Climate scientists face a deluge of environmental data to analyze and interpret from real-time sensors and satellites across the globe. The stakes are as high as our planet’s long-term future, but rapidly changing conditions are already impacting communities through extreme events like floods and wildfires as well as management of everyday resources. In this context, detailed environmental maps are key sources for urgent global issues like food security, water quality, and vegetation levels.

Scientists, researchers, and developers rely on state-of-the-art cloud computing tools like Google Earth Engine (EE) to detect changes, map trends, and quantify differences on the Earth’s surface. EE leverages the compute power of Google Cloud to combine a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities.

To find out more about how climate scientists address these complex research challenges, I spoke to Dr. Richard Fernandes, Research Scientist at Natural Resources Canada (NRCan). Dr. Fernandes is also a 2022-23 member of the Google Research Innovators Program, which provides technical and professional support to a global cohort of leading researchers. I asked him to describe how cloud computing and machine learning (ML) tools can support climate resilience research and drive awareness about climate sustainability.

Dr. Fernandes, can you give me an overview of your research in climate sciences?

My research focuses on mapping the status and trends of vegetation over Canada. Every month we generate maps of vegetation parameters, such as canopy cover at 20m resolution, to support environmental monitoring and assessment. These maps contribute to global datasets that are used to reduce uncertainty in weather and climate forecasts.

Canada has approximately 10 million square kilometers of land and the annual data volume of these maps is equivalent to streaming HD movies for over 750 hours non-stop. And that’s only the tip of the iceberg: the volume of input data we need to generate those maps is typically 100 times more. Unlike movie streaming services, we have to independently process each input pixel to locate it accurately, screen for clouds (and even the shadows of clouds), and then transform it into a vegetation parameter value like canopy cover using ML algorithms.

The volume of high-resolution data and the amount of compute we need are challenging and ever-increasing. Rather than dedicating servers 24/7 for constant monitoring, we rely on cloud computing and ML. Cloud computing allows us to manage all this data in a useful and accessible way. We have also been able to successfully use the Google Artificial Intelligence (AI) Platform to calibrate new ML models for third-party datasets.

How did you start working with Google Cloud?

I started using EE together with open source APIs for integration with Google Drive about four years ago. With the pandemic my research group has transitioned to using EE, Google Cloud, and Google Drive for both our Canada-wide mapping as well as for R&D activities. In January we developed and released the LEAF toolbox, which builds on EE satellite data to create customizable maps of foliage density in near real-time. We do all of the pixel processing in EE and leverage the ability to integrate our own functions using their APIs. We combine EE with Google Cloud to handle and manage output datasets. Fortunately, EE has most of the input data already at hand so we don’t need to deal with that 100x larger volume.

What impact do you expect LEAF will have now–and down the road?

Both the Canadian federal government and provinces already use our data products as inputs to permafrost, crop status, and water resource models. Agriculture Canada had been using a conventional Geographic Information System (GIS) and approached us to ask how we manage our data. They want to use LEAF to assess how crops are progressing, which impacts local economies and the global food supply. We’re in a pilot with them to apply EE to their crop condition assessments.

Also exciting is that the Province of Alberta is integrating the LEAF toolbox within a system for monitoring the reclamation status of oil and gas wells and mines. They want to know how to rehabilitate sites that were developed for mineral and gas deposits. This is another great use case but many others are possible. The technology itself is cool, but that’s not even the point. ML algorithms are constantly evolving and improving. New ML algorithms use active learning that detects mistakes and makes updates on the fly. The technology will be different in five years — or five months! — so our priority is making these tools accessible and useful now.

Why is state-of-the-art technology for climate research so important?

Scientific research must be validated, transparent, and rigorous to drive the best solutions to our complex and changing ecosystem. Climate action needs greater public awareness, which requires more knowledge, which demands the best data. By democratizing information and decision-making, we can create an ecosystem of openness and public-private partnerships. We can lower the barrier to entry for advanced research and help scientists validate and reproduce their results. Also, scientists don’t want to have to become software engineers for these custom highly specific solutions. They want user-friendly tools that let them focus on their analyses and share their insights with the world. That’s one of the major appeals of the Google Research Innovators Program for me: I want to share LEAF with colleagues and collaborate with other researchers who are using new tools in new ways.

