City of San Jose Ensures Critical Services Reach Community Using AI Translation

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San José is one of the most diverse U.S. cities, with residents speaking more than 100 languages. Several years ago, we set out to improve city community interactions through more equitable service management and delivery. This demanded a new approach to automating the intake of requests from a majority population whose first language is not English.
We first created the 311 portal and app, which was an important step as we effectively separated resident service requests from emergencies. The way we describe it to our citizens, you call 311 for a burning question, and 911 for a burning building. In the last fiscal year, San José 311 received nearly 210,000 contacts by phone and an additional 211,000 service requests through the SJ 311 app.
Through the portal and app, we gave citizens an omnichannel experience enabling them to interact with the city to request improvements, access useful information, and get emergency help when they need it.
In order to truly serve our diverse communities, we recognized language translation services would be required to offer truly equitable services to everyone. That’s when we started working closely with SpringML, Google Cloud, and other partners with involvement from our Mayor and City CIO.
Building public services with community engagement in mind
When we first rolled out the My San José website and mobile app, we used an out-of-the-box translation service that ended up not working. It had poor accuracy and did not meet our needs to provide all citizens with coherent services. After looking at many other options, we decided to partner with SpringML and Google Cloud to leverage the AutoML Translation with other technologies such as our virtual agent.
SpringML was selected through an open RFP process, and helped us to build and optimize our integrations, interfaces, and more between several systems, making the app and website more intuitive to manage. SpringML delivered the product we needed on time and up to specifications, and additional value came from the training sessions they provided to our team. This enabled us to understand everything we could do with AutoML and opened the door to other enhancements such as simplifying the vernacular used with our residents, making government access easier to navigate regardless of natural language spoken.
After establishing the My San José app’s translation capabilities using AutoML, SpringML also helped us incorporate Dialogflow virtual agents. Dialogflow also positions us to make modifications with our own staffing practices – something that has become increasingly important amid the frequent changes in service levels from COVID-19 response in the past year.
Responding to community needs
With the app up-and-running, our next step was to bring in community members to help with testing, improvements, and more. We wanted the app and the website to not just be something we provided to the community, but rather something they helped us build so they would readily adopt it.
Thanks to the greater accuracy of translation supported by Google Cloud services, we were able to leverage the expertise of a small pool of community members to evaluate translations. AutoML Translation and Glossary proved to be a powerful combination that pushed us closer to our goals.
Our primary targets were Spanish and Vietnamese translations. We are now seeing 90 percent accuracy in automated Spanish translations while Vietnamese translations continue to improve. We continue to work to simplify the language used in these services, which makes a big difference in terms of ensuring optimal language accessibility.
This work includes best serving our community members who primarily use phones to get in touch with us through 311 services. Using Google Cloud Contact Center AI, we have been able to effectively manage the calls we receive 24×7 and communicate with residents who speak Spanish as well as English. No matter which channel one of our residents choose to use to reach out, we can serve them efficiently.
A well-timed release
We’re proud of the work we’ve done. We’ve made many government services available to our community 24 hours a day, 7 days a week — accessible through many channels. Regardless of a person’s native language, the consistency of experiences enjoyed by everyone is improving every day thanks to AI. We’re also actively incorporating more language translation capabilities to better serve more people.
While we began this process several years ago, the recent integration of machine learning language translation with our customer relationship management system in late 2020 was very well-timed because we were able to incorporate this into our COVID-19 pandemic response.
We’re also beginning to work with other municipalities across the U.S. to share some of the lessons we’ve learned and success we’ve seen in hopes of furthering more equitable citizen services far beyond our City limits.
We are excited to continue working with SpringML, Google Cloud, and other partners to improve our city and the equity and quality of services that our residents enjoy.
Learn more about how you can work with a Google Cloud Partner here.
