Cloud traffic will grow at an annual rate of 33% over the next 5 years, Cisco predicts

The new version of the Cisco Cloud Index computes the rapid expansion of today’s stampede to the cloud. “We have never seen anything like this in terms of speed of customer adoption,” Oracle Co-CEO Mark Hurd said recently, describing how his corporate customers have enthusiastically embraced the cloud.

One of them, General Electric, has moved, in just the last 18 months, 10% more of its computing load into the cloud, and expects to run 70% of its applications in the cloud by 2020. In their latest quarterly financial reports, Amazon reported that its cloud business has surged 79% year-over-year and Microsoft announced that its cloud business has “more than doubled.”

Here are the highlights of Cisco’s ongoing study of the growth of global data center and cloud-based data traffic.

Almost all of the work of IT will be done in cloud data centers

Based on its hands-on knowledge of the movement of data over global computer networks, Cisco predicts that cloud traffic will grow at an annual rate of 33% over the next 5 years, quadrupling from 2.1 zettabytes (2.1 trillion gigabytes) in 2014 to 8.6 zettabytes by the end of 2019. 86% of workloads will be processed by cloud data centers in 2019  and only 14% will be processed by traditional data centers.

Cisco Figure 3 DC and Cloud Growth

Source: Cisco Global Cloud Index, 2014–2019

Cloud traffic is expected to account for 83% of total data center traffic by 2019. Cloud traffic is a subset of data center traffic and is generated by cloud services accessible through the Internet from scalable, virtualized cloud data centers. Total data center traffic, which Cisco projects will reach 10.4 zettabytes by the end of 2019, is comprised of all traffic traversing within and between data centers as well as to end users.

10.4 trillion gigabytes is the equivalent of 144 trillion hours of streaming music or 6.8 trillion of high-definition (HD) movies viewed online. Ones and zeros are eating the world and the companies providing consumers with digital entertainment and other services have been at the forefront of the migration to the cloud.  Indeed, The Wall Street Journal has reported recently that Netflix has shut down the last of its data centers, moving the last piece of its IT infrastructure to the public cloud.

The public cloud will grow faster than the private cloud

Source: Cisco Global Cloud Index, 2014–2019

Source: Cisco Global Cloud Index, 2014–2019

While overall cloud workloads will grow at a CAGR of 27% from 2014 to 2019, the public cloud workloads are going to grow at 44% CAGR over that period, and private cloud (where cloud services are  delivered to corporate users by their IT department) workloads will grow at a slower pace of 16%. By 2019, there will be more workloads (56%) in the public cloud than in private clouds (44%).

New sources of data, especially the Internet of Things, will keep the clouds very busy

Source: Cisco Global Cloud Index, 2014–2019

Source: Cisco Global Cloud Index, 2014–2019

The total volume of stored data on client devices and in data centers will more than double to reach 3.5 zettabytes by 2019. Most stored data resides in client devices today and will continue to do so over the next 5 years, but more data will move to the data center over time, representing 18% of all data in 2019, up from 12% in 2014.

In addition to larger volumes of stored data, the stored data will be coming from a wider range of devices by 2019. Currently, 73% of data stored on client devices resides on PCs. By 2019, stored data on PCs will go down to 49%, with a greater portion of data on smartphones, tablets, and machine-to-machine (M2M) modules. Stored data associated with M2M will grow at a faster rate than any other device category at an 89% CAGR.

A broad range of Internet of Things (IoT) applications are generating large volumes of data that could reach, Cisco estimates, 507.5 zettabytes annually by 2019. That’s 49 times greater than the projected data center traffic for 2019 (10.4 zettabytes). Today, only a small portion of this content is stored in data centers, but that could change as big data analytics tools are applied to greater volumes of the data collected and transmitted by IoT applications.

The figure below maps several M2M applications for their frequency of network communications, average traffic per connection, and data analytic needs. Applications such as smart metering can benefit from real-time analytics of aggregated data that can optimize the usage of resources such as electricity, gas, and water. On the other hand, applications such as emergency services and environment and public safety can be much enhanced through distributed real-time analytics that can help make real-time decisions that affect entire communities. Although some other applications such as manufacturing and processing can have potential efficiencies from real-time analytics, their need is not very imminent.

