Big Data Bootcamp: What Managers Need to Know to Profit from the Big Data Revolution (2014)
Chapter 10. How Big Data Is Changing the Way We Live
Sectors Ripe for Big Data Projects
The starting line at Ironman France 2012 was eerily quiet. There was a nervous tension in the air as 2,500 people got ready to enter the water and spend as many as the next 16 hours trying to complete what was for some the goal of a lifetime. Made famous by the Ironman World Championship held in Kona, Hawaii every year, an event that started out as a 15-person race in 1978 is now a global phenomenon.
Ironman contenders, like almost all athletes, are some of the most data-driven people on earth. Completing the race, which consists of a 2.4-mile swim, a 112-mile bike, and a 26.2-mile marathon, takes focus, perseverance, and training.
It also requires an incredible amount of energy. Ironman athletes burn some 8,000 to 10,000 calories during the race.1 To put that in perspective, human beings burn approximately 2,000 to 2,500 calories on an average day. Often called the fourth sport of triathlon, nutrition can mean the difference between finishing and bonking, which is athlete-speak for running out of energy.
As a result, both preparing for an Ironman and finishing the event requires incredible attention to data. Athletes that don’t put in enough miles won’t have enough endurance to finish come race day. And even those who have trained won’t cross the finish line if they don’t take in enough calories and water to keep their bodies moving.
In the fall of 2011, I decided to train for a full Ironman. Over the course of the next nine months, I learned more about training and nutrition and gathered more data about my personal fitness and health than I ever had before. I would regularly upload my training data to a web site called Garmin Connect, developed by the well-known maker of GPS devices.
Remarkably, as of May 2014, athletes had logged more than 5 billion miles of user activity (stored in a 40 terabyte database) on the Garmin Connect web site. They weren’t just logging miles. When it comes to training and events, they were also logging elevation gain and loss, speed, revolutions per minute on their bikes, calories, and heart rate. Off the course, they were uploading metrics on their weight, body fat percentage, body water percentage, muscle mass, and daily calorie intake, among other health measurements.
One might think that capturing, storing, and analyzing such an immense amount of data would cost thousands if not tens of thousands of dollars or more. But watches with built-in GPS are now available for under $100 and scales that measure body composition are available for just over that. Measurement devices come in all forms and there are easy-to-use, free and low-cost measurement and logging applications for iPhone and Android devices. What’s more, the Garmin Connect service and others like it are free.
This combination of low-cost devices and applications for capturing a wide variety of data, combined with the ability to store and analyze large volumes of data inexpensively, is an excellent example of the power of Big Data. It shows how Big Data isn’t just for large enterprises, but for all of us. It’s something that can help us in our everyday lives. And it points the way, as we shall see, toward many areas ripe for product development.
Personal Health Applications
Taking the capture and analysis of our personal health information one step further by applying Big Data to personal genetics is DNA testing and data analytics company 23andMe.2 Since its founding in 2006 by Anne Wojcicki, the company’s CEO and wife of Google co-founder Sergey Brin, the company has analyzed the saliva of more than 400,000 people.
By analyzing genomic data, the company identifies individual genetic disorders, such as Parkinson’s, as well as genetic propensities such as obesity.3 By amassing and analyzing a huge database of personal genetic information, the company hopes not only to identify individual genetic risk factors that may help people improve their health and live longer, but more general trends as well.
As a result of its analysis, the company has identified some 180 previously unknown traits, including one called the “photic sneeze reflex,” which refers to the tendency to sneeze when moving from darkness to bright sunlight, as well as another trait associated with people’s like or dislike of the herb cilantro.4
Using genomic data to provide insights for better healthcare is in reality the next logical step in an effort first begun in 1990. The Human Genome Project’s (HGP) goal was to map all of the approximately 23,000 genes that were ultimately found to make up our DNA. The project took 13 years and $3.8 billion in funding to complete.
Remarkably, storing extensive human genome data doesn’t need to take up that much physical space. According to one analysis, human genes can be stored in as little as 20 megabytes—consuming about the same amount of space as a handful of songs stored on your iPod.
How is that possible? About 99.5% of the DNA for any two randomly selected people is exactly the same. Thus, by referring to a reference sequence of the human genome, it’s possible to store just the information needed to turn the reference sequence into one that is specific to any one of us.