How the City of Memphis Uses Technology to Identify 75 Percent More Potholes

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At 340 square miles, the City of Memphis is among the largest in the United States in terms of land area. Memphis has over 6,800 lane-miles of city streets, enough to drive back and forth to Los Angeles four times. Keeping these streets well maintained and safe for citizens and visitors is a major priority for the city.
Lots of traffic, lots of roads, and a four-season climate prone to wintertime freeze-thaw-refreeze cycles means the opportunity for potholes. Although the city aims to fill potholes within five business days of notification, it can take longer, especially during winter and early spring. Last year, the city’s Public Works crews repaired some 63,000 potholes, only 20% of which were reported by residents. Approximately 32,000-man-hours each year are spent repairing potholes, with seasonal fluctuations requiring ten to twelve Street Maintenance crews working steadily during the winter months. Still, many went unreported, leading the city to flag pothole request resolution under “needs improvement” on its open data portal website.
Like many large cities, Memphis also struggles with vacant and blighted properties. Nearly 15,000 properties in Memphis are likely vacant, and city officials contend that many are owned by out-of-town investors who live elsewhere and do not take necessary restoration or maintenance steps. These properties can decrease the value of surrounding real estate and discourage new businesses and other residents from moving to an area. Citizen frustration and concerns over the number of blighted properties has made blight eradication a major focus of the City of Memphis.
Historically, residents reported potholes and blighted properties by calling 311, or more recently by using the Memphis 311 app. However, these reports only covered about 20 percent of the problems — often the worst cases. And by the time residents took the initiative to submit a 311 report, they usually weren’t feeling good about the situation.
Recognizing that potholes and vacant properties are often the most visible indicators of whether a city government is doing its job efficiently, Memphis Mayor Jim Strickland and CIO Mike Rodriguez began looking for ways they could apply technology to fix the problems. Mike approached Google for ideas, and Google recommended conducting a machine learning proof-of-concept (POC) with SpringML, a Google Cloud Partner.
“Memphis is focused on easy living, and we want to do everything we can to keep our citizens happy,” says Mike Rodriguez. “Working with Google and SpringML to reduce potholes and urban blight using machine learning and artificial intelligence was an easy decision.”
Bringing machine learning to city operations and budgets
The city’s goal is to detect potholes and abandoned properties by analyzing video footage of roads and residential properties. It wanted to classify potholes by width and depth, and share the information with workers who can repair them. For abandoned properties, it wanted to enable more strategic deployment of resources for homeowners citywide and take action to hold neglectful property owners accountable.
The POC began by training TensorFlow models for ML object detection using preconfigured AI Platform Deep Learning VM Images on Compute Engine. SpringML helped set up cameras and developed a user interface to collect pothole data and automate the 311 ticketing process.
Together, the teams analyzed 30 days of video from a moving city bus and high-resolution video from 360-degree cameras mounted to a code enforcement vehicle, overlaid with data from 311 reports. As the models were refined, accuracy quickly climbed from 50 percent to over 90 percent as models were taught to differentiate a pothole from a manhole cover or other object.
The city also imported routes, potholes, and paving data along with geolocation data from ArcGIS and Google Maps into BigQuery to better understand street conditions and the proximity of potholes to one another. BigQuery also analyzes city property records, tax records, 311 reports, and third-party survey data on-demand to predict where homes are starting to become run down and where neighborhood decay is most likely to occur. The SpringML team created a pilot analysis to begin vacant property protections and developed a user interface tool to interact with the model’s results.
“Google Cloud Platform made it possible for us to experiment with machine learning and artificial intelligence to help solve our city’s problems while working within the budget constraints of a municipal IT organization,” says Mike. “Google turned a ‘nice to have’ into a ‘let’s do this!'”
Identifying 75 percent more potholes
Memphis expects to substantially reduce the number of potholes on its streets, creating a better driving experience for residents and visitors alike. Because drivers won’t be as likely to swerve to miss a pothole, streets will be safer and friendlier to bicycles and scooters. Fewer potholes will also save the city between $10,000 and $20,000 annually in city claims that it pays out in cases where vehicle damage results from a pothole that was not addressed in a timely manner.
