In the world of sports, becoming the fiercest competitor possible is the name of the game.
Today, the intersection of cloud technology and smart sports equipment is helping athletes and para-athletes perform at a world-class level. As a techie, you might admire the new shock absorbers built for downhill skis so paraplegics can hurl down a mountain at 70mph. Or how can you not marvel at the development of the prosthetic retina that can help blind athletes perform the sports they love?
Another amazing advancement is highlighted in Rick Smolan’s book, The Human Face of Big Data. Sheila Nirenberg, an associate professor at Weill Cornell Medical College, developed a way to enable patients with macular degeneration to see again. Awesome! As stated in the book:
“Using an array of high-speed, parallel processing computers, Nirenberg and her team embedded custom software in microprocessors and cameras that will be built into eyeglasses…images captured by the cameras will be translated into code in the form of thousands of pulsing lights, which can be recognized by the brain.”
It won’t be long before today’s visually impaired athletes can use this technology to compete at the highest level. And more than ever, this technology will rely on data that flows quickly and in real-time.
This is where cloud computing plays a key role -- allowing data to be easily accessed and stored, so that mobile devices and the peripherals of tomorrow (connected eyeglasses, etc.) can provide new experiences to athletes. These devices will be able to transmit data, communicate to each other (M2M) and relay to the user (M2P) vital information needed for the athlete.
Advancements in medical technology and cloud computing are giving us a new perspective on life
For example, a partially blind, or fully blind cross-country skier may one day have the capabilities through the Internet of Everything (IoE) to communicate through M2P technology while on the course. What will this mean? Sensors indicating course characteristics (downhill, uphill, turns, starting line/finish line, timing, etc.) will be able to communicate and relay the information in real-time to the skier. These types of mobile-enabled experiences are powered through cloud infrastructure and applications.
As the cloud prepares for another history-making year in its campaign to become a part of every part of our lives, a different type of history is being made. The birth of life. As we begin a new year, many around the world are celebrating new life, building on their family foundation.
“Foundations” are traditionally thought of as ground-level, or even underground; but as we ring in 2014, it’s time to start thinking of foundation in a new light. The cloud makes the possibility of sharing our lives with others more easily than before, like birth for instance. It’s enabling this connection and allowing people to access more information, more pictures, more video, and more data, with more ease than ever before. That connection doesn’t stop at content and data points- in fact, it doesn’t stop at all.
The cloud’s biggest value is in the Internet of Everything (IoE). IoE brings new experiences and interactions to life, and the cloud will only broaden IoE’s breadth over our lifetime with all of the devices, communicating, and sharing information.
In photojournalist Rick Smolan’s Human Face of Big Data project, stunning facts about how big data and the world of many clouds are changing how we live our lives, from our very first day, are showcased. For example:
During the first day of a baby’s life, the amount of data generated by humanity is equivalent to 70 times the information contained in the Library of Congress.
One-third of children born in the United States already have an online presence before they are born. That number grows to 92 percent by the time they are two.
In 2012, the average digital birth of children occurs at approximately six months.
Within weeks of their birth, another one-third of all children’s photos and information are posted online.
I am attending South Korea’s Big Data Forum in Seoul, and one question here is, “How big is Big Data?” My friend and colleague Dave Evans has pointed out that by the end of this year, more data will be created every 10 minutes than in the entire history of the world up to 2008. Now, that’s big!
Much of this data is being created by billions of sensors that are embedded in everything from traffic lights and running shoes to medical devices and industrial machinery—the backbone of the Internet of Things (IoT). But the real value of all this data can be realized only when we look at it in the context of the Internet of Everything (IoE). While IoT enables automation through machine-to-machine (M2M) communication, IoE adds the elements of “people” and “process” to the “data” and “things” that make up IoT. Analytics is what brings intelligence to these connections, creating endless possibilities.
To understand why, let’s step back and take a look at the classic approach to Big Data and analytics. Traditionally, organizations have tended to store all the data they collect from various sources in centralized data centers. With this model, if a retailer wants to know something about the buying patterns of a certain store’s customers, it can create an analysis of loyalty card purchases based on data in the data warehouse. Collecting, cleansing, overlaying, and manipulating this data takes time. By the time the analysis is run, the customer has already left the store.
