Category Archives: Google for Work Blog

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How AI can help make safer baby food (and other products)

Editor’s note: Whether you’re growing cucumbers or building your own robot arm, machine learning can help. In this guest editorial, Takeshi Ogino of Kewpie tells us how they used machine learning to ensure the quality and safety of the ingredients that go into their food products.

Quality control is a challenge for most industries, but in the world of food production, it’s one of the biggest. With food, products are as good as the ingredients that go into them. Raw materials can vary dramatically, from produce box to produce box, or even from apple to apple. This means inspecting and sorting the good ingredients from the bad is one of the most important tasks any food company does. But all that work inspecting by hand can be time-consuming and arduous both in terms of overhead and manpower. So what’s a food company to do?

At Kewpie Corporation, we turned to a surprising place to explore better ways to ensure food quality: artificial intelligence built on TensorFlow.

Although Kewpie Corporation is most famous for our namesake mayonnaise, we’ve been around for 100 years with dozens of products, from dressings to condiments to baby foods. We’ve always believed that good products begin with good ingredients.

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Ingredients that are safe and also give you peace of mind

Last October, we began investigating whether AI and machine learning could ensure the safety and purity of our ingredients faster and more reliably than ever.

The project began with a simple question: “What does it mean to be a ‘good’ ingredient?” The ingredients we purchase must be safe, of course, and from trustworthy producers. But we didn’t think that went far enough. Ingredients also need to offer peace of mind. For example, the color of potatoes can vary in ways that have nothing to do with safety or freshness.

Kewpie depends on manual visual detection and inspection of our raw ingredients. We inspect the entire volume of ingredients used each day, which, at four to five tons, is a considerable workload. The inspection process requires a certain level of mastery, so scaling this process is not easy. At times we’ve been bottlenecked by inspections, and we’ve struggled to boost production when needed.

We’d investigated the potential for mechanizing the process a number of times in the past. However, the standard technology available to us, machine vision, was not practical in terms of precision or cost. Using machine vision meant setting sorting definitions for every ingredient. At the Tosu Plant alone we handle more than 400 types of ingredients, and across the company we handle thousands.

That’s when I began to wonder whether using machine learning might solve our problem.

Using unsupervised machine learning to detect defective ingredients

We researched AI and machine learning technology across dozens of companies, including some dedicated research organizations. In the end, we decided to go with TensorFlow. We were impressed with its capabilities as well as the strength of its ecosystem, which is global and open. Algorithms that are announced in papers get implemented quickly, and there’s a low threshold for trying out new approaches.

One great thing about TensorFlow is that it has such a broad developer community. Through Google, we connected with our development partner, BrainPad Inc, who impressed us with their ability to deliver production level solutions with the latest deep learning. But even BrainPad, who had developed a number of systems to detect defective products in manufacturing processes, had never encountered a company with stricter inspection standards than ours. Furthermore, because our inspections are carried out on conveyor belts, they had to be extremely accurate at high speeds. Achieving that balance between precision and speed was a challenge BrainPad looked forward to tackling.

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Sorting diced potato pieces at the Tosu Plant.

To kick off the project, we started with one of our most difficult inspection targets: diced potatoes. Because they’re an ingredient in baby food, diced potatoes are subject to the strictest scrutiny both in terms of safety and peace of mind. That meant feeding more than 18,000 line photographs into TensorFlow so that the AI could thoroughly learn the threshold between acceptable and defective ingredients.

Our big breakthrough came when we decided to use the AI not as a ”sorter” but an ”anomaly detector.” Designing the AI as a sorter meant supervised learning, a machine learning model that requires labels for each instance in order to accurately train the model. In this case that meant feeding into TensorFlow an enormous volume of data on both acceptable and defective ingredients. But it was hugely challenging for us to collect enough defective sample data. But by training the system to be an anomaly detector we could employ unsupervised learning. That meant we only needed to feed it data on good ingredients. The system was then able to learn how to identify acceptable ingredients, and reject as defective any ingredients that failed to match. With this approach, we achieved both the precision and speed we wanted, with fewer defective samples overall.