“Historically, Public Works has relied primarily upon Street Maintenance crews to proactively locate and fill potholes. As Memphis has over 6,800 lane-miles of public streets, it is a daunting task to reliably survey the entire system in an efficient and systematic way,” says Robert Knecht, Public Works Director for the City of Memphis. “The outcome of the data collected will be invaluable to Public Works so that it can ensure it is managing the city’s street system in a more proactive manner.”
Memphis will be able to better prioritize road maintenance based on condition and impact, increasing the efficiency of its Public Works road crews. Analyzing video of streets also gave the city visibility into issues it wasn’t previously aware of, such as curbs, gutters, and manhole covers that had been mistakenly paved over and need to be excavated. The ML process is easily transferrable to other concerns as well, helping the city identify illegal signs or spools of cable hanging on light posts that could be potentially unsafe.
Helping communities recover and thrive
Memphis is also having success in analyzing predictive trends to combat high rates of abandoned and blighted properties, surpassing 97.5 percent accuracy. “In the past, Public Works experimented with comprehensive, city-wide blight identification by using approximately 200 volunteers to survey and photograph over 237,000 city parcels. This effort was costly, took a long time to complete, and resulted in inconsistent data collection,” says Robert. “Blighted property conditions can change quickly in a city the size of Memphis. Now, with this new technology, Memphis will be able to make a significant difference in the efforts to proactively and comprehensively identify and manage blighted and substandard properties.”
Code Enforcement with better data-driven detection mechanisms enables the city to also identify cases where homeowners are not physically or financially able to keep up with the challenges of homeownership and make them aware of resources that are available to assist them. Memphis Code Enforcement can do a better job of finding people living in derelict properties that pose hazards to inhabitants’ health and safety, and help them fix those problems or find a new place to live.
“Using SpringML and Google Cloud Platform to detect indicators of vacant or blighted properties will help Memphis create safer neighborhoods that will be more attractive to businesses and home buyers,” says Mike. “Property values and employment will go up, crime will go down, and social services can be more focused and effective.”
Revolutionizing service delivery for citizens
Memphis is proving the viability of a cost-effective, cloud-based machine learning model that other cities can follow. The city is already looking into new applications of AI and ML that will further improve city services and help it build a better future for its 652,000 residents.
As part of his commitment to a transparent government, Memphis Mayor Jim Strickland created an open data policy that commits to releasing raw data and sharing it with citizens in a variety of downloadable formats. Going forward, this transparency will help citizens understand how their needs are being served and uncover new, innovative use cases for AI and ML.
“Our goal is to become a smart city, and technologies such as Google Cloud Platform and SpringML put us ahead of the game,” says Mayor Strickland. “Google understands data, and there isn’t a better company to help us analyze our data resources for actionable insights.”
Vizrt’s Story of ‘Lift and Shift’ and Delivering Phenomenal Performance with Google Cloud

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When moving software applications from on-premise hardware to the cloud, it often “just works,” but it’s never guaranteed. This is especially the case for applications that are hardware intensive. This blog post examines what happened when a media company took software for real-time video broadcasts into the cloud. We’ll share how we, Google Cloud, collaborated with media software provider Vizrt to meet the demanding requirements of an eSports broadcaster. Together, we delivered a solution that not only met but exceeded the expected performance from a cloud-based deployment.
Video broadcasting in the cloud
Video broadcasts are a very hardware-intensive workflow. By needing to process and store data streams in near-real-time, broadcasts stress GPU, memory, disk, and CPU. In addition, the performance requirements quickly increase as producers add additional video streams into the mix, such as in this case, an eSports broadcast.
Vizrt’s customer wanted to increase their broadcast production by doubling the amount of camera feeds from 8 to 16, to have a more compelling and elaborate production.