Although the DNA information of any individual takes up a lot of space in its originally sequenced form—a set of images of DNA fragments captured by a high-resolution camera—once those images are turned into the As, Cs, Gs, and Ts that make up our DNA, the sequence of any particular person can be stored in a highly efficient manner.
Given the sequence of any one person alone, it is hard to produce any informative conclusions. To gain real insight, we must take that data and combine it with scientific research and other forms of diagnosis as well as with changes in behavior or treatments to realize an impact on our health.
From this is should be clear that it is not always the ultimate size of the data that makes it Big Data. The ability not just to capture data but to analyze it in a cost-effective manner is what really makes Big Data powerful. While the original sequencing of the human genome cost some $3.8 billion, today you can get an analysis of your own DNA for $99 from 23andMe. Industry experts believe that that price is subsidized and that the actual cost of individual DNA analysis is more in the $500 to $1000 range. But even so, in just under a decade the cost of sequencing has dropped by multiple orders of magnitude. Just imagine what will happen in the next decade. In the long-run, it is likely that such companies hope not just to offer DNA analysis services, but to offer products and treatments customized to your personal profile as well or to work with pharmaceutical companies and doctors to make such personalized treatments possible. Entrepreneurial opportunities abound in finding new applications of previously unavailable data like DNA information.
Another company, Fitbit, has the goal of making it easier to stay healthy by making it fun. The company sells a small device that tracks your physical activity over the course of the day and while you sleep. Fitbit also offers a free iPhone app that lets users log food and liquid intake.
By tracking their activity levels and nutrition intake, users can figure out what’s working well for them and what’s not. Nutritionists advise that keeping an accurate record of what we eat and how much activity we engage in is one of the biggest factors in our ability to control our weight, because it makes us accountable.
Fitbit is collecting an enormous amount of information on people’s health and personal habits. By doing so, it can show its users helpful charts to help them visualize their nutrition and activity levels and make recommendations about areas for improvement.
Tip Many health-related products that have historically required expensive, proprietary hardware and software can be redeveloped as consumer-friendly applications that run on a smartphone. Now the latest medical technology can be made available to a much broader group of doctors and patients than ever before.
Another device, the BodyMedia armband, captures over 5,000 data points every minute, including information about temperature, sweat, steps, calories burned, and sleep quality.5 The armband has been featured on NBC’s The Biggest Loser, a reality game show focused on weight loss.
Strava combines real-world activity data with virtual competition by taking such challenges outdoors. The company’s running and cycling application for iPhone and Android devices is specifically designed to take advantage of the competitive natures of sporting activities. Fitness buffs can compete for leader board spots on a diverse set of real-world segments, such as cycling from the bottom to the top of a challenging hill, and compare their results on Strava’s web site. The company also offers pace, power, and heart rate analysis to help athletes improve.
According to an American Heart Association article entitled The Price of Inactivity, 65% of all adults are obese or overweight.6 Sedentary jobs have increased 83% since 1950 and physically active jobs now make up only about 25% of the workforce. Americans work an average of 47 hours per week, 164 more hours per year than they did 20 years ago. Obesity costs American companies an estimated $225.8 billion per year in health-related productivity losses. As a result, devices like the Fitbit and the Nike FuelBand stand to make a real impact on rising healthcare costs and personal health.
One iPhone app can even check your heart rate by reading your face or detecting the pulse rate in your finger. Biofeedback app company Azumio has had more than 20 million downloads of its mobile applications, which can do everything from measure your heart rate to detect your stress level. Although Azumio started out developing individual applications, over time it will be able to measure data across millions of users and provide them with health insights.
There is an opportunity when it comes to Big Data health applications to apply some of the same approaches Facebook and others use for online advertising to improve health. Facebook figures out which advertisements produce the most conversions for users who are similar and optimizes the advertisements it shows as a result. Similarly, future Big Data health applications could use data collected from millions of users not just to monitor health, but to make suggestions for improvement—based on what worked for others with similar profiles.
Tip Future Big Data health applications could use data from millions of users not just to monitor health, but to make suggestions for improvement—based on what worked for others with similar profiles.