By early April, we were able to test a prototype at the Tosu Plant. There, we ran ingredients through the conveyor belt and had the AI identify which ones were defective. We had great results. The AI picked out defective ingredients with near-perfect accuracy, which was hugely exciting to our staff.

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The inspection team at the Tosu Plant.

It’s important to note that our goal has always been to use AI to help our plant staff, not replace them. The AI-enabled inspection system performs a rough removal of defective ingredients, then our trained staff inspects that work to ensure nothing slips through. That way we get “good” ingredients faster than ever and are able to process more food and boost production.

Today we may only be working with diced potatoes, but we can’t wait to expand to more ingredients like eggs, grains and so many others. If all goes well, we hope to offer our inspection system to other manufacturers who might benefit. Existing inspection systems such as machine vision have not been universally adopted in our industry because they're expensive and require considerable space. So there’s no question that the need for AI-enabled inspection systems is critical. We hope, through machine learning, we’re bringing even more safe and reassuring products to more people around the world.

Source: Google Cloud


How App Engine helped power Super Mario Run

When Nintendo invited app developer DeNA to collaborate on its release of Super Mario Run last year, both companies knew they had a unique challenge on their hands. It wasn’t just that the game would bring one of Nintendo’s most beloved characters, Mario, to smartphones for the first time. Nintendo was also planning a simultaneous worldwide launch, meaning the game would go live in 150 different countries at the same time. With a launch that massive, both Nintendo and DeNA knew system downtime would be unacceptable. That meant being sure that the game’s back-end could handle the demands of millions of new users on day one.

Here’s a little insight into how Nintendo and DeNA worked together to solve these challenges in advance of the game’s launch.

Super Mario - App Engine

Preparing for the future of game apps by leaving the back-end to a managed service

Nintendo and DeNA had already collaborated on the mobile title, Miitomo, so both knew how critical a strong back-end would be for Super Mario Run. After weighing their options, Kenta Sugahara, team leader for DeNA’s System Development Division, recommended using Google App Engine.

“When Miitomo was released last spring,” explained Sugahara, “the back-end was constructed almost entirely on-premises. This inevitably meant resources were used up on operations, obstructing efficient development in some respects. Although it was working at the time, I knew it would become increasingly difficult to work on more titles without changing our approach. Also, at that time, we learned that projected traffic for Super Mario Run would be massive — even by our standards as experienced smartphone app developers. That’s why we proposed using a managed service like App Engine.”

But using App Engine meant they’d need to rebuild the game’s back-end entirely from scratch. With less than six months before the release date of Super Mario Run, Sugahara and team knew they had their work cut out for them.

Working together towards a “crazy target”

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System organization diagram (using Google Cloud Platform)

With a simultaneous launch in 150 countries just months away, the work began.

One major reason DeNA and Nintendo chose App Engine was its ability to implement services demanding high levels of availability. Because they were anticipating a massive traffic spike on launch day, it was important that their cloud platform had the ability to scale quickly. App Engine’s auto scaling can automatically add or remove instances in line with traffic volume, and can be optimized in units of milliseconds. Adding to that, DeNA also compiled and shared estimation sheets with Google so they could anticipate the load on various services on day one. All this helped ensure they wouldn’t risk downtime while the systems where scaling.

With launch day rapidly approaching, load testing also became a major priority. Using Google Cloud Datastore, DeNA was able to complete a test with 3 million accesses per second. This gave both DeNA and Nintendo confidence that Super Mario Run’s back-end would be more than capable of withstanding the projected number of accesses when the game went live.

Looking toward the future

All of DeNa and Nintendo’s hard work paid off when Super Mario Run launched last December. Although there were more than 40 million downloads in the first four days alone, the launch went off without a hitch.

Now the teams are looking forward to tackling new challenges. A system like Super Mario Run generates log data in huge volumes, so plans are already in the works to use Google BigQuery to analyze those logs and apply any learnings to future app development. They’re also using their experiences with Super Mario Run and App Engine for the development of new games, like the recently released Fire Emblem Heroes. We look forward to seeing what they do next.

