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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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

Evolution-of-a-Terabyte-of-Data-7DayShop-800px

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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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FinTech Startups:The Landscape of Blockchain Companies in Financial Services

Blockchain Startups

Source: Startup Management 

HT: Leaders in Pharmaceutical Business Intelligence

The Economist:

Bitcoin fanatics are enthralled by the libertarian ideal of a pure, digital currency beyond the reach of any central bank. The real innovation is not the digital coins themselves, but the trust machine that mints them—and which promises much more besides.

Whatever you think of the cryptocurrency, the “blockchain” is a trust machine that may yet take its place alongside double-entry book-keeping and the limited-liability company as a way of oiling the wheels of commerce.

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Driverless Cars: A Misguided 20th Century Idea

Our Robots

IEEE Spectrum:

A vision of fully autonomous, self-driving cars allowing human owners to nap or read in the car seems to come from the future. But David Mindell, a historian and electrical engineer at MIT, says that the idea of such fully autonomous vehicles roaming the streets represents a more rigid vision left over from the last century. Mindell casts some doubt over the current course along which Google and other huge tech companies are racing to build self-driving cars that don’t require any human supervision.

In his new book, released this month, titled, “Our Robots, Ourselves: Robotics and the Myths of Autonomy” (Viking/Penguin), Mindell envisions a future in which humans are kept in the loop for (mostly) self-driving cars and other robotic technologies, rather than taking them completely out of the equation…

Spectrum: What do you think of the current focus of Google and other tech companies pursuing self-driving cars?

Mindell: Overall, robotics is still focused on full autonomy as the ultimate goal. Researchers should be working on a “perfect five” with trusted, transparent, flexible collaboration between people and autonomous systems. (The “perfect five” refers to the middle of a scale for automation that ranges from very low at level 1, to fully autonomous at 10; the concept is based on the work of Tom Sheridan, professor of mechanical engineering at MIT.)

Such systems should have the ability to turn on autonomy when it can be helpful. Autonomy can reduce human workload and fatigue, but humans should still be present in the system. That’s an empirical argument based on everything we’ve seen in the last 40 years of autonomous systems. People are always thinking that full autonomy is just around the corner. But there are 30 to 40 examples in the book, and in every one, autonomy gets tempered by human judgment and experience.

Spectrum: You’ve said that the best way forward involves a mix of humans, remotely-controlled systems and autonomous robots. Do you think the future you’re hoping for is the one we’re likely to see?

Mindell: I’m hoping the likely future is the one I’m arguing for. There is a quote in the book from the chief of BMW saying “People buy our cars because people like driving them; we’d be crazy to cut them out of the loop.” I think the world is ready for a more nuanced approach to robotics.

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Connected Cars: A History of Security Vulnerabilities

Driverless_Cars_Vulnerabilities

Chris Poulin, IBM, on Tech Crunch:

A Short History Of Car Vulnerability Research

In 2010, researchers from the University of Washington and University of California, San Diego published a seminal paper proving that once an attacker has physical access to a vehicle, they can compromise every component, from the entertainment system to the electronic control units (ECUs) that operate the engine, brakes and even the steering wheel in modern cars that self-park and sport lane-departure correction.

This research showed that an attacker could use connection points between vehicle systems as an entry point to inject arbitrary commands on the controller area network (CAN) bus to perform activities such as disabling all the engine’s cylinders, locking up one brake pad and disabling all brakes — even when the car was traveling at 40 miles per hour. The researchers even created a CAN bus analysis and packet injection tool, dubbed CarShark.

But the automakers weren’t phased by the research; their view was that an attacker would have to be jacked into your car in order to execute an attack.

In response, these same researchers undertook another study in 2011 to further prove their point, this time centered on how to remotely gain access to the vehicle. The paper enumerated the attack surfaces, including channels that provide remote access: Bluetooth, in-vehicle Wi-Fi, telematics, remote keyless entry and RFID immobilizers, dedicated short-range communications (DSRC) used to communicate between vehicles and the road infrastructure, global positioning (GPS), satellite radio and even tire pressure monitor sensors.

The researchers took the play from the punt to the end zone by remotely compromising a vehicle, then using the techniques they created in their first paper to gain complete control of the car. They even claimed they could compromise the telematics unit by simply playing an audio file over the mobile carrier’s network.

Using another vector, the researchers wrote a mobile phone Trojan that gave them remote access to a driver’s or passenger’s mobile phone, and when paired with a vehicle’s telematics unit, exploited a vulnerability in the Bluetooth firmware. They effectively used the mobile phone as a springboard to pwn the vehicle.