Azumio has already introduced a fitness application called Fitness Buddy, a mobile fitness app with more than 1,000 exercises, 3,000 images and animations, and an integrated fitness journal. Meanwhile, the company’s Sleepy Time application monitors sleep cycles using an iPhone. Such applications present intriguing possibilities for Big Data and health and are a lot more convenient and less expensive than the equipment used in traditional sleep labs.
Data collected by such applications can tell us what’s going on in the moment as well as offer us a picture of our health over time. If our resting heart rate is fluctuating, that may indicate a change in our health status, for example. By working with health data collected across millions of people, scientists can develop better algorithms for predicting future health. Applications can make better suggestions about changes we should make to improve our health.
One of the most compelling aspects of such applications are the ways in which they make it easier to monitor health information over time. Historically, such data collection required specialized and inconvenient devices or a trip to the doctor’s office. Inconvenience and expense made it difficult for most people to track basic health information.With Big Data, data collection and analysis becomes much easier and more cost-effective. In one example, Intel and the Michael J. Fox Foundation are working together on a project combining wearable devices and Big Data to gain better insight into Parkinson’s disease. Patients use a device like the FitBit, which collects data about them throughout the day. Historically, doctors have collected data about Parkinson’s patients only during medical exams. Because symptoms can vary from one minute to the next, such exams may not provide an accurate picture of a patient’s health. By combining low-cost devices with data collection, researchers will now be able to analyze “patient gait, tremors and sleep patterns, among other metrics.”7
The availability of low-cost personal health monitoring applications and related technologies has even spawned an entire movement in personal health. Quantified Self is “a collaboration of users and tool makers who share an interest in self-knowledge through self-tracking.”8 The founders of the Quantified Self movement are two former editors of Wired magazine, Kevin Kelly and Gary Wolf. Wolf is known for his TED talk,9 “The Quantified Self,” in which he highlights all of the data we can collect about ourselves, and his New York Times article, “The Data Driven Life.”10
New applications show just how much heath data can be collected via inexpensive devices or devices like smartphones that we already have combined with the right software applications. Put that data collection ability together with low-cost cloud services for analysis and visualization and the area of personal health and Big Data has significant potential to improve health and reduce healthcare costs.
Tip The intersection of health and fitness, body sensors, and analysis technology residing in the cloud presents a wealth of opportunity for application development.
Big Data and the Doctor
Of course, even with such applications, there are times when we need to go to the doctor. A lot of medical information is still collected with pen and paper, which has the benefit of being easy to use and low-cost. But this also result in errors when it comes to recording patient information and billing. Having paper-based records spread across multiple locations also makes it challenging for healthcare providers to access critical information about a patient’s health history.
The HITECH—Health Information Technology for Economic and Clinical Health—Act was enacted in 2009 to promote the use of health information technology, and in particular, the adoption of Electronic Health Records or EHRs. It offers healthcare providers financial incentives to adopt EHRs through 2015 and provides penalties for those who don’t adopt EHRs after that date. Electronic Medical Records (EMRs) are digital versions of the paper records that many physicians use today. In contrast, an EHR is intended as a common record of a patient’s health that can be easily accessed by multiple healthcare providers.11 The HITECH Act and the need for both EMRs and EHRs is driving the digitization of a lot of healthcare data, and opening up new avenues for analysis as a result.
New applications like drchrono allow physicians to capture patient information using iPads, iPhones, Android devices, or web browsers. In addition to capturing the kind of patient information previously recorded using pencil and paper, doctors get integrated speech-to-text for dictation, the ability to capture photos and videos, and other features.
EHRs, DNA testing, and newer imaging technologies are generating huge amounts of data. Capturing and storing such data presents a challenge for healthcare providers but also an opportunity. In contrast to historically closed hospital IT systems, newer, more open systems combined with digitized patient information could provide insights that lead to medical breakthroughs.
IBM’s Watson computer became famous for winning Jeopardy! The Memorial Sloan Kettering Cancer Center is now using Watson to develop better decision support systems for cancer treatment.12 By analyzing data from EHRs and academic research, the hope is that Watson will be able to provide doctors with better information for making decisions about cancer treatments.
Such analysis can lead to additional insights as well. For example, an intelligent system can alert a doctor to other treatments and procedures normally associated with those she’s recommending. These systems can also provide busy doctors with more up-to-date information on the latest research in a particular area. The right medical analytics software can even offer personalized recommendations based on data about other patients with similar health profiles.