Super Mario Run is available for iOS and Android.

Source: Google Cloud


G4NP Around the Globe – Zooming in on Action Against Hunger

Every dollar and minute count to further your cause and focus on your mission. We’re pleased to highlight nonprofits who were able to make greater impact with fewer resources by using Google tools—from G Suite to Google AdGrants–made available through Google for Nonprofits (G4NP) at no charge.

Varying in size, scope, and timezones, these nonprofits from around the world share one thing in common: utilizing the G4NP suite of tools to help their specific needs. G4NP offers nonprofit organizations across 50 countries access to Google tools like Gmail, Google Calendar, Google Ad Grants and more at no cost. This week, we’ll take a look at how the nonprofit Action Against Hunger utilizes these tools to increase productivity, visibility, and donations,  in order to improve lives in  the communities they serve.

Action Against Hunger

In 2016 alone, Action Against Hunger provided nourishment to over 1.5 million starving children(1). In order to save lives with nutritional programs, Action Against Hunger looked to Google for aid—not for food, but for technology. Action Against Hunger now utilizes five Google technologies that have drastically improved their ability to save lives around the globe.

Raising Awareness with  Google Ad Grants & Analytics

For major international emergencies, like the Ebola outbreak or the South Sudan famine, Action Against Hunger needs a way to inform people and recommend ways to get involved. With Ad Grants, the nonprofit activates targeted keywords relating to the crises to drive people to their page and empower them to take action. Google Analytics then allows them to track their effectiveness and adjust accordingly to increase engagement and improve their fundraising techniques. With this data-driven strategy and the tools’ ability to optimize campaigns, Action Against Hunger has nearly doubled funding year-over-year. In fact, Ad Grants brought 158,000 people to their website in the past year alone, raising $66,000 which is equal to treating 1,466 hungry children.

Ad Grants brought 158,000 people to their website in the past year alone, raising $66,000 which is equal to treating 1,466 hungry children.

Increasing Productivity with  G Suite

When working with a global network and managing hundreds of programs abroad, collaboration and communication are key. After experiencing unnecessary latencies in their operations, Action Against Hunger has since adopted G Suite which streamlined their workflow. The nonprofit is especially fond of Gmail, Hangouts, and Drive where Action Against Hunger employees can message each other quickly, share files securely, and collaborate on Docs in real-time—avoiding duplication of efforts and saving time.

Fundraising with One Today & YouTube

To drive donations and expand awareness to broad audiences, Action Against Hunger uses One Today, a Google app that allows users to easily donate $1 or more towards causes they care about. Campaigning on One Today on World Food Day in 2016,  Action Against Hunger raised more than $1,200 in support of their cause with each dollar going directly helping those in need—the equivalent of feeding 1,000 hungry children. Additionally, Action Against Hunger creates and shares content on YouTube to reach their global audience, and is  beginning to use the YouTube donation cards to further increase donations. The large exposure and website referrals from both YouTube and Google+ helped Action Against Hunger raise over $20,000.

Using Google products Action Against Hunger gained extra time and energy to focus on what really matters: feeding the hungry.

To read more aboutAction Against Hunger’s story and learn how they used Google tools so effectively, visit our Google for Nonprofits Community Stories page. Stay tuned in the coming weeks for more inspirational stories about nonprofits using technology to help their cause.


To see if your nonprofit is eligible to participate, review the Google for Nonprofits eligibility guidelines. Google for Nonprofits offers organizations like yours free access to Google tools like Gmail, Google Calendar, Google Drive, Google Ad Grants, YouTube for Nonprofits and more. These tools can help you reach new donors and volunteers, work more efficiently, and tell your nonprofit’s story. Learn more and enroll here.

Footnote:  Statements are provided by Nonprofits that received products as part of the Google for Nonprofits program, which offers products at no charge to qualified nonprofits.


Source: Google Cloud


How we’re collaborating with Citrix to deliver cloud-based desktop apps

Businesses of all types are accelerating their transition to the cloud, and for many, desktop infrastructure and applications are part of this journey. Customers often tell us they want to be able to use their current desktop applications from any device and any place just as easily and securely as they can use G Suite.