The researchers also compromised a typical diagnostics computer used by many service shops so that when it was connected to the diagnostics port on a vehicle, the computer would infect the vehicle with malware allowing the attackers to control it. In a zombie apocalypse scenario, the researchers even wrote software that could turn cars into a rolling “bot” army that reports back to a command and control (C&C) channel through which a criminal could issue commands.

It would seem that these researchers had proven conclusively that connected vehicle security required retooling, and that the consequences could have a major impact on customer confidence and safety. However, without details on the specific vehicles involved in the research, nor publicly disclosed proof of concept instructions, the automotive industry made little public noise about the research.

In fairness, the auto industry may have rallied war rooms and devised plans to amp up security in their automotive products; however, the automotive industry is tight-knit and guards new designs and technology closely. Further, modern automobiles are complex marvels of engineering, and the process of retooling the mechanics and software has to be undertaken slowly, carefully and over a period of many years. Bear in mind that from inception, a new automobile typically takes 5-7 years before it hits the mass market.

And yet, to the general public — and especially to researchers — the silence implied apathy on the part of the automakers. Some in the industry may not fully recognize the broader implications of these results. For example, I spoke to the design manager on the topic of the tire pressure monitoring system (TPMS) vulnerability and he responded with: “So what? All you could do is light up an amber LED on the dashboard.”

Which would be true if all TPMS receivers only had a wire loop that went to the LED in question; however, it’s likely that most of the automakers connect the TPMS receiver to other parts of the in-vehicle network, if for no other reason than to send that data as telemetry back to the predictive maintenance analytics running in the cloud. But let’s not get hung up on the TPMS system: The vehicle threat surface is as broad as the African savanna is to a big game poacher.

Enter Charlie Miller and Chris Valasek, whose 2013 Today Show vehicle hack elicited a collective gasp from the public. Automakers pointed out that such a hack would be unfeasible in real life, as the dashboard is dismantled and there’s a guy sitting in your back seat with a laptop. As is the way with such stories, other shiny objects and celebrity reality television soon overwrote that chunk of the public’s short-term memory, and drivers slid behind the wheel with nary a thought of cars gone wild.

In 2015, Miller and Valasek were back. The widely publicized video of these researchers remotely hacking into a vehicle on the road and ultimately sending it into a ditch struck a chord with the general public that research to date had yet to reach.

To put this in perspective, Recorded Future, which collects intelligence from more than 600,000 sources, including social media and underground forums, queried their data warehouse for mentions of connected vehicle security. As displayed [above], there was a fair amount of chatter when the CarShark exploit was announced, then it exploded around the two Valasek and Miller exploits. The red “bubbles” show the amount of references by date and the milestones are called out. Additionally, references to announced or publicly speculated future events are plotted at the bottom of the chart.

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The Dell-EMC Merger and the Googlization of IT

Yes, Joe Tucci is a great salesman and Michael Dell is the ultimate entrepreneur, but it is Google that is really behind the $67 billion merger. Tucci: “The waves of change we now see in our industry are unprecedented and, to navigate this change, we must create a new company for a new era.” In other words, we must survive in the digital natives era, ushered in by Google, and magnified by the likes of Amazon and Facebook.

To understand what Tucci calls “the new world order,” let’s take a quick tour of the old one, to better understand how the digital natives forced Dell and EMC into the largest tech acquisition in history. Dell and EMC were the two most successful U.S. stocks in the 1990s, appreciating more than any other stock over that booming decade.  They rode on a new tidal wave of digital data, unleashed by the advent of the PC and the networking of PCs in 1980s.

As a result, between 1990 and 2000, the structure of the IT industry has changed for the first—and so far, the last—time, expanding to include large vendors focused on one layer of the IT stack: Intel in semi-conductors, EMC in storage, Cisco in networking, Microsoft in operating systems, Oracle in databases. IBM—the dominant player in the previous era of vertically-integrated, “one-stop-shopping” IT vendors—saved itself from the fate of DEC, Wang, and Prime (all, like EMC, based in Massachusetts) by focusing on services.

The restructured IT industry, and specifically, the focused, “best-in-class” vendors, answered a pressing business need. Digitization and the rapid growth of data unleashed new business requirements and opportunities that called for new ways to sell and buy IT.

There were new business needs for storing much larger volumes of data, mining the data for new market insights, and providing better service to customers by making increasingly “mission-critical” computer systems available 24/7. New IT buyers, such as executives in leading-edge IT departments, business executives impatient with their IT departments, or IT  executives that were asked to take over the out-of-control IT systems acquired by the business units, eschewed the vertically-integrated IT vendors in favor of the new focused competitors, embracing enthusiastically the new “mix and match” IT mentality.