Tip Knowledge workers in the enterprise have always benefited from better access to data. Now the opportunity exists to bring the most up-to-date information to healthcare providers and to enable them to make recommendations based on your personal health profile.
The amount of data that all these systems capture and store is staggering. More and more patient data will be stored digitally, and not just the kind of information that we provide on health questionnaires or that doctors record on charts. Such information also includes digital images from devices like iPhones and iPads and from newer medical imaging systems, such as x-ray machines and ultrasound devices, which now produce high-resolution digital images.
In terms of Big Data, that means better and more efficient patient care in the future, the ability to do more self-monitoring and preventive health maintenance, and, of course, a lot more data to work with. One key challenge is to make sure that data isn’t just collected for the sake of collecting it but that it can provide key insights to both healthcare providers and individuals. Another key challenge will be ensuring patient privacy and confidentiality as more data is collected digitally and used for research and analysis.
Big Data and Health Cures
A few years ago, I got a rather strange email from my dad. Holding a doctorate in chemistry, my Dad thrives on data. He had had some tests done that showed that his PSA levels, which I would later find out meant Prostate-Specific Antigen, were significantly above normal.
Higher PSA levels are highly correlated with prostate cancer. This raised two key questions. The first was whether my dad actually had cancer. The test did not reveal cancer cells. Rather, higher levels of PSA were often found in people that ultimately were diagnosed with prostate cancer. The difficulty is that not all people with higher PSA levels have cancer. Some of them just have higher PSA levels.
The second challenge my Dad faced was what to do with the information. His options were at the same time simple and complex. On the one hand, he could do nothing. As my personal doctor told me in his classically objective manner, “it’s usually something else that kills them first.” However, my Dad would have to live with the psychological impact of having a slowly worsening disease, which ultimately, if it did spread, he would likely be too old to do anything about.
On the other hand, he could take action. Action would come in the form of a range of treatments, from hormone therapy to ablative surgery to the complete removal of his prostate. But the treatment might prove worse than the cure.
“What should I do?” my Dad asked the doctor. The doctor gave him the only answer he could: “It’s up to you. It’s your life.”
In the case of hormone therapy, which he ultimately chose, my Dad suffered depression, cold sweats, and extensive periods of difficulty sleeping. Had he chosen the surgery he would have been looking at a year or more of a colostomy bag. A few months later a research study was published indicating that the best treatment for prostate cancer may be not to test for it at all. Apparently the microscopic hole associated with the tests can allow the cancer, which is contained in the prostate gland, to escape.
Therein lie two important lessons about our use of data.
First, data can give us greater insights. It can deliver more relevant experiences. It can allow computers to predict what movie we’ll want to watch or what book we’ll want to buy next. But when it comes to things like medical treatment, the decisions about what to do with those insights aren’t always obvious.
Second, our insights from data can evolve. Insights from data are based on the best data we have available at the time. Just as fraud detections systems try to identify fraudsters based on pattern recognition, those systems can be improved with better algorithms based on more data. Similarly, the suggested approaches to different medical conditions change as we get more data.
In men, the cancers with the highest mortality rates are lung, prostate, liver, and colorectal cancer, while in women the cancers that strike highest are lung, breast, and colorectal cancer. Smoking, a leading cause of lung cancer, has dropped from a rate of 45% of the U.S. population in 1946 to 25% in 1993 to 18.1% as of 2012.13 However, the five-year survival rate for those with lung cancer is only 15.5%, a figure that hasn’t changed in 40 years.14
Despite then President Richard Nixon declaring a national war on cancer in 1971, there remains no universal prevention or cure for cancer. That’s in large part because cancer is really hundreds of diseases, not just one. There are more than 200 different types of cancer.15
The National Cancer Institute (NCI), which is part of the National Institutes of Health, has a budget of about $5 billion per year for cancer research.16 Some of the biggest advances in cancer research have been the development of tests to detect certain types of cancer, such as a simple blood test to predict colon cancer, which was discovered in 2004.