Big Data today is characterized by volume, variety, and velocity. This phenomenon is putting a tremendous strain on the centralized model, as it is no longer feasible to duplicate and store all that data in a centralized data warehouse. Decisions and actions need to take place at the edge, where and when the data is created; that is where the data and analysis need to be as well. That’s what Cisco calls “Data in Motion.” With sensors gaining more processing power and becoming more context-aware, it is now possible to bring intelligence and analytic algorithms close to the source of the data, at the edge of the network. Data in Motion stays where it is created, and presents insights in real time, prompting better, faster decisions.
If you’ve been following my past blogs and presentations, you’ve heard me talk about “Data in Motion.” That’s the catch-all term used to describe the swelling flood of data that is at maximum value while still in motion (and often at that fleeting moment in which it is created). Data in Motion requires rapid, real-time response in order to provide actionable insights at the right place and at the right time. Done right, it can be evaluated in meaningful ways that lead to knowledge and wisdom. But even a slight delay in reacting to it can mean the data loses its value.
Data in Motion is a completely different animal than the persistent, static “data at rest” that is the subject of Big Data today. And, so far, Data in Motion has gone largely untapped. Retailers could tap Data in Motion to send targeted alerts and promotions in real time to shoppers. Healthcare providers could use it to remotely monitor patients in their homes. Manufacturers could harness it for process monitoring and control. My friend Rick Smolan and I recently shared some inspiring examples of how Data in Motion and data in general have changed people’s lives. If you haven’t already seen it, you can watch our conversation here.
Written By Kiran Matty, Marketing Manager, and Ola Mabadeje, Marketing Manager
If “Big Data” is crude oil, then Analytics is its refinery. According to a Cisco IBSG report, “if ‘crude” data can be extracted, refined, and piped to where it can impact decisions, its value will soar”. The trends, patterns, and insights that can be gathered from the various sources of Big Data are virtually limitless. However, this blog shall primarily focus on the analytics that can be generated by refining i.e. analyzing the data that’s resident in a mobile network and is largely untapped.
According to Cisco VNI, the number of connected devices will be three times the global population by 2017 and the global IP traffic will also increase threefold in the same time frame. Mobile Networks have not only been primed to sustain this onslaught but have also transformed into a programmable platform that can collect, correlate, and contextualize data rapidly. Network data, Policy, and Analytics interplay in a multitude of ways and form the basis of Data in Motion that’s at the heart of network monetization.
Hidden opportunities exist in the market gaps
As you might be aware, CPM (Cost per Mille) for mobile Ads is lower than that of other advertising media such as online, television, etc. This is because mobile Ads are generally untargeted, which leads to ineffective Ad campaigns, and could be attributed to a large extent to the lack of contextual awareness vis-à-vis location, demographics, browsing history, network conditions, screen size, etc. Although market researchers have perfected the measurements for other advertising media, they haven’t yet cracked the nut for mobile and the mobile metrics remain fuzzy at best which is impeding the flow of advertising dollars to mobile. On the other hand, as much as we love applications like Apple Siri and FaceTime, Angry Birds, etc., and devices like the Apple iPhone, they have turned out to be an operational nightmare in certain cases for the mobile operators around the world because of the data and signaling Tsunami that they can potentially bring about. This is due in part to lack of network analytics that can predict such surges in the network traffic with reasonable accuracy to allow for timely management of the network in terms of network capacity and bandwidth. This would eventually lead to operational efficiency and hence cost savings. Further, think about Internet of Everything and the 50 billion devices that would come online by 2020!
Mobile network operators are well positioned to address the above pain-points. With access to millions of subscribers, they can predict network and consumer behavior with high degree of accuracy quite simply because of the law of large numbers. Unlike many pure-play analytics vendors, network operators have direct access to data from a variety of sources such as CDN (Content Delivery Network), devices, applications, network billing and charging systems, not to mention the various mobile network elements. Some may even have access to subscriber Wi-Fi data. Lastly, many have the cloud infrastructure that’s needed for analyzing data at a bigger scale.