The 2000s were a decade of more-of-the-same with the industry and IT buyers recuperating for a long time from Y2K and the implosion of the dot-com bubble, and going through two recessions. IBM (minus its PC business) and HP (plus Compaq, a successful, focused, PC vendor, like Dell) were the only large “one-stop-shopping” vendors to survive (Sun Microsystems did not). Dell tried, not too successfully, to expand its business beyond PCs to become a one-stop-shopping enterprise IT vendor.

But IT was not the same. Yet another wave of digital data was unleahsed by the advent of the World Wide Web (a.k.a. “the Internet). Unlike the previous wave, this one gave rise to “digital natives,” a new breed of companies with new business models based on Web domination (i.e., mastering online advertising) and data mining (i.e., indexing, recommendations, linking, etc.).  It also gave rise to a new breed of IT buyers.

In the early 200os, Google’s business presented unprecedented IT requirements for performance, availability and scalability (IT jargon for “we have lots of data to store, process, and shuttle around”). They could buy computer storage, servers and networks from existing IT vendors but the cost was prohibitive. More important, Google’s engineers, as someone who was there at the time told me, always thought they could do a better job than anyone else. So they went ahead and built their own IT infrastructure, stringing together “commodity” (off-the shelf) hardware components, and developing innovative software to manage it.

In a recently published paper, Google’s engineers described their approach to “overcoming the cost, operational complexity, and limited scale endemic to datacenter networks a decade ago.” This was the latest in a long string of influential papers that Google has published (starting, I think, in 2006), sharing with the world its experience and expertise in building an IT infrastructure for the 21st century. Moreover, it also released some of the code it has developed as open software, available for free for anyone dealing with similar IT requirements.

Other digital natives were the first to benefit from Google’s academic-like “publish or perish” mentality. They developed Google’s ideas further or came up with their own solutions, taking a page from Google’s business model—it’s a business where IT matters a lot, IT is a core competency. A prominent example is Hadoop, originally developed at Google as a solution to a storage bottleneck standing in the way of analyzing or manipulating large amounts of data, developed further by Yahoo engineers and released by them as open source software, eventually to become a foundational technology for big data analytics.

Facebook, absorbing some top Google engineering talent, went on further to invent an IT infrastructure handling not only petabytes of data every day but also providing an online service to more than 1 billion people worldwide. And it went further than Google in influencing how IT is done everywhere, by establishing the Open Compute Project, with companies such as Goldman Sachs, Bank of America, and Fidelity as members.

Amazon not only built an IT infrastructure for the 21st century, but went even further than Google and Facebook by making it available to the world for a fee, establishing the concept of IT-on-demand or cloud computing on a solid footing. In the process, it has convinced many digital natives, such as Netflix, to run their entire demanding IT infrastructure on Amazon Web Services.  Now, Amazon is ready to take over the enterprise IT market, making clear at AWS:reinvent 2015 that it is going after the legacy IT business.

This is the supply side of the equation that forced Dell and EMC into this merger. But the demand side is no less important. Just like in the early 1990s, when cheaper hardware and software allowed business executives to do their own computing, by-passing the central IT department, we see today the rise of business executives building their fame and fortunes by buying computer services directly from cloud computing providers.

But the Googlization or Amazonization of IT is not limited to business executives.  It is impossible to overstate the impact Google and other digital natives had on IT executives. The new breed of IT executives is ready to “mix and match,” to buy “best-of-breed,” to experiment with off-the-shelf hardware and open source software.

All of this explains why Dell and EMC are merging but also hints at the enormous challenges they will have in convincing IT buyers to buy into their “back-to-the-future” strategy, that a business model that stopped working in the 1990s is the answer to winning in “a new world order.” All the Google-derived talk about “software-defined-everything” and “converged infrastructure” may not be enough for IT buyers looking to take charge of what is increasingly becoming, if not a core competency, a competitive differentiator and a new source of revenues for many companies. All businesses are now digital businesses and their IT requirements are starting to resemble those Google encountered a decade ago.

IBM, HP, Oracle, and Cisco also need to articulate why “one-stop-shopping” is the way forward for IT buyers. Their task is not made easier by the industry’s influential opinion makers, such as Gartner. In its recent Symposium, Gartner told the more than 8,000 CIOs and senior IT executives in attendance to choose as partners “digital accelerators” such as Amazon and Google, not “digital inhibitors” such as Dell and EMC.

Gartner, however, put VMware, the crown jewel in the EMC “federation,” somewhat ahead of the legacy vendors. Will the company that made cloud computing a reality (there will be no cloud computing without server virtualization) save the biggest technology-industry takeover ever?

Originally published on Forbes.com

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