At the heart of the eSports broadcasters’ production was Viz Vectar Plus, Vizrt’s software-based 4K switcher. While the client wanted to move more of its production into the cloud, Viz Vectar Plus was designed initially for on-premise hardware. So, when they tried a straightforward “lift and shift ” to the cloud, it surprised no one that the software didn’t run as well. They turned to Google Cloud and Vizrt to make it run the way they needed it to.
Troubleshooting lift and shift
Initially, we suspected that the issue could be in the design of the cloud deployment, i.e., the configuration of the VM hosting the software. So the focus of our troubleshooting was to find a cloud configuration that 1) made sure the software worked to spec and 2) did so optimally considering costs, robustness, and performance. Furthermore, we wanted to ensure that all components met performance specifications, particularly throughput, IOPS, and network bandwidth, as this was a video media application. Only after we validated the cloud deployment would we ask Vizrt to investigate the code itself. We would:
- Set up a test environment.
- Benchmark the environment.
- Test various configurations.
- Validate that the vendor software was optimally using the configuration.
This high-level methodology is straightforward. However, we approached the details in a particular order, considering we were optimizing for broadcast video. We honed in on the optimal cloud configuration by testing the following elements, prioritized in order of expected impact:
- VM type: The VM type largely dictates the available memory and CPU configurations. However, because this was a VM workflow, we had to pick N1’s. Today, they are the only VM type that can be attached to GPUs, which are practically a requirement for broadcast video.
- Disk type: Video broadcasts require high I/O speeds to handle high-quality video streams. We went from an HDD to a much-faster SSD.
- CPU size: We increased the VM CPU cores from 16 in increments up to 32. Increasing CPU size indeed increased performance, but did not return the level of performance we needed.
- SSD size: We increased the SSD size (and the accompanying higher IOPS and throughput that comes with increasing the size) enabling more simultaneous recordings. Again, this only partially worked.
- Disk count. We noticed read/write problems when the application was reading/writing with a single drive. There are two typical ways to approach this: 1) Separate read/writes tasks among two discs and 2) striping the data streams across discs. Implementing these had improved but marginal improvements in performances.
After our testing, we arrived at the following optimal configuration:
- VM: n1-standard-32 instance w/ 500Gb boot drive
- GPU: 1 T4 GPU
- SSD: 1 persistent disk with 1TB*
* We would later determine that two SSDs for separate read and write operations would be more optimal
This configuration was able to produce between 6 – 12 streams. Compared to the on-premise target of 8 streams, this was about as good but was not the customer’s target of 16. So we would need Vizrt to take the ball from here to optimize the software itself.
Optimizing media applications for cloud
We provided Vizrt our recommended configuration, performance notes, and the following best practices that are generally applicable to cloud-based video workloads:
- Separate read and write operations to two different disks to enable higher performance for both operations.
- A second 1 TB SSD persistent disk can be attached to the VM instance to increase performance.
With this information, Vizrt engineers worked their magic, providing daily patches to test; with each daily iteration the overall solution was found quickly. Not only were they able to meet the broadcaster’s request of 16 feeds, but they were also able to go even further to 44. Over a 5x improvement by optimizing for the cloud!
Teamwork in troubleshooting
Because of the specialized nature of media and entertainment, workflow situations across multiple companies are common as specialized applications hand their work from one to another.
“By working in partnership with Google Cloud we managed to build a system that can scale in ways that probably none of us thought would be possible. This allowed Viz Vectar Plus to run fully in the cloud using NDI and opened up amazing possibilities for making shows,” Dr. Andrew Cross, President R&D, Vizrt Group. “We ended up with great feedback from the customer, who were appreciative of how Google Cloud and Vizrt collaborated on a solution.”
The results speak for themselves: A satisfied customer with over a 5x improvement in results. That’s what we call a good game.
