The Right Datawarehouse Helps Fight Climate Change, While Improving Customer Experience
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When you think about climate change, you might not consider a daily commute to work or a drive around town as big contributing factors. And yet, transport is the fastest growing source of CO2 emissions from fossil fuel, which in turn is the largest contributor to climate change. This is the key insight behind the mission of Spanish carbon-neutral multinational Acciona. The group, which provides sustainable solutions for infrastructure and renewable energy projects across 65 countries, launched an electric scooter sharing service called Acciona Mobility in 2018.
Aiming to contribute to the decarbonization of the transport sector while helping relieve traffic congestion within cities, Acciona Mobility enables users to rent electric scooters powered 100% by energy from renewable sources. Users can find, reserve, and rent the scooters at the tap of their mobile screens via the Acciona Mobility application and pay on the basis of minutes spent riding. The app, which is available free to download online, is also the gateway through which new users can register for the service, which welcomes anyone with a valid driver’s license for motorbikes. After having their right to drive, identity, and card details validated, citizens can start enjoying the service, available 24 hours a day, every day of the year.
“We needed to deploy a reliable infrastructure for a new service before understanding exactly what its scale and demand would be. Our strategy was to adopt a serverless architecture that can grow with us to sustain our long-term vision. This kind of thinking led us directly to Google Cloud.”
—Jose Luis Rosell, CIO Services Division, Acciona
“Acciona Mobility is exciting because it stems from our vision to make people’s lives easier in a sustainable way,” says Jose Luis Rosell, CIO of Acciona’s Services Division. “40% of the pollution near cities comes from private transportation. We want to help solve that problem with zero-carbon, electric, multimodal transportation that’s also convenient for people to use.”
However, Jose Luis says that building the appropriate IT infrastructure to bring the new service to life required external support. “We needed to deploy a reliable infrastructure for a new service before understanding exactly what its scale and demand would be. Our strategy was to adopt a serverless architecture that can grow with us to sustain our long-term vision,” he explains. “This kind of thinking led us directly to Google Cloud.”
Developing the transportation platform of the future
Because Acciona was already a Google Cloud customer, Jose Luis and his team reached the decision to develop the new service on Google Cloud with ease, as he explains: “We had a very positive experience working closely with the Google Cloud team during Acciona’s first project on Google Cloud, and that collaborative mentality is very important for us. We feel Google Cloud is a trustworthy partner that is willing to take risks with us and is flexible enough to lead us through business uncertainty with the right technology,” he says. “So we were confident that its technology and people could help us bring the Acciona Mobility service from theory to reality.”
This time, Acciona partnered with cloud consultancy Altostratus as well, to consult on a selection of Google Cloud products that would help develop the application quickly by making use of managed services. Acciona’s strategy was to launch the new services first in Madrid, where the company is headquartered, and then spread the vision across more cities later, if the idea worked out well in practice. Due to the scale of the project and its data needs, BigQuery was chosen as the primary data warehouse to store all the information related to the service, such as the location of scooters and their availability status. With all its data readily available on BigQuery, Acciona is able to run specific queries that help it to gain insights such as which urban areas have the highest volume of scooters being rented. Knowing this, Acciona can reorganize availability to make sure there are always enough scooters in that specific location.
Integrating it with Pub/Sub and Dataflow, Acciona ensures that the data generated by scooters is ingested and processed in real time so that users searching for scooters nearby, using the Acciona Mobility application on their phones, can always have up-to-date information at hand. Using Google Maps Platform APIs such as the Directions API, Acciona’s mobile app translates the geographical coordinates of scooters into an easy-to-read address displayed beside a visual map. Using Cloud SQL, Acciona automates the storage capacity management of the database as the number of scooters and active users grow.
Meanwhile, Google Cloud Armor protects the service against cyber breaches and distributed denial of service attacks to keep it running uninterrupted. In addition, Google Cloud itself helps to reinforce the cyber security measures with secure-by-default managed services, such as data encryption. “The managed services mean more resilience and more uptime, because we don’t need to worry about maintenance or external threats,“ says Jose Luis of the solutions protecting all information generated by the new service, which as CIO is a topic he holds dearly. “I face cybersecurity issues more confidently as a Google Cloud partner,” he adds.
“Today, we have data coming in every 15 seconds from 10,000 electric scooters around Europe. BigQuery and Cloud SQL-managed services help us handle that data very carefully and with precision so that our users don’t experience any lags when searching for scooters or returning them.”
—Jose Luis Rosell, CIO Services Division, Acciona
Expanding an 100% sustainable transport solution throughout Spain
Within two months of the decision to deploy Acciona Mobility on Google Cloud, the service was ready for launch, filling the streets of Madrid with 500 electric scooters that run 100% on renewable energy. By 2020, the number of scooters has grown to 10,000 and the service has scaled to multiple cities in and outside of Spain, including Lisbon, Milan, and Rome. To date, more than 3.5 million intracity rides have used the Acciona Mobility app, reducing CO2 emissions by more than 1,000 tons.