Source: Cisco Global Cloud Index, 2014–2019

Source: Cisco Global Cloud Index, 2014–2019

More consumers will keep their data in the cloud

Cisco estimates that by 2019, 55% (2 billion) of the Internet-connected consumer population will use personal cloud storage, up from 42% (1.1 billion users) in 2014.

Source: Cisco Global Cloud Index, 2014–2019

Source: Cisco Global Cloud Index, 2014–2019

Global consumer cloud storage traffic will grow from 14 exabytes (14 billion gigabytes) annually in 2014 to 39 exabytes by 2019 at a 23% CAGR. This growth translates to per-user traffic of 1.6 gigabytes per month by 2019, compared to 992 megabytes per month in 2014.

Source: Cisco Global Cloud Index, 2014–2019

Source: Cisco Global Cloud Index, 2014–2019

Ones and zeros are eating the world and today we got fresh insights into how much, how fast, and how their movement changes the way IT services are delivered to businesses and consumers.  For more data and the study’s methodology, go to the Cisco Global Cloud Index webpage.

Originally published on Forbes.com

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Google Knows Everything (#IoT)

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Who Does What in Data Science (Infographic)

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Hunting Unicorns and Rapidly Becoming the Master of the Startup Universe

Anand Headshot - black and white

Anand Sanwal, co-founder and CEO, CB Insights

CB Insights is riding the Unicorn Boom, doubling its headcount since the beginning of the year, propelled by its unique database of companies and investors and everything there is to know about them. The frequency of “according to CB Insights” appearing in a wide range of media outlets has gone up dramatically this year. The fast-growing subscriber list for its engaging newsletter, bursting with visually-appealing data nuggets and topical analysis, is now at more than 100,000. Recently, it has supplied the New York Times with a list of the “50 Companies That May Be the Next Start-Up Unicorns.

This master of the startup universe got its start as Chubby Brain. “In our early days we were talking to an investment bank and they said they really liked our product but they will never buy something called Chubby Brain,” recalls Anand Sanwal, CB Insights’ co-founder and CEO. “At that moment we understood we needed to lose some of our edgy internet entrepreneur desire, given the market we were going after,” he adds.

The market they were going after consisted of all the people that need to understand the health of private companies. Doing M&As for American Express and managing, among other things, investments in companies trying to disrupt AmEx, Sanwal found out how difficult it was to use traditional information providers such as Dow Jones and Thomson (“their products, in one word, are terrible,” he says). To find out what’s going on with startups and other private companies, people were spending a lot of time manually gathering data by calling investors and VCs. Besides, the scope of this data collection was severely limited by the fact that private companies do their best to keep their financial performance private.

The answer to this need was in the explosion of publicly available data on the Web. “Better understanding private companies by using public information was the germ of the idea for CB Insights,” says Sanwal.

So a new digital business was born. CB Insights uses big data tools to automate the data collection, crawling about 100,000 sources daily, and big data algorithms to analyze the data about investors, companies, and industries. Most important, it identifies and tracks the publicly available signals that serve as good indicators of the health of private companies, e.g., hiring statistics from job boards, news and sentiment about the news, and information about new partners and customers. “I don’t think any of these [signals] is going to be independently a smoking gun,” says Sanwal. “We build this mosaic of a private company that’s instructive in understanding its health.” Doing it since 2009, CB Insights has amassed a large historical record that allows it to pinpoint which signals are strong (serving as valid indicators of a company’s success or failure) and which are weak.

Ironically for a startup that started up by providing recommendations to other entrepreneurs about the best funding sources for their startups, the founders of CB Insights did not seek angel or VC investment. Instead, they applied for a grant from the Small Business Innovation Research (SBIR) program of the National Science Foundation (NSF). The timing was right, as banks stopped lending to small businesses after the financial crisis. “Banks think about private companies as one monolithic entity,” says Sanwal, “and when times are tough they see all small businesses as a risk. Our thesis was—can we give lenders data that will help them make better decisions.”

They got an initial $150,000 grant to prove their thesis. When they did, they received a $500,000 grant, and when they started generating revenues, an additional $500,000, for a total of $1.15 million. “I don’t think we needed the NSF money from a survival perspective,” says Sanwal, “but it let us pursue some of the more moonshot ideas.”