G R Infraprojects Limited Turns to Google Cloud to Run Business-Critical SAP S/4HANA

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Road construction is booming in India. A recent report prepared by the India Brand Equity Foundation—a trust established by the Department of Commerce—pointed out that in FY 2019 alone, the country added 10,855 kilometers of highways to a road network that spans 5.89 million kilometers. This network is the second largest in the world and transports 90% of passenger traffic and 64.5% of all goods in the country.
The Indian Government has also earmarked road construction as key to plans to increase the nation’s GDP to $5 trillion in coming years, targeting road construction worth $212.8 billion in the two years from April 2020.
G R Infraprojects Limited is well positioned to support the government’s program. The business, which started as a contractor building roads in rural villages in India, now specializes in road engineering, procurement, and construction (EPC), a model whereby private construction firms build roads funded by the government.
The business now undertakes the processing of bitumen, manufacture of thermoplastic road-marking paint and road signage, and fabrication and galvanizing road-crash barriers. Its in-house integration model includes a design and engineering team, as well as manufacturing facilities in Rajasthan, Assam, and Gujarat.
The business recently expanded into rail—another area expected to benefit from extensive government investment—with its competencies including earthworks, materials supply, track lining, and bridge construction.
In this environment—and despite the economic impact of the coronavirus pandemic—G R Infraprojects Limited aims to substantially increase turnover and manpower over the next five years.
Best-in-class infrastructure key to success
Digital transformation is key to enabling growth while best-in-class IT infrastructure is one of the foundations on which the business seeks to build success.
G R Infraprojects Limited’s digital initiatives include deployment of a new document management system and corporate systems that enable remote monitoring, live tracking, effective real-time communication, and efficient data management.
Providing a scalable, reliable cost-effective infrastructure
But most important of all is providing a scalable, reliable, and cost-effective infrastructure to support a business-critical SAP enterprise resource planning system. Over the last few years, versions of SAP have enabled the organization to digitize processes and seamlessly run business-critical functions such as inventory management and finance.
G R Infraprojects Limited initially went live with SAP ECC6.0, with a few hundred team members using the system for business-critical tasks such as tracking stock level and movement and generating financial statements and reports for review and action.
“Google Cloud is a very big brand, so we were easily able to secure the trust from our executive and business teams to run an important system such as SAP S/4HANA on the platform.”—Sachin Kumar Agarwal, Head, Transformation, G R Infraprojects Limited
Lowering maintenance costs
However, as G R Infraprojects Limited grew, projects proliferated, and new markets emerged, the business elected to move to the cloud from an on-premises infrastructure. “We wanted to move because there were so many maintenance costs in on-premises solutions and cloud provided convenience to IT and the broader organization,” explains Sachin Kumar Agarwal, Head, Transformation at G R Infraprojects Limited.
The business also wanted to move to SAP S/4HANA to take advantage of features such as AI, advanced analytics, and machine learning to transform business processes. The system runs on the HANA database, an in-memory database with fast processing speeds and a simplified data model.
G R Infraprojects Limited selected Google Cloud to run SAP S/4HANA because, Sachin says, it is “much better than any other platform,” incorporates a wide range of features, and meets uptime requirements. The cloud service could also scale to support forecast growth without a sharp increase in cost.
Furthermore, Sachin adds, “Google Cloud is a very big brand, so we were easily able to secure the trust from our executive and business teams to run an important system such as SAP S/4HANA on the platform.”
“With Google Cloud, our speed and availability are controlled and optimized day by day. With such a scalable and dynamic platform, we are very happy with the performance.”—Sachin Kumar Agarwal, Head, Transformation, G R Infraprojects Limited
Successful transition with Infrabeat
G R Infraprojects Limited completed the project with assistance from partner InfraBeat over three months and SAP S/4HANA on Google Cloud went live mid-2019. “Our dedicated SAP team worked closely with Infrabeat to deliver the project successfully,” says Sachin. “We needed an experienced partner to assist with the implementation process and Infrabeat performed that role admirably. Both teams supported each other and worked to plan to deliver a great result.”