“Today, we have data coming in every 15 seconds from 10,000 electric scooters around Europe. BigQuery and Cloud SQL-managed services help us handle that data very carefully and with precision so that our users don’t experience any lags when searching for scooters or returning them,” says Jose Luis of the fast-growing scale of the project.
“Google Cloud enables us to keep our services always available and to scale quickly to respond to its growing demand. Our ratings have been very positive as a result. More importantly, our vision of transportation as a cleaner, more sustainable commodity is really spreading and resonating with the public.”
—Jose Luis Rosell, CIO Services Division, Acciona
Set to continue expanding, Acciona is using Google Kubernetes Engine to make sure the Acciona Mobility application has the computing capacity it needs to continue scaling to reach new markets and serve more users. On that note, Google operations tools (formerly Stackdriver) are also being deployed to help monitor, troubleshoot, and improve the performance of the application as it scales.
“Google Cloud enables us to keep our services always available and to scale quickly to respond to its growing demand. Our ratings have been very positive as a result. More importantly, our vision of transportation as a cleaner, more sustainable commodity is really spreading and resonating with the public,” he concludes.
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Can Users Just Ask Questions of Data in BigQuery and Get Answers?
At the root of every data insight lies a question. For a non-technical user, getting answers to an ad-hoc question, not found in existing dashboards or reports, has been the burden of BI teams for ages.
Data QnA, a new service that empowers business users to simply ask questions of their data in BigQuery, using natural language, and get an answer immediately can help.
In this video, Abhishek Kashyap, Product Manager, Google Cloud introduces Data QnA and demonstrates how it can be used.
Then Fabrice Nico, Data and Robotic manager, Veolia, a global leader in water, waste, and energy resource management solutions, will share the companies journey and explain how they’re using Data QnA to democratize access to analytics.
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Everything You Need to Know About Google Cloud ML Engine 101
Machine learning is all around us today. But data scientists and IT teams tasked with creating models have a hard time bringing together the right mix of ingredients—from data, infrastructure, tools, and APIs—to do their jobs effectively.
Google’s Cloud ML Engine eases many of the challenges data scientists and IT teams face. It’s a managed service that allows businesses to build and deploy their own models using any type or any size of data.
With Google’s Cloud ML Engine, data scientists can create models for training and prediction. And it provides APIs for these two building blocks.
Nikhil Kothari, Senior Staff Software Engineer, Google Cloud, breaks down Google’s Cloud ML Engine. He shows you how to use it as a service, so that data scientists can focus on data and on building models instead of managing infrastructure.
Beyond Traditional Learning: AI-based Online Learning Platform and Google Cloud Solutions Push Learners to Get Ahead
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The combination of a vital need for IT experts among businesses and a digital skills gap is making lifelong learning increasingly critical. Beyond professional development, learning new skills offers additional rewards from building peer connections to boosting your creativity. That’s why in 2020 Krishna Deepak Nallamilli and I launched KIMO.ai to reimagine how people approach learning, especially in developing markets. Our team is building the artificial intelligence needed to generate individual learning paths through a wide range of quality digital learning content.
Google Cloud and its Startup Program have been instrumental in connecting our team with the tools, people, processes, and best practices to grow our business.
Existing learning platforms lack engagement
Outside of traditional education settings, massive open online learning courses (MOOCs)—often modeled after university courses—can provide a flexible and affordable way to upskill or reskill. But the vast majority of people who participate in MOOC programs fail to complete courses. Based on our research, the challenge with existing learning platforms is a lack of engagement, primarily caused by limited direction on which skills to learn, whether AI, fintech, blockchain, or other in-demand disciplines.
We’ve also received feedback that many corporate learning management systems–developed as online training systems to upskill employees–tend to be poorly designed and time-consuming to use.
Overall, a significant challenge with most existing learning platforms is that they’re generic. For example, suppose you’re interested in learning about AI. In that case, you need AI-related coursework that applies to your industry and the job you want because AI in medicine is vastly different from AI in financial services. Today’s online learning options typically take a one-size-fits-all approach and fail to capture the nuances of what learners really need to get ahead.
Building a future-proof learning platform
The commitment to highly personalized, accessible learning inspired KIMO.ai, a platform that we believe is the future of education. Depending on your goals, current skills, location, and other factors, our AI-based platform will identify which coursework (and where to find those classes) to build the skills you need. The more personalized, relevant learning recommendations even take into account people’s preferences for podcasts, MOOCs, books, articles, videos, courses, publications, and more.
In a mix of cooperation and competition we call “coopetition,” KIMO.ai will regularly recommend courses from other established online learning systems if, based on our automated assessment, it’s the best option for a learner. There’s also the option to access free content only.
Google cultural alignment fosters trust
Our platform started with one developer exploring NLP models and Google APIs. As we’ve grown our team and launched our beta to 110,000 users in developing markets, we discovered there is a lot of interest in our platform, and we believe we can make a significant impact. In feedback forums, we also learned that we need to focus our efforts on the mobile experience to improve engagement since 99% of the beta testers use mobile devices.