In addition to this unusual funding mechanism, CB Insights is also quite unique in this Unicorn Boom era in that it has been revenue-funded from the beginning. “We’ve been very disciplined, always making more revenues than we spend every month,” says Sanwal. That’s a lesson he learned working for Kozmo.com, one of the poster boys of the dot-com bubble which shut down after raising about $250 million. “I saw the perils of growth at all costs,” he says.

On the flip side, Sanwal probably also saw the benefits of free publicity, generated by the media’s obsession with dot-com startups. The SBIR grants helped in marketing the company as “a National Science Foundation-backed big data company” to potential customers and employees, but CB Insights needed more than the prestige of government-backed research to reach its targeted audience.

“We had zero marketing dollars,” says Sanwal, “and unlike Dow Jones or Thomson we could not take [prospects] to a dinner or a Yankees game.” Instead, their “weapon of choice” was their excellence at Excel. They started building a “content marketing engine,” providing potential customers—and the media—with a taste of what can be done with their data and analysis, via a newsletter and on their research blog. This marketing effort has showcased their data visualization skills, knack for knowing what will be quoted in the media, and an engaging combination of far-from-suppressed edgy  humor, “data geeks” passion, and maverick attitude (Sanwal signs all newsletters with “I love you” or, most recently, with “even if you never say it back, I still love you”).

The Unicorn Boom has provided a lot of opportunities for CB Insights to demonstrate their predictive analytics skills and get lots of free publicity, although hunting unicorns is a very insignificant part of the business. But, by popular demand, Sanwal has been happy to offer an opinion in the press and public speaking engagements regarding the perennial question—are we in a bubble? No, he says, ”the mechanism that’s going to force valuations down isn’t there as the public markets are closed to private companies right now. If companies start to IPO that have no business going public, then we will start to worry. A unicorn might fail and this will generate headlines but it will not cause any systemic risk to anybody. Right now, it’s only a private market euphoria, but no doubt it’s a little crazy.” (In this presentation, Sanwal explains in more detail why there is no bubble right now).

Sanwal says he has always wanted to be an entrepreneur: “I grew up in a family that was entrepreneurial. My father is a chemical engineer and started his own chemical manufacturing firm long time ago. I always wanted to be my own boss.”  Sanwal got at Wharton a chemical engineering degree and a finance/accounting degree, so I asked him what did his father think about him not pursuing an engineering career. “I think he knew he is a much better engineer than I’ll ever be and that the world is a much safer place because I’m not engineer,” Sanwal answered.

Like other successful entrepreneurs, Sanwal has a larger vision, going beyond the specific business opportunity he has spotted. Providing lenders, investors, and others a risk assessment tool akin to a FICO score for private companies, CB Insights makes private markets work faster, enabling faster decision-making. Correcting the inefficiencies he discovered in the market for information on private companies, leads to smoothing the inefficiencies in a variety of economic decisions, activities, and endeavors.

That vision was behind the development of a predictive analytics platform on top of high-quality database, serving as the foundation from which to launch a variety of applications or services targeted at specific audiences and needs.  In addition to a subscription-based access to its database, CB Insights has offered so far applications and tools for assessing the health of private companies and investors, mapping the links between investors and companies, tracking valuation and valuation multiples data, monitoring the health and growth potential of markets, and industry analytics.

About a month ago, the company launched CB Insights for Sales, “helping sales teams fill the top of their funnel with more prospects,” says Sanwal. It is targeted at companies selling “high-value products, $10,000 and above,” and corrects yet another inefficiency—the business-to-business selling process which is “hopelessly antiquated.”  Salespeople need not only new leads, but also to nurture their prospects. CB Insights’ database—which Sanwal argues is a competitive differentiator in the crowded sales analytics market—alerts them to news about the prospect which provide them with a reason to call. A company signing up for CB Insights for Sales uploads a list of their existing clients, which helps the application provide a similar list of companies to target. This is a big step for CB Insights towards customizing their database for the need of a specific customer.

Other recent and potential applications include recommending the likely acquirers of a private company, identifying the industries and markets that are hot, indicating for accounts receivables departments when they should tighten up credit terms for specific companies, and identifying for recruiters companies that are not doing too well so they can poach their talent. The long-term goal is to provide “a predictive analytics API that other people can pull into their own use cases and platforms,” says Sanwal.