SAP S/4HANA runs on an infrastructure comprising virtual machine instances delivered through Compute Engine, Cloud Storage, and Cloud NAT to enable the secure transmission and receipt of packets to and from the internet.
G R Infraprojects Limited estimates the cost of running SAP S/4 HANA in Google Cloud is significantly lower than on alternative infrastructure options—freeing up budget for other business priorities.
In addition, moving SAP S/4 HANA to infrastructure as a service through Google Cloud has eliminated the need to assign internal team members to infrastructure management, allowing them instead to focus on higher-value activities.
Google Cloud also incorporates the security needed to protect the data and processes of SAP S/4 HANA from intrusion or disruption and ensure the uptime and continuity required of a business-critical system.
Optimized speed and availability
G R Infraprojects Limited’s decision to run SAP S/4 HANA on Google Cloud is delivering benefits on a daily basis. “With Google Cloud, our speed and availability are controlled and optimized day by day,” says Sachin. “We are very happy with the performance.”
Success with SAP S/4HANA has helped the business decide to move its remaining apps and data to the cloud when its on-premises servers and other equipment reach end of life. G R Infraprojects Limited is already running Active Directory in Google Cloud and Sachin says the cloud platform’s “fast and excellent services” made the decision easy.
“There are definitely instances of various upgrades of our systems and within the organization, and as we talk about our application and mobility requirements, we see Google Cloud playing a crucial role.”—Sachin Kumar Agarwal, Head, Transformation, G R Infraprojects Limited
Adding value
As G R Infraprojects Limited grows, Sachin adds, the business expects Google Cloud to continue to add value. “There are definitely instances of various upgrades of our systems and within the organization, and as we talk about our application and mobility requirements, we see Google Cloud playing a crucial role.”
TELUS and Google Cloud Partner to Move Towards a More Sustainable Future

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Environmental sustainability is a key priority for TELUS, a world-leading communications technology company. It continues to rank in the top 100 most sustainably managed companies in the world, and seeks to make a healthier planet for all by leveraging its global-leading technology, compassion to drive social change and reduce our collective carbon footprint through innovative technologies and sustainable business practices.
TELUS surpassed its sustainability objectives in 2019 and is now on a journey to procure all of its electricity from renewable or low-emitting sources by 2025. Next, it aims to achieve net carbon neutrality for its operations by 2030. TELUS has also been named to the Dow Jones Sustainability Index for 21 consecutive years, a feat unmatched by any other North American telecom or cable company. In 2021, it became the first company in Canada to release a Sustainability-linked bond (SLB) framework and complete an SLB offering, formally linking TELUS financing to its environmental performance.
“We’ve spent the last decade becoming a global leader in sustainability, helping make the planet healthier by ensuring that our operations are as environmentally responsible as possible,” said Geoff Pegg, Head of Sustainability and Environment at TELUS.
In part, TELUS’ strategy is focused on three key areas:
- Seek the best renewable energy options available
- Focus on migrating workloads to the cloud
- Embrace a multiplier effect through the use of sustainable partners
Renewable energy impact
Part of this environmental responsibility involves investing heavily in renewable energy sources through power purchase agreements (PPAs) that help renewable energy providers like wind farms and solar companies develop their infrastructure. TELUS executed PPAs with four Alberta-based solar and wind facilities to provide 100 per cent of its electricity load demand in a province where one-third of the grid is powered by coal.
As a technology company, electricity represents a large portion of TELUS’ energy needs: 80 percent of the operational carbon footprint comes from the power requirements for TELUS’ network and administrative buildings, Pegg explains. While TELUS is using renewable energy sources and low-emitting energy grids to power its buildings and network, there’s also the often-forgotten part of the carbon emissions equation: the energy it takes to power data centers. As the International Energy Agency recently reported, data centers represent 1 percent of the global electricity demand and that figure is expected to keep rising as the world increases usage of data-heavy technologies.