That’s why today, we’re announcing a collaboration with Citrix to help deliver desktop applications running in a cloud-hosted environment.

Managing and delivering hosted desktop applications requires several pieces of technology: Google brings highly scalable and reliable infrastructure, a global network to reach customers and employees wherever they may be, and a team of security engineers who work to keep Google Cloud customers secure. Citrix brings the application management, backup and redundancy from XenApp, its desktop virtualization suite, and application delivery with Netscaler. Finally, Google Chromebooks and Android devices together with Citrix XenApp offer a highly secure, managed end-point that provide users a safe and user friendly experience on which to use applications.

All this requires close partnership and excellence in engineering. Google and Citrix have collaborated for years and we're expanding that relationship today in a few key ways:

  • Simplifying the path for customers to more securely transition to the cloud by bringing Citrix Cloud to Google Cloud Platform (GCP)

  • Bringing the application load balancing expertise of Netscaler to the world of containers via Netscaler CPX on GCP

  • Integrating Sharefile with G Suite to use Gmail and edit and store Google Docs natively.

  • Expanding use of secure devices with Citrix Receiver for Chrome and Android link

This collaboration helps address key challenges faced by enterprises moving to the cloud quickly and securely. Both Google and Citrix look forward to making our products work together and to delivering a great combined experience for our customers.

Source: Google Cloud


Announcing the winners of our Machine Learning Startup Competition

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On Wednesday, July 12, Google Cloud hosted the finals of its Machine Learning Startup Competition in San Francisco. Launched at Google Cloud Next ’17 with our sponsors Data Collective and Emergence Capital, the competition aimed to bring together the best early-stage startups implementing machine learning. According to Fei-Fei Li, Google Cloud Chief Scientist of AI/ML, “AI will change the way we live and work and it’s happening at a faster pace than most people think.” We received more than 350 applications from startups across the U.S. that are leveraging machine learning to improve healthcare, financial services, retail, IoT and many other sectors.

From this strong group, 10 finalists were selected to compete for investments and the “Built with Google” grand prize of $1 million GCP credits:

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At the event, finalists took the stage to share their technology and vision with our expert judges.

Finalists had just three minutes to pitch and three minutes of Q&A to convince the judges.  They also spoke to an audience of investors representing  over 40 of Silicon Valley’s top venture firms.

After careful deliberation and debate, judges selected the following winners:

Built with Google — Grand Prize Winner ($1M in GCP Credit) — PicnicHealth

PicnicHealth creates training data for precision medicine. By engaging patients directly they provide life sciences studies with complete, structured outcomes data for any patient, from any source. To date, PicnicHealth has collected and structured 500,000 records from 5,000 different health care facilities. Their current customers include 23andMe, the National Institute of Health, Stanford, Sanofi Genzyme, and Biogen. Already leveraging Google Container Engine (GKE) and BigQuery, they plan to use the $1M in GCP credit to scale their machine learning efforts on Cloud Machine Learning Engine, Cloud Vision API, and Genomics API.

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Congrats to the PicnicHealth Team

Built with Google Prize, Runner-Up ($500K in GCP Credit) - LiftIgniter

LiftIgniter is a machine learning personalization layer powering user interactions on every digital touchpoint. Built by the team behind YouTube’s recommendation algorithm, LiftIgniter runs their full stack on GCP. LiftIgniter’s customers include Vevo, Fandom, and Tableau.

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Adam Spector accepting LiftIgniter award

In addition, Data Collective and Emergence Capital selected two startups that are eligible to receive an investment of up to $500,000:

Data Collective Choice Winner — Brainspec

Emergence Capital Choice Winner — LiftIgniter

All remaining finalists will receive $200K in GCP credits and technical assistance from Google Cloud to support the next stages of their companies. We want to thank our sponsoring venture capital sponsors, DCVC and Emergence Capital, and our supporting sponsors A16Z, Greylock, KPCB, GV, NEA, Sequoia.

A special thanks to all the startups who traveled many miles and spent countless hours preparing to participate in the competition.