Other advances have been those linking cancer to certain causes, such as a study in 1954 that first showed a link between smoking and lung cancer, and a study in 1955 that showed that the male hormone testosterone drives the growth of prostate cancer while the female hormone estrogen drives the growth of breast cancer. Still further advances have come in the approaches to treating cancer: the discovery, for example, of dendritic cells, which became the basis for cancer vaccines and the discovery of angiogenesis, the process by which tumors create a network of blood vessels to bring them the oxygen that allows them to grow.17
More recently, Big Data has been playing a bigger role. The National Cancer Institute’s CellMiner, for example, is a web-based tool that gives researchers access to large quantities of genomic expression and chemical compound data. Such technology makes cancer research more efficient. In the past, working with such data sets often meant dealing with unwieldy databases that made it difficult to analyze and integrate data.18
Historically, there was a big gap between the people who wanted to answer questions by using such data and those who had access to the data. Technologies like CellMiner make that gap smaller. Researchers used CellMiner’s predecessor, a program called COMPARE, to identify a drug with anticancer activity, which turned out to be helpful in treating some cases of lymphoma. Those researchers are now using CellMiner to figure out biomarkers that will tell them which patients are likely to respond favorably to the treatment.
One of the biggest impacts the researchers cite is the ability to access data they couldn’t easily get to before. That is a critical lesson not just for cancer researchers but for anyone hoping to take advantage of Big Data. Unless the large amounts of data collected are made easily accessible, they’ll remain limited in their use. Democratizing Big Data, that is, opening up access to it, is critical to gaining insight from it.
According to the Centers for Disease Control (CDC), heart disease is the leading cause of death in the United States, accounting for almost 600,000 deaths each year of the nearly 2.5 million in total.19 Cancer accounts for just slightly fewer deaths. AIDS is the sixth leading cause of death among people aged 25 to 44 in the United States, down from the number one cause in 1995.20 About two-thirds of all deaths in the United States each year are due to natural causes.
What about that much less serious, but far-reaching illness, the common cold? It’s estimated that people in the United States catch a billion colds each year. That’s three colds for every person. The common cold is caused by rhinoviruses, some 99% of which have been sequenced, and the number of different strains has historically been the reason the common cold is so hard to cure.
Although there’s no cure on the immediate horizon, scientists have found commonalities in the proteins that make up the different forms of the virus, which may lead to advances in the future.
Tip The promise of Big Data is nowhere more prominent than in the area of medicine. Considering healthcare is about 20% of the GDP in the United States, smart product developers and entrepreneurs are looking for ways to optimize healthcare delivery, improve outcome success rates, and uncover important trends.
There are more than seven billion people living on the planet, according to estimates by the U.S. Census Bureau and the United Nations Population Fund.21 Big Data applied to healthcare isn’t just about addressing non-natural causes of death. It’s also about increasing access to healthcare, improving quality of life, and reducing the costs associated with lost time and productivity due to poor health.
According to the most recent published CDC statistics, as of 2011, the United States spent about $2.7 trillion on healthcare annually or about $8,680 per person.22 As people continue to live longer and fewer die young, more people are grappling with chronic illnesses and diseases that strike later in life.23
More children are receiving vaccines that are reducing death under the age of five, while outside of Africa, obesity has become a greater problem than malnutrition. In research that the Bill & Melinda Gates Foundation funded along with others, scientists found that people around the world are living longer, but they’re also sicker. All of this points to the need for more efficiency in delivering healthcare and in helping people track and improve their own health as much as possible.
Big Data and Where We Live: Energy, Leisure, and Smart Cities
Big Data isn’t just improving health and well-being by changing the way we live, it’s also changing the environments in which we live. Smart cities hold the promise of helping cities better organize for growth, according to the World Bank. The promise of smart cities24 “is their ability to collect, analyze, and channel data in order to make better decisions at the municipal level through the greater use of technology.” When it comes to “urban data, things haven’t evolved much over the last century. We are today where we were in the 1930’s on country or national level data,” according to Christopher Williams of UN Habitat.