“It’s probably no surprise that everyone, whether you’re a business or a consumer, is concerned about reducing carbon emissions,” said Chris Talbott, the Google Cloud Sustainability Lead. “A lot of us think about the carbon emissions associated with our cars or with the electricity that powers our homes, but oftentimes we forget about the carbon emissions that come from the digital services that we use or the networks required to deliver that data.”
As a leader in sustainability, how can TELUS meet the energy demands of its customers while also protecting the environment? One way is through the company’s previously announced collaboration with Google Cloud. The two companies are working together to build a more sustainable world through technology and reduce TELUS’ carbon footprint, create value along the entire supply chain, and optimize industry solutions for social impact through data analytics and machine learning.
Taking a cloud first approach — reducing carbon emissions with green cloud computing
Google became carbon neutral in 2007 and has achieved 100 per cent renewable energy matching every year since 2017. Google has invested in renewable energy to match the electricity we use across our entire operations, including Google Cloud, meaning every workload that TELUS runs on Google Cloud has been matched with renewable energy purchases.
“The operational carbon footprint of running anything on Google Cloud is zero,” Talbott said. Also, by working with Google, TELUS gets the benefit of economies of scale using less electricity. Not only is TELUS leveraging Google data centers, it’s also relying on the digital collaboration made possible by Google Workspace to reduce the amount of travel required by employees attending meetings in different offices. Collaboration tools like Google Meet can reduce the carbon footprint of in-person conferences by 94 percent.
Google compensates for the environmental footprint of any electricity used in the data center and out to the edge network. “You can feel pretty good about using Google Meet because it’s carbon-neutral,” Talbott said.
Multiplier through sustainable partnerships — green cloud computing radiates out
By supporting TELUS in its environmental sustainability efforts, Google Cloud is also enabling TELUS to do the same for its various partnerships. For example, powered by Google Cloud’s infrastructure and data analytics capabilities, TELUS is partnering with Picacity (formerly NXN Digital) and Google Cloud to deliver an ecosystem of integrated smart technologies that enable cities to improve the lives of their residents.
From dynamic traffic signaling that reduces congestion and emissions, to data analytics that create smarter, more efficient city planning, the partnership is transforming the way municipalities operate in our increasingly digital world.The partnership is built on four foundational pillars of infrastructure and environmental sustainability, intelligent transportation, public safety and security, and health. In the case of intelligent transportation, this means sensors, cameras, and other devices are built into or near roads, sidewalks, and bike paths to provide data for innovative software to improve traffic flow in real time. The data can then foster informed decisions about infrastructure, city planning, fleet optimization, and public safety.
All of these environmental measures may seem small when compared with the enormity of the problem that is climate change, but as Talbott said, “Change begins with the small decisions we make every day such as paying attention to the practices of companies that we’ve come to rely on daily in the modern world. They may seem small and in the margins, but at scale, this is how we can make a real impact.”
Italian Utility Company Deploys its SAP Workloads on Google Cloud to Meet Sustainability Goals

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With more than 2.5 million customers, Italian utility company A2A is committed to delivering electricity, gas, clean water, and waste collection every day. More recently, the company made another significant commitment: To incorporate the principles of the “circular economy” into its way of doing business — part of the UN 2030 Agenda’s Sustainable-Development Goals — all while also aiming to double its client base by 2030. Growing rapidly but sustainably requires operating as efficiently as possible at every level of the organization, from operating smart meters to generating accurate demand projections. That’s why A2A chose to deploy its SAP S/4HANA ERP and the SAP BW/4HANA data warehouse on Google Cloud.