The competition is just one of the many ways Google is focusing on machine learning and startups. Gradient Ventures recently launched to fund early-stage startups focused on artificial intelligence.

For more information on the Google Cloud Startup Program, check out our website.

Source: Google Cloud


Nutanix and Google Cloud team up to simplify hybrid cloud

Today, we’re announcing a strategic partnership with Nutanix to help remove friction from hybrid cloud deployments for enterprises. We often hear from our customers that they’re looking for solutions to deploy workloads on premises and in the public cloud.

Benefits of a hybrid cloud approach include the ability to run applications and services, either as connected or disconnected, across clouds. Many customers are adopting hybrid cloud strategies so that their developer teams can release software quickly and target the best cloud environment for their application. However, applications that span both infrastructures can introduce challenges. Examples include difficulty migrating workloads such as dev-testing that need portability and managing across different virtualization and infrastructure environments.

Instead of taking a single approach to these challenges, we prefer to collaborate with partners and meet customers where they are. We're working with Nutanix on several initiatives, including:

  • Easing hybrid operations by automating provisioning and lifecycle management of applications across Nutanix and Google Cloud Platform (GCP) using the Nutanix Calm solution. This provides a single control plane to enable workload management across a hybrid cloud environment.

  • Bringing Nutanix Xi Cloud Services to GCP. This new hybrid cloud offering will let enterprise customers leverage services such as Disaster Recovery to effortlessly extend their on-premise datacenter environments into the cloud.

  • Enabling Nutanix Enterprise Cloud OS support for hybrid Kubernetes environments running Google Container Engine in the cloud and a Kubernetes cluster on Nutanix on-premises. Through this, customers will be able to deploy portable application blueprints that target both an on-premises Nutanix footprint as well as GCP.

In addition, we’re also collaborating on IoT edge computing use-cases. For example, customers training TensorFlow machine learning models in the cloud can run them on the edge on Nutanix and analyze the processed data on GCP.

We’re excited about this partnership as it addresses some of the key challenges faced by enterprises running hybrid clouds. Both Google and Nutanix are looking forward to making our products work together and to the experience we'll deliver together for our customers.

Source: Google Cloud


How STEM tools on Chromebooks turn students into makers and inventors

Editor's note: Over the last year, we’ve introduced new ways for students to develop important future skills with Chromebook tools, including active listening and creativity. Yesterday at ISTE we announced our latest bundles in this series, curated in collaboration with educators. In this post, we dive into the STEM tools on Chromebooks bundle, designed to help students become makers and inventors. Follow our updates on Twitter, and if you’re at ISTE in San Antonio, visit us at booth #1718 to learn more and demo these tools for yourself.

Students everywhere are exploring important concepts in science, technology, engineering and math (STEM), with a level of sophistication that’s rising every year. They’re also developing skills like problem solving and collaboration that they’ll need in higher education and, eventually, in their careers, while being exposed to real-world opportunities to be makers.

“If we want a nation where our future leaders, neighbors and workers have the ability to understand and solve some of the complex challenges of today and tomorrow, building students’ skills, content knowledge and fluency in STEM fields is essential,” the Office of Innovation & Improvement, U.S. Department of Education noted in a statement in January, 2017.

To help school districts provide more STEM opportunities to students, we’re now offering a bundle of STEM tools on Chromebooks, designed to to help students become inventors and makers. These tools are available at a special discounted price and may be purchased alongside Chromebooks or independently from U.S. Chromebooks resellers.

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Let’s take a deeper look at the tools in the STEM bundle.

The Dremel 3D40 3D Printer was developed by Bosch, a company that has made reliable tools for builders and hobbyists for over 80 years. About the size of a microwave oven, a 3D printer “prints” solid objects, layer by layer. The 3D40 3D Printer supports design tools such as Tinkercad and BlocksCAD, that help students create three-dimensional versions of just about anything they can dream up.

Michael Miller is a K-5 technology teacher and high-school computer science teacher for Otsego Public Schools in Otsego, MI. “Students are being exposed to technology that’s now used in a lot of fields. Medical, dental, the food industry—they’re all using 3D printers,” he says. “It will definitely make students more future ready.”