Beyond our team’s high level of trust in Google Cloud solutions, our team also appreciates the cultural alignment with Google. We value Google’s developer-centric approach and rely on tools like Dataflow for batch data processing and Cloud TPU to reliably run machine learning models with AI services on Google Cloud. We also build all of our deployments on Google Kubernetes Engine (GKE), which makes it easy to manage all our containerized workloads
On the front end, Google App Engine makes it easy to deploy apps and experiment, and it integrates seamlessly with Firebase for authentication and more. BigQuery is our serverless data warehouse that efficiently scales to support the millions of articles, videos, and other learning resources we need to analyze to provide the targeted coursework recommendations our learners require.
As we grow our business having a network of trusted advisors is also extremely valuable. By working closely with DoIT International, the 2020 Google Cloud Global Reseller Partner of the Year, our team has access to their cloud, Kubernetes, and machine learning expertise. DoIT has already helped us quickly resolve IT issues and create analytics dashboards that give us insights to continually enhance our services.
Building for a growing industry
The dynamic edtech market is growing rapidly and estimated to become an $11B industry by 2025. We’re proud to be part of the next wave of personalized education that has the potential to empower people in developing markets and beyond to grow their skills with coursework tailored to their exact needs and how they like to learn. This year, we will deliver our platform to at least 400,000 more people. We’re excited to see how they use it and where it takes them.
If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
Home Depot’s Interconnected Retail Experience by Virtue of Google Cloud Migration for SAP Applications
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With nearly 2,300 stores, The Home Depot is the world’s largest home-improvement chain — a brand that professional contractors and DIYers alike have come to depend on. The home improvement industry continues to experience unprecedented demand and dramatic increases in online ordering accompanied by expanding consumer expectations for things like curbside pickup and same day delivery. The Home Depot’s decision to migrate to cloud-based infrastructure, including the migration of the company’s SAP applications on Google Cloud which began in 2017, has set it up for success in an increasingly digital world, and helped the company adapt to changing market conditions quickly.
Interconnected retail at scale
Building on a strong customer-first philosophy, The Home Depot aims to create what it calls interconnected retail—allowing customers to shop however, whenever, and wherever they want. “So many companies are focused on omni-channel retail,” explains Sam Moses, Vice President of Corporate Systems. “At The Home Depot, we wanted to take it to the next level. Interconnected retail puts the customer at the center of everything and enables them to shop in store, online, or both. Customers can begin a transaction online and continue in-store, or vice-versa.”
To support this strategy, the company’s SAP environment needed to be more agile. Running everything on-premises, from central finance to POS systems, meant that The Home Depot’s IT teams experienced redundancy and repetitive, manual processes. Their data warehouse needed an upgrade to process and analyze growing and increasingly diverse data sets. The Home Depot chose to migrate its SAP environment to Google Cloud to support both the velocity and scale needed for the business as well as critical analytics capabilities needed for its bold digital initiatives. “We chose Google Cloud to support our SAP implementation. Our decision had a lot to do with the relationship between Google Cloud and SAP and also for the applications and services that are offered by Google Cloud, like BigQuery, which are helping to enable data and analytics within our organization,” Moses explains.
After migrating its SAP applications—including S/4HANA, its customer activity repository (CAR), general ledger, e-commerce system, enterprise data warehouse and more to Google Cloud, the company now has the speed, scale and flexibility to tackle enormous spikes in the business, all while staying fully available for their customers. Additionally, The Home Depot was able to transform its financial systems and make them more agile to deliver critical information across multiple business functions in real time.
Maximizing data insights to support customer experiences
By migrating to Google Cloud, The Home Depot is leveraging Google Cloud analytics to build the industry’s most efficient supply chain including more robust demand forecasting, supplier lead times, estimated delivery times and more, all while maintaining better security than before. “We experienced unprecedented change in our customers’ behavior and their buying patterns, which puts a lot of pressure on our supply chain,” explains Moses. “So having the ability to leverage data and analytics gives us insights to know exactly what it is that our customers need.”
The company’s analysts now use BigQuery ML for machine learning directly against the company’s BigQuery data and use AutoML to determine the best model for predictions. The Home Depot’s engineers have also adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time—capabilities that were not as seamless in the on-premises environment.
With hundreds of projects on Google Cloud, The Home Depot’s cloud journey is well on track, but the company is always looking to the future. “As our customers’ needs have continued to evolve, and as technology has continued to evolve, our relationship with Google will continue to advance — to be able to innovate together, to be able to find new solutions together, to better serve our customers.”
Learn more about how The Home Depot is renovating its retail operation with SAP on Google Cloud.
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Deploying Real-time AI: A Walkthrough
Enterprises are increasingly looking to leverage streaming data (customer interactions, sales transactions, machine logs) for building new innovative features, increasing sales, and protecting against risks.
AI/ML (artificial intelligence/machine learning) technologies make it possible to amp up the detection of insights in large volumes of data: insights that otherwise would be impossible to gain with manual inspection.
Learn how Dataflow together with TensorFlow Extended (TFX) and Cloud AI are enabling the extraction of label and classification information from video clips and natural text, predicting probabilities and detecting anomalies, fraud, and patterns in streams of business data.
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