Today, more than half of the world’s population lives in urban areas and that number is expected to rise to three quarters of the population by 2050. One challenge in collecting data from cities is standardizing the kind of data that is collected. Cities use a diverse set of data-collection methods and collect different kinds of data, making it difficult to compare data from multiple cities to develop best practices. But cities that gather relevant data can make better decisions about infrastructure investments. That’s important given how long such investments tend to last.25
Devices like smart energy meters are already measuring energy consumption and providing consumers with detailed reports on their energy usage. In cities like San Francisco, smart parking meters report about the availability of parking on city streets, data which is then accessible to drivers via easy-to-use mobile apps. Those same parking meters work with products from PayByPhone to enable people to pay for parking by calling a phone number or using a mobile application. Some three million people now make use of the company’s offering in 180 cities, including London, Miami, Vancouver, and San Francisco.
Meanwhile, applications are making it easier to get around major cities. CabSense analyzes data from the New York City Taxi & Limousine Commission and other sources to tell users the best corner on which to catch a cab based on the day of the week, the time of the day, and their location. CabSense analyzed tens of millions of historical data points and uses the data to rate every street corner on a scale of one to five.
Other apps tell users the best ways to make use of public transportation, and even the best train car to be in to make the fastest exit from the subway. Through a combination of applications that cities provide or at least sponsor the development of, and private application developers’ innovative uses of publicly available data, cities are becoming easier to navigate and municipal governments are getting more information about what services will be most helpful to city residents. Even those cities without official smart city mandates or programs are getting smarter.
Tip Technology based on Big Data offers huge opportunities for those who can use it to make life easier or less expensive or more friendly to the global environment.
Big Data in Retail
Of course, where there are cities, there are consumers. Retailers are using Big Data not just to optimize inventory but to deliver personalized shopping experiences to their customers. Loyalty programs have been around for decades. But now, new Big Data Applications combined with in-store mobile technology like Apple’s iBeacon can deliver advertisements, coupons, and deals to consumers who are in close proximity to particular products.
Retailers are using Big Data in other ways as well. Big Data enables retailers to pinpoint the best locations for new stores. Queue analytics enable retailers to evaluate shopping behavior within those stories, identifying bottlenecks and improving the shopping experience by reducing wait times. Shopper activity maps provide visualizations of consumer activity and the paths they take through stores, enabling stores to better optimize layouts and product displays to meet consumer needs.
Tip Amazon is rapidly eating the traditional retailer’s lunch. The company is using Big Data to recommend products to customers, improve inventory management, and introduce new services. Traditional retailers need to get smart quickly about their use of Big Data, both to remain competitive and to keep loyal customers happy.
Retailers are using analytics to reach their customers outside the physical store as well. Big Data analytics can now deliver personalized deals and custom offers to shoppers to bring them back to the retailer’s store or web site more frequently. Big Data isn’t just changing how companies market to consumers—it’s changing the nature of the conversation by delivering a shopping experience that’s personalized to each consumer.
Big Data in Finance
Big cities mean big money and banks and other financial institutions are getting in on the Big Data wave as well. Banks use Big Data analytics to reduce fraud, leveraging pattern matching to detect purchases that seem out of the ordinary for a credit or debit cardholder. At the same time, financial institutions are using high-speed trading systems combined with complex algorithms to automate trading. These systems combine Big Data with the cloud to make automated buying and selling decisions involving billions of dollars every day.
Meanwhile, lenders are making more informed lending decisions based on wider sources of data. In addition to using traditional credit scoring approaches, lenders can now evaluate data sources like online reviews to figure out which restaurants are doing well and therefore might be good candidates to extend credit to—and which aren’t. They can even combine that information with online real estate data to discover up-and-coming neighborhoods that may be good markets for new business. By combining multiple, previously unavailable data sets with the right algorithms, financial institutions can bring credit and banking to a broader set of consumers and businesses, supporting further growth.
The Mobile Future
Nearly five decades after the concept was introduced on Star Trek in 1966, the possibility of a handheld medical tricorder is becoming a reality.26 Smartphone applications can now measure our heart rates and stress levels. Low-cost smartphone add-ons can gauge glucose levels and even provide ultrasounds at home. Such consumer applications and devices hold the promise of making some aspects of healthcare, at least health monitoring, more widely available and cost-effective. The data that such devices generate is useful to patients, to doctors performing diagnosis, and to scientists who rely on large quantities of data to inform their research.