Miller uses a 3D40 3D Printer with Chromebooks in his elementary and high school classes. Depending on the class, students use the tools to create anything from a light saber to a miniature model of a Wright brothers’ airplane. From components for robots to mouthpieces for flutes, his students bring a range of personal interests to the design and printing process.

It brings what they imagine in their head into their lives. Michael Miller Technology teacher, Otsego Public School

Although students often work on individual projects, Miller encourages them to solve problems together as a team. “If they need help, I expect them to look to their neighbor first before coming come to me.” Miller also sees how 3D printing can be a way to engage female students, who are often underrepresented in STEM fields today, as well as students who are less likely to speak up in class. “I had a high school student—a very reserved student—and it helped him feel more ownership in the class. It gave him a greater sense of belonging when he could make something.”

The littleBits Code Kit combines block-based visual coding, powered by Google’s Blockly, with programmable physical “bits” that are electronic color-coded building blocks that snap together with magnets. Using the Code Kit, which is designed to be accessible to a wide range of grades, students have fun building and coding games, all while learning the foundations of computer science. The kit also comes with lessons, video tutorials, getting started guides and other resources for educators and students.

Rob Troke, a computer science teacher at James Denman Middle School in San Francisco recently took a sixth-grade class to I/O Youth at Google’s headquarters in Mountain View, CA. There, his students used the littleBits Code Kit to program light and sound patterns on a physical Bit. They quickly learned about programming logic such as loops and variables.

“I was happy to see how engaged the kids were,” he says. “It maintained their interest the entire hour, whereas with other apps and tools, I’ve seen the novelty wear off after 15 minutes.”

For some students, having a physical object linked to a coding activity helps bring additional context to computer science. It also brings electrical and mechanical engineering, often overlooked subjects in K-12, into the classroom. “Having things to play with, to figure out what they are, what they do, is extremely helpful… it’s like robotics, but without the robot,” Troke says.

Dremel’s 3D40 3D printer and littleBits Code Kit, along with free programs created by Google—like CS First and Applied Digital Skills—help bring STEM concepts to life in creative and tangible ways. To learn more about these and other educational tools, please visit g.co/educhromebookapps, check out the websites, or contact your school’s Chromebook reseller. And follow @GoogleForEdu on Twitter to see all that's launching at ISTE.

Source: Google Cloud


As G Suite gains traction in the enterprise, G Suite’s Gmail and consumer Gmail to more closely align

Google’s G Suite business is gaining enormous traction among enterprise users. G Suite usage has more than doubled in the past year among large business customers. Today, there are more than 3 million paying companies that use G Suite.   

G Suite’s Gmail is already not used as input for ads personalization, and Google has decided to follow suit later this year in our free consumer Gmail service. Consumer Gmail content will not be used or scanned for any ads personalization after this change. This decision brings Gmail ads in line with how we personalize ads for other Google products. Ads shown are based on users’ settings. Users can change those settings at any time, including disabling ads personalization. G Suite will continue to be ad free.

The value of Gmail is tremendous, both for G Suite users and for users of our free consumer Gmail service. Gmail is the world’s preeminent email provider with more than 1.2 billion users. No other email service protects its users from spam, hacking, and phishing as successfully as Gmail. By indicating possible email responses, Gmail features like Smart Reply make emailing easier, faster and more efficient. Gmail add-ons will enable features like payments and invoicing directly within Gmail, further revolutionizing what can be accomplished in email.

G Suite customers and free consumer Gmail users can remain confident that Google will keep privacy and security paramount as we continue to innovate. As ever, users can control the information they share with Google at myaccount.google.com.

Source: Google Cloud


The SAP-Google data custodian partnership

In March of this year, SAP and Google partnered to advance innovation, agility and global reach for enterprises adopting the public cloud. As part of our collaborative development and solutions integration, we are working on a data custodian model that allows customers with specific needs to manage sensitive data on a public cloud platform.

