Google Rebrands Enterprise Business As Google Cloud, And Apps As G Suite

Google executives made the case Thursday for why the internet giant should be considered a serious enterprise technology vendor, highlighting recent customer wins and cutting-edge cloud services.

To drive home that point, Diane Greene, Google's cloud chief and former VMware CEO, introduced a rebranding of Google's rapidly expanding enterprise cloud business—to Google Cloud—after a few years experimenting with various monikers.

Customers would always ask, "Is Google really serious about the enterprise?" Greene told attendees of the company's Horizon event in San Francisco. "It was kind of driving us crazy because we were very serious about the enterprise. That's why we were all there."

A couple of months ago, however, "the question started dissipating," Greene said. "People started getting it."

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When Greene first came to Google almost a year ago, the enterprise division she took charge of was officially called Google For Work, but internally, and often externally as well, went by Google Enterprise. The pithier Google Cloud reflects its enterprise maturity.

Google Cloud includes the stack of cloud services—Compute Engine, App Engine, Apps— as well as Chrome, Android, and popular services like maps, search, advanced analytics and machine learning.

At Horizon, leaders of various Google business units introduced enterprise customers like Snap (formerly Snapchat), Evernote and Airbus to the stage and demonstrated technologies available through Google Cloud Platform, the burgeoning network of cloud data centers, with emphasis on artificial intelligence and its implications for driving business productivity.

Horizon, held at a decommissioned U.S. mint, also served as the formal introduction of another significant rebranding.

Google launched its cloud business a decade ago with Apps, its groundbreaking office productivity suite, which initially was a showcase for Gmail, said Prabhakar Raghavan, vice president of the Apps business.

That suite of Software-as-a-Service tools—from Drive to Docs to Sheets—has now been upgraded and infused across-the-board with artificial intelligence, he said.

Going forward, Apps will be called G Suite, Raghavan told attendees.

Machine learning is "something Google invested in pretty much from the beginning," Greene told attendees. The technology now operates across the company's products, and cloud customers are using it to solve previously forbidding problems.

Urs Holzle, Google's senior vice president for technical infrastructure, said Google spent $9.9 billion on upgrading its cloud infrastructure in 2015—just shy of the combined spend of rivals Amazon and Microsoft.

Google's public cloud differentiates itself in a number of ways from those competitors, Holzle said, especially around networking infrastructure and analytics features.

Holzle unveiled a new version of Google's Big Query analytics platform, called Big Query for Enterprise. That product, which allows users to update tables, transitions the product from an analytics query to a full data warehouse, he said.

The next step from analytics, as far as deriving more value from data, is machine learning, he said. That technology, by which computing systems train their predictive capabilities, has always put up a high hurdle to adoption.

Google wants to change that, he said.

Google now offers a number of pretrained machine learning models as services, such as one that converts voice to text.

And Google's cloud-based machine learning platform, Google ML, is ready to be rolled out in beta, available to all partners and users.

Paul Vallee, CEO of Pythian, a Google partner based in Ottawa, Canada, told CRN machine learning makes just about every customer believe they have an opportunity to compete in the market with their data by doing things like increasing their fraud detection rate or analyzing customer behavior.

Intense customer interest in that technology is why Pythian is eager to be a beta partner of Cloud ML, he said.

"Everyone believes there is a competitive advantage to be seized with data. Modern machine-learning platforms like Cloud ML bring a rapid turnkey way to validate whether a model can support a business thesis," Vallée told CRN.

A decade ago, only the world's foremost artificial intelligence experts could implement machine-learning solutions; with cloud-based platforms, average business users can beat those experts' results in far shorter time, Holzle said.

Those technologies will increasingly be delivered through a NoOps model, Holzle said, available without having to provision virtual machines.

Google's "moving away from virtualized hardware to scalable services, scalable data," he said.

Brian Stevens, vice president of cloud platforms, told attendees that G Suite will introduce a service called Quick Access that monitors user activity patterns, then employs machine learning to try to offer stored files as they are needed, speeding the time to access data.

Another new collaboration component of the suite, Team Drives, helps "get away from notion that content is owned and managed by individuals," Stevens said.

Vallee, of Pythian, told CRN that while much has been made about Google needing to prove it offers enterprise-ready technology, he's never seen any doubt of that capability in the marketplace.

Google's original cloud strategy involved internet-scale applications, replete with audit and compliance capabilities, as well as encryption of data at rest and in transit, "which all enterprises need to care about in the modern era.

And because Google built its cloud in a Platform-as-a-Service model, it essentially solidifies its market share, he told CRN.

"Every application written and running in GCP is already treating infrastructure as code and not lifted and shifted in," he said.