CB Insights aims to be “the Bloomberg for private companies,” Sanwal tells his public audiences. But it’s more than that. “Our mantra internally is that probability trumps punditry,” he says. “We want to take on all of those people who make bold prognostications of where the world is going but they completely pull it out of [thin air]. We want to use data to inform the conversation about what’s next.”

Update: On November 9, 2015, CB Insights announced it has raised a $10 million Series A and provided details regarding its business metrics.

Originally published on Forbes.com

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The Evolution of Biotechnology 8000 BC to Near Future (Infographic)

Biotech-evolution

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45% of work activities can be automated including those performed by highest-paid occupations

McKinsey_Automation

McKinsey:

…our research suggests that as many as 45 percent of the activities individuals are paid to perform can be automated by adapting currently demonstrated technologies.4 In the United States, these activities represent about $2 trillion in annual wages. Although we often think of automation primarily affecting low-skill, low-wage roles, we discovered that even the highest-paid occupations in the economy, such as financial managers, physicians, and senior executives, including CEOs, have a significant amount of activity that can be automated…

…fewer than 5 percent of occupations can be entirely automated using current technology. However, about 60 percent of occupations could have 30 percent or more of their constituent activities automated. In other words, automation is likely to change the vast majority of occupations—at least to some degree—which will necessitate significant job redefinition and a transformation of business processes. Mortgage-loan officers, for instance, will spend much less time inspecting and processing rote paperwork and more time reviewing exceptions, which will allow them to process more loans and spend more time advising clients. Similarly, in a world where the diagnosis of many health issues could be effectively automated, an emergency room could combine triage and diagnosis and leave doctors to focus on the most acute or unusual cases while improving accuracy for the most common issues.

As roles and processes get redefined, the economic benefits of automation will extend far beyond labor savings. Particularly in the highest-paid occupations, machines can augment human capabilities to a high degree, and amplify the value of expertise by increasing an individual’s work capacity and freeing the employee to focus on work of higher value. Lawyers are already using text-mining techniques to read through the thousands of documents collected during discovery, and to identify the most relevant ones for deeper review by legal staff. Similarly, sales organizations could use automation to generate leads and identify more likely opportunities for cross-selling and upselling, increasing the time frontline salespeople have for interacting with customers and improving the quality of offers…

Our work to date suggests that a significant percentage of the activities performed by even those in the highest-paid occupations (for example, financial planners, physicians, and senior executives) can be automated by adapting current technology.7 For example, we estimate that activities consuming more than 20 percent of a CEO’s working time could be automated using current technologies. These include analyzing reports and data to inform operational decisions, preparing staff assignments, and reviewing status reports. Conversely, there are many lower-wage occupations such as home health aides, landscapers, and maintenance workers, where only a very small percentage of activities could be automated with technology available today [see chart above].

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Google: Machine Learning and Deep Neural Networks Explained (Video)

[youtube https://www.youtube.com/watch?v=bHvf7Tagt18?rel=0]

*Greg and Chris did an AMA on Friday, September 25th to answer people’s deep learning questions. Check out their answers here: https://goo.gl/jpbMy9

*To read more about machine learning, neural nets, and the like – check out the Google Research blog:http://googleresearch.blogspot.com/ and Chris’s blog: http://colah.github.io/

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Evolution of Computer Storage 1956-2015

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68% of Americans have smartphones, 45% have tablet computers, other devices not growing

Pew_Device Ownership

Today, 68% of U.S. adults have a smartphone, up from 35% in 2011, and tablet computer ownership has edged up to 45% among adults, according to newly released survey data from the Pew Research Center. Smartphone ownership is nearing the saturation point with some groups: 86% of those ages 18-29 have a smartphone, as do 83% of those ages 30-49 and 87% of those living in households earning $75,000 and up annually.

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The Economist’s Data Editor on Data Fetishism

Ken Cukier

Ken Cukier

“We fetishize data, we think that data is the answer. It’s far from the truth. In fact, it’s ridiculous, because the data is only a simulacrum of reality in the same way that a map is not a territory. And so while we need to use information and data to make decisions as we need to do, the data is always unfaithful, always unreliable, it always misleads, and you have to torture it until it confesses”–Kenneth Cukier, Data Editor, The Economist

Source: Economist Radio, “Arthur Miller and Modern-Day Witch-Hunts”

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