To fully benefit from cloud computing, enterprises need to store and process their sensitive data on public cloud platforms, while complying with regulations and managing unauthorized access risks. Enterprises often need to address these requirements as part of a broader governance, risk and compliance solution for the public cloud. 

The data custodian model

Google Cloud Platform (GCP) already offers robust security capabilities and extensive compliance with public cloud security and privacy standards. To further increase customer trust, the data custodian model allows SAP, a trusted enterprise solution provider, to act as the custodian of the customer’s data on GCP. This provides greater transparency and separation of controls.

With the data custodian model, we envision enterprises defining a set of controls about how they want to handle their data on GCP, then relying on SAP, as the data custodian, to continuously monitor compliance to these controls and manage exceptions as needed. A current focus is on data access transparency for GCP services that store or process customer data. In the coming months, SAP and Google will continue to work together to enable custodian oversight and control over handling customer data on GCP. 

What are the benefits for customers?

Enterprises can benefit from the data custodian model in several ways. They can leverage SAP’s deep knowledge of GCP’s security approach, controls and workflows instead of building that expertise in-house. With SAP as a data custodian, customers have additional confidence that their data is accessed and stored in compliance with their defined data sovereignty, privacy and protection policies.

In addition, with this partnership, SAP and Google are extending and integrating their product portfolios, including GCP and G Suite to provide even greater value to customers. Look to SAP and Google to continue to collaborate on solutions like the data custodian model to enable the next generation of digital services.

Source: Google Cloud


How The New York Times used the Google Sheets API to report congressional votes in real time

There’s a common phrase among reporters: “The news never sleeps.” This is why many news outlets rely on cloud-based productivity tools like Google Docs and Sheets to share information, check facts and collaborate in real time. And The New York Times is no exception.

In May 2017, the U.S. House of Representatives voted on a new health care law affecting millions of Americans. To report the news as fast as possible, The Times’ editorial team used Sheets to tally and display House votes in real time on NYTimes.com.


Engaging voters with the Sheets API

“People want to feel connected to the decisions their legislators make as soon as they make them,” said Tom Giratikanon,  a graphics editor at The Times. But rules in the House chamber make reporting on how every representative votes in real time difficult. Photography is restricted on the assembly floor, and there is a delay until all votes are displayed on the House website—a process that can sometimes take up to an hour.

To get around this lag, Giratikanon’s team used the Google Sheets API. The editorial team dispatched reporters to the chamber where they entered votes into a Google Sheet as they were shown on the vote boards. The sheet then auto-populated NYTimes.com using the Sheets API integration.

Says Giratikanon: “It’s easy to feel like decisions are veiled in the political process. Technology is a powerful way to bridge that gap. Sharing news immediately empowers our readers.”

It’s easy to feel like decisions are veiled in the political process. Technology is a powerful way to bridge that gap. Tom Giratikanon Graphics Editor, The New York Times
House votes

How it worked

To prep, Giratikanon tested the Sheets integration ahead of the House vote. He created a sheet listing the names of legislators in advance, so his team could avoid typos when entering data on the day of the vote. Next, he set up the Sheet to include qualifiers. A simple “Y” or “N” indicated “yes” and “no” votes.

After a few practice rounds, Giratikanon’s team realized they could add even more qualifiers to better inform readers–like flagging outlier votes and reporting on votes by party (i.e., Democrats vs. Republicans). The editorial team researched how each of the 431 legislators were expected to vote in advance. They created a rule in Sheets to automatically highlight surprises. If a legislator went against the grain, the sheet highlighted the cell in yellow and the editorial team fact-checked the original vote to reflect this in the article. Giratikanon also set up a rule to note votes by party.

As a result, The Times, which has roughly 2 million digital-only subscribers, beat the House website, reporting the new healthcare bill results and informing readers who were eager to follow how their legislator voted. 

NYT GIF

Try G Suite APIs today 

You can use Sheets and other G Suite products to help speed up real-time reporting, no matter the industry. Get started using the Sheets API today or check out other G Suite APIs, like the Slides API, Gmail API or Calendar API.

Source: Google Cloud