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.
Mobility extends beyond devices. Yet, having the right devices and choice of devices allows us to work the way we’d like. In fact, Cisco is one of the world’s largest enterprise users of Apple products. Employees have purchased 33,000 iPhones and 16,000 iPads as part of Cisco’s BYOD program, and almost half of our regular employees are using Macs.
Earlier this year, I was having a business dinner with an important client in London when I received an IM on my iPhone. The message was from a Cisco colleague, and it said, “I need you to approve a purchase order. Right now.”
So I stepped away from the table, launched an app on my iPhone, read the purchase order, and clicked “approve.” Then I returned to my seat and went back to our dinner without missing a beat. The whole process took maybe two minutes.
Just a few years ago, this transaction would have required a laptop tethered to the network in a hotel or office, and it would have completely disrupted the dinner. This pace of change, leveraging mobility solutions, across IT is unprecedented.
Why are so many businesses turning to mobile enterprise apps? According to Gartner, more than 25 percent of enterprises will have an app store by 2017. Mobile apps are making the promise of BYOD a reality. People love their apps and the highly personalized experience they deliver. By bringing their own devices to work, people can enjoy their work more, use the devices they choose, and do their jobs better and faster—from anywhere. According to Cisco’s latest study, the Financial Impact of BYOD, they can also be more innovative and productive. At Cisco we firmly believe that work is a thing you do, not a place you go. Read More »