventional farming.)

“The main idea of genomic selection is that effects of abiotic stresses like heat are controlled by lots of different genes,” Dr. Lobell says. “Those types of things can be better identified by more and more data for lots of different varieties. You can start to statistically pull out smaller effects with larger data sets.”

iStock_000047221908_SmallBig data is analyzing plant populations to understand better why some plants thrive in certain environments and others don’t. The Compadre database is a collection of more than 1,000 plant population models across 600 species, while the similar Comadre database is for animals. The data are difficult to collect, with researchers visiting the sites several times, notes Yvonne Buckley, professor and head of zoology at the University of Dublin.

By looking, for example, at how big and efficient leaves are, scientists hope to be able to predict whether a species will become extinct. “It’s important for food security, which populations might be vulnerable to disappearing,” she says.

Precision agriculture: Big data can also help farmers decide which seeds to plant, whether to apply fertilizers or whether to irrigate. With sensors, they can measure conditions such as soil moisture, while drones can provide a close-up view of far-flung fields in real time. Moreover, technology required to collect this data keeps getting cheaper.

“By monitoring what’s really happening, you can give people information and boost their food security,” says John Corbett, founder and chief executive of aWhere Inc., a Broomfield, Colorado, agriculture intelligence company.

aWhere analyzes temperature, rainfall, humidity (which can affect fungus and mold), solar radiation, wind and agronomic modeling. Its high-tech methods aren’t restricted to developed countries.

Farmer or agronomist in soy bean field with tabletThe cell phone is by far the most influential technology for dispersing information,” Dr. Corbett says. “The penetration of cell phones in sub-Saharan Africa is phenomenal. Any farmer can be connected to the world’s data bank. Without changing anything like seed or fertilizer, they can improve yields 30% just by using better information.”

aWhere delivers information to farmers in sub-Saharan Africa. In Kenya, for example, aWhere supplies weather data to iShamba, a for-profit agricultural advisory company that also produces a hit reality TV show, “Shamba Shape Up” (shamba is Swahili for “farm”) to answer subscribers’ questions and update commodity prices by SMS.

Cell phones can also collect data—aWhere surveys farmers by SMS. As the Internet of Things moves to the farm, tractors and other machinery will be able to transmit data from the field.

“If you can get on-the-ground information, and if you process it and push it back to the person, there’s an enormous amount of optimization and efficiency that will come to the agriculture value chain. Farmers can plan what will sell. They can form cooperatives, which make selling more efficient,” Dr. Corbett says. “If you do it across the value chain, the whole chain strengthens.”

Catherine Bolgar is a former managing editor of The Wall Street Journal Europe. For more from Catherine Bolgar, contributors from the Economist Intelligence Unit along with industry experts, join the Future Realities discussion.

Photos courtesy of iStock

How Medicine Makes Sense of Big Data

By Catherine
Share on LinkedInTweet about this on TwitterShare on FacebookShare on Google+

Written by Catherine Bolgar*

Big data for Medical

Big data is a game-changer for medical research. The ability to analyze vast sets of information, thanks to bigger and faster computers, is helping researchers to understand diseases, tease out genetic factors and spot patterns.

More researchers are looking at big data and understanding how we can utilize [it] in a better manner,” says Ervin Sejdic, assistant professor of electrical and computer engineering at the University of Pittsburgh, U.S., and founder of its Innovative Medical Engineering Developments lab.

In the past, clinicians would get data from patients and hold it up to metrics to try to see something by looking among different patient groups. “What they’re doing is flushing out the details. But the devil lies in the details,” Dr. Sejdic says. “The details are where we start understanding things. What’s really shifting in medicine is the fact that, yes, there is data, but let’s look at whole data sets.”

At the same time, better and smaller electronics, from smartphones to sensors you can wear, can compile more information at a detailed level and over bigger populations. “Researchers are looking at the interactions between different physiological systems. Sometimes these interactions break down in people with various diseases. Sometimes you have to look at the level of a minute, or an hour, or a day,” Dr. Sejdic says. “What big data is going to enable us to do is finally look at a human system as a system, rather than as individual components put together.”

Big data also is helping doctors and researchers to view diseases in shades of gray, rather than with a purely black-and-white outlook.

In the past, diseases were viewed in a simplistic way: a person is healthy or a person has disease. We would get specific information about the two states and compare the difference,” says Sergei Krivov, research fellow at the University of Leeds, U.K., who recently published research on the monitoring of kidney-transplant patients using big data techniques.

With transplants, he says, “There are two outcomes: perfect or problems. We are trying to find a single parameter to describe where you are between these two stages and what is the prognosis.” Based on the indicator, doctors can decide at an earlier stage whether to intervene into the process.

What I would like to see in the future is the following picture,” Dr. Krivov says. “A sizable part of the population frequently gives blood for analysis, for example during regular visits to their doctors. This would go to a data center. Based on this data for five or 10 years, we could determine indicators describing the degree of progression or the likelihood to occur for different diseases. We will give back this information as numbers, which is easy to interpret. This, in turn, will encourage patients to participate.”

One indicator patients might get with this approach is their biological age. “So you’re 30 years old, but your biological age is 20—or 40,” Dr. Krivov says. “Changes in your diet, exercise or lifestyle affect biological age. You might get younger, biologically. That would be reinforcement to the patient that he or she is doing well.”

DNA moleculeSome recent uses of big data include predicting the future of metabolic syndrome, advancing neuroscience, identifying dangerous pathogens, and conducting cancer research, among many others. DNA sequencing is getting cheaper thanks to big data, and genetic sequencing with big data is becoming a key part of epidemiology, because it helps trace chains of infection. Big data is helping researchers not only to understand the different genetic mutations in cancer, but also to personalize medicine: different mutations respond differently to treatments, and getting the right treatment straight away spares patients from side effects of treatments that aren’t effective for their particular kind of cancer.

However, challenges remain for big data to reach its full potential of analyzing many kinds of information from many patients. With computers, it’s “garbage in, garbage out,” so data needs to be structured to ensure consistency. Information often isn’t shared because organizations lack procedures or systems for communication. Advances in technology are helping to overcome some of those challenges, according to “The ‘Big Data’ Revolution in Healthcare,” a study by McKinsey & Co.

Big data is still a work in progress in medicine. “If a certain number of people have a disease, the task of searching for them will take minutes instead of days,” Dr. Sejdic says. “But for other things, it will still take days because you need to develop software first for analyzing the data.”

Too much data can be a problem, too. “When you know what you want to find out, it’s a much easier problem,” he says. “But if you’re looking for new patterns, it’s more of a fishing expedition. Whenever we do clinical trials, we are flushing out the details. There’s so much information that it’s hard to track it. Until we do that, we won’t have a good understanding. The major change will occur in the next 10 to 15 years.”

*For more from Catherine, contributors from the Economist Intelligence Unit along with industry experts, join The Future Realities discussion.

Page 1 of 3123