Roadblocks to innovation
Instead of a linear consumption model that starts with raw materials and ends with use and disposal, the circular economy is a continuous cycle that emphasizes repair, recycling, and the creation of materials rather than their disposal. To take an example from A2A’s own success story: The company keeps 99.7 percent of collected waste out of landfills.1 Of the UN’s sustainability goals, A2A is committing to the three most relevant to its industries:
- Ensuring availability and sustainable management of water and sanitation for all
- Ensuring sustainable consumption and production patterns
- Protecting, restoring, and promoting sustainable use of terrestrial ecosystems
Achieving A2A’s sustainability and customer-first strategies requires high scalability, rapid data ingestion, and rich, accurate analytics. None of this could be reliably supported with the company’s legacy on-premises SAP and Data Warehouse, especially given A2A’s projected growth and the increasing complexity of the data landscape, including IoT deployments and energy market liberalization.
Provisioning data infrastructure was also slow and complex. Simply adding a new metric could require increasing capacity by an order of magnitude. And analytical and transactional data lived in siloes, which created a fragmented and out-of-date view of each customer across sales and customer support teams. A2A’s fragmented data also made it difficult to take proactive action when changing priorities or processes required shifting focus from one data source to another.
With a data warehouse that refreshed only once every 24 hours, simple processes such as responding to a customer calling because their power has been cut off due to an unpaid bill became cumbersome.
Scalability was also a concern. With the on-premises solution, A2A needed to define the budget for its data warehouse over a two-year timeframe, but the rollout of new electricity meters — each sending data every 10 minutes — across Italy made those data requirements hard to predict.
The move to the cloud: From monolith to microservices
The move has been a giant step forward in A2A’s goal of meeting its data-driven, customer-centric strategy. In deploying its SAP systems to Google Cloud, A2A can take advantage of a highly flexible hybrid environment and powerful data management and analytics. It can replicate data from Salesforce, SAP, and other systems in BigQuery, which operates as a data lake with Google Cloud SQL, connected directly to Google Analytics and Google Ads for data-driven customer service, decision-making, and marketing.
“From BigQuery we can feed relevant information directly to the people who need it. Our customer operators work on Salesforce, so we use an OData protocol to embed real-time data in that platform. Elsewhere, we present the information through a dashboard, or with a BI component delivering one-page reports.” —Vito Martino, Head of CRM, Marketing and Sales B2C & B2B, A2A
By running SAP on Google Cloud, A2A can also count on an infrastructure platform that provides:
- Scalability. The robust data architecture on Google Cloud adapts to shifting and increasing demands without compromising on speed or availability, so A2A doesn’t have to worry about over- or under-provisioning as the rollout of smart meters proceeds.
- Speed. The new A2A data solution refreshes every five minutes instead of 24 hours, so the company can respond to its customers’ needs without delays. Customer operators working in Salesforce now receive real-time data from Google BigQuery so that, when a customer calls, operators can see accurate information in seconds. They can now offer value-added services and sustainable options tailored to the customer’s needs, from energy consumption to their preferred method of communication.
- Availability. With microservices orchestrated by Google Kubernetes Engine, the team can update the solution through continuous integration and delivery (CI/CD), eliminating the need for downtime when changes are required.
- Security and control. The A2A IT team uses Google Kubernetes Engine to orchestrate clusters of instances on Google Compute Engine, with Google Cloud Load Balancing and backups on Google Cloud Persistent Disk. Google Cloud Anthos ensures operational consistency across on-premises and cloud platforms.
Ready to grow the sustainable way
By moving to Google Cloud — the industry’s cleanest cloud, with zero net emissions — A2A is ready to grow quickly while locking down the efficiency it will need to meet its ambitious sustainability goals. “To bring sustainable utilities to market, we need to be both responsive to our customers and responsive to the internal needs of A2A,” explains Davide Rizzo, Head of IT Governance and Strategy at A2A. “Understanding what customers need in detail means we can improve their services and reduce their environmental impact at the same time.”
Learn more about the ways Google Cloud can transform your organization’s SAP solutions with scalability, speed, and advanced analytics capabilities.
1. Circular Economy: one of the four founding pillars of A2A’s 2030 sustainability policy | Drupal
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