In the enterprise software world, Software as a Service (SaaS) applications disrupted the traditional software delivery model by making applications like Customer Relationship Management (CRM) fast to set up and easy to use. Similarly, Big Data health applications that combine smartphones, low-cost hardware, and web-based analysis software have the opportunity to disrupt traditional, hard-to-use, and expensive medical devices, while improving the quality and reducing the cost of patient care at the same time.
Tip In the enterprise, Software as a Service applications disrupted the traditional software delivery model by making applications fast to set up and easy to use. Big Data entrepreneurs have a similarly disruptive opportunity when it comes to the delivery model for healthcare.
Mobile devices may be one of the easiest ways for smart cities to collect critical data that enables them to improve services and make better decisions about infrastructure investments. Smartphones combined with low-cost medical add-ons may be one of the lowest cost and most efficient ways to expand access to health technology.
Some estimates put the number of smartphones in use worldwide at more than a billion, and the addition of the next billion devices could come as soon as 2015.27 Mobile phone connectivity is on the rise in sub-Saharan Africa, reaching a penetration rate of some 70% as of 2014,28 and smartphones, according to one writer, are not far behind.29 Such devices come with built-in connectivity, making it easy for them to report data back and receive updates.
As in other areas of Big Data, it is at the intersection of the growing number of relatively inexpensive sensors for collecting data—such as iPhones and the specialized medical add-ons being built for them—and the innovative Big Data software applications where some of the most promising opportunities lie in improving our daily lives, the cities we live in, and healthcare globally. Combined with the digitization of medical records and more intelligent systems that can give doctors better information, Big Data promises to have a big impact on our health, both at home and in the doctor’s office.
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1http://www.livestrong.com/article/232980-the-calories-burned-during-the-ironman-triathlon/
2https://www.23andme.com/
3http://www.theverge.com/2012/12/12/3759198/23andme-genetics-testing-50-million-data-mining
4http://blog.23andme.com/health-traits/sneezing-on-summer-solstice/
5http://www.genetic-future.com/2008/06/how-much-data-is-human-genome-it.html
6http://www.bodymedia.com/Professionals/Health-Professionals
7http://www.heart.org/HEARTORG/GettingHealthy/PhysicalActivity/StartWalking/The-Price-of-Inactivity_UCM_307974_Article.jsp
8http://www.usatoday.com/story/news/nation/2014/08/13/michael-j-fox-parkinsons-intel/13719811/
9http://quantifiedself.com/about/
10http://www.ted.com/talks/gary_wolf_the_quantified_self.html
11http://www.vanityfair.com/culture/2013/02/quantified-self-hive-mind-weight-watchers and http://www.nytimes.com/2010/05/02/magazine/02self-measurement-t.html
12http://www.healthit.gov/buzz-blog/electronic-health-and-medical-records/emr-vs-ehr-difference/
13http://healthstartup.eu/2012/05/top-big-data-opportunities-for-health-startups/
14http://www.cdc.gov/tobacco/data_statistics/fact_sheets/fast_facts/
15http://www.lungcancerfoundation.org/who-we-are/the-right-woman-for-the-job/
16http://www.cancerresearchuk.org/cancer-help/about-cancer/cancer-questions/how-many-different-types-of-cancer-are-there
17http://obf.cancer.gov/financial/factbook.htm
18http://www.webmd.com/prostate-cancer/features/fifty-years-of-milestones-in-cancer-research
19http://www.cancer.gov/ncicancerbulletin/100212/page7
20http://www.cdc.gov/nchs/fastats/deaths.htm
21http://www.ncbi.nlm.nih.gov/pubmedhealth/PMH0001620/
22http://en.wikipedia.org/wiki/World_population
23http://www.cdc.gov/nchs/fastats/health-expenditures.htm
24http://www.salon.com/2012/12/13/study_people_worldwide_living_longer_but_sicker/
25http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTSDNET/0,,contentMDK:23146568~menuPK:64885113~pagePK:7278667~piPK:64911824~theSitePK:5929282,00.html
26http://mercuryadvisorygroup.com/articles/sustain/100-pt4-reischl.html
27http://www.economist.com/news/technology-quarterly/21567208-medical-technology-hand-held-diagnostic-devices-seen-star-trek-are-inspiring
28http://thenextweb.com/mobile/2012/10/17/global-smartphone-users-pass-1-billion-for-the-first-time-report/
29http://allafrica.com/stories/201406101485.html