Do you have any parting words about your mission?

We’re so fortunate to have Canadian taxpayers and the Government of Canada funding our work. We have worked with many scientists over the past two decades to develop and validate the algorithms we use, especially Fred Baret and Marie Weiss at INRA France, who have championed the idea of free and open access to algorithms and knowledge, and data.

I really believe in democratic access to information. I like the fact that the terms of service of Google products allows us to offer not only maps but the actual processing system in a free and open manner to everyone. I also like that EE provides a simple-to-use user interface that works on mobile devices. We designed LEAF not just for experts, but for individuals. My mom was able to make maps of a nearby park in real time on her tablet. It is my hope that expanding access to critical environmental information will increase our collective awareness of how our actions impact both near and far places — and make us active in the cause of a sustainable future.

That’s an inspiring note to end on. Thank you for your time!

Thanks for having me!

Click here to learn more about Google’s commitment to renewable energy. Or try the LEAF toolbox for yourself!

Do you want to fire up a bunch of techies? Talk about multicloud! There is no shortage of opinions. I figured we should tackle this hot topic head-on, so I recently talked to four smart folks—Corey Quinn of Duckbill Group, Armon Dadgar of Hashicorp, Tammy Bryant Butow of Gremlin, and James Watters of VMware—about what multicloud is all about, key considerations, and why you should (or shouldn’t!) do it.

Five important insights came out of these discussions. If you’re on a multicloud journey or considering one, keep reading.

Do: Choose to do multicloud for the right reasons

Don’t do multicloud because Gartner says so, implores Corey Quinn. Before embarking on a multicloud, define a “why” focused on business value journey, says Armon Dadger. For example, you might want to use services from each public cloud because of their differentiated services, according to Tammy Bryant Butow. Armon also calls out regulatory reasons, existing business relationships, and accommodating mergers and acquisitions. On the topic of M&A, Corey points out that if you acquire a company that uses another cloud, it’s usually expensive and difficult to consolidate. It can be smarter to stay put.

Don’t: Over-engineer for workload or data portability

Thinking that you’ll build a system that moves seamlessly among the various cloud providers? Hold up, says our group of experts. Armon points out that aspects of your toolchain or architecture may be multicloud—think of some of your workflows or global network routing—but that shifting workloads or data is far from simple. Corey says that trying to engineer for “write once, run anywhere” can slow you down, and ignores the inherent uniqueness that’s part of each platform. Specifically, Corey calls out the per-cloud stickiness of identity management, security features, and even network functionality. And data gravity is still a thing, says James, that causes some to dismiss multicloud outright.

If you’re using multiple public clouds, you take advantage of the distinct value each offers, Armon says. Use native cloud services where possible so that you see the benefits from useful innovations, built-in resilience, and baked-in best practices. The value from that cloud-infused workload may outweigh the benefits of seamless portability.

Do: Recognize different stakeholder interests and needs

James smartly points out that many multicloud debates happen because people are arguing from different perspectives. Context matters. If you’re an infrastructure engineer who invests heavily in a given cloud’s identity and access management model, multicloud looks tricky. Or if you’re a data engineer with petabytes of data homed in a particular cloud, multicloud may look unrealistic. James highlights that many developers default to multicloud because their local tools—where all the work happens—are multicloud. A developer’s IDE and preferred code framework(s) aren’t tied to any given cloud. Be aware that groups within your organization will come at multicloud from distinct directions. And this may impact your approach!

Don’t: Go it alone

Corey talks about the importance of asking others what worked, and what didn’t. Tammy offers her best practices around sharing results from experiments. It’s about sharing knowledge and tapping into it for community benefit. Others have probably tried what you’re trying, and can help you avoid common pitfalls. If you’ve just made an architectural choice that didn’t work out, share it, and help others avoid the pain. 

Read research from analysts, go to conferences or watch videos to observe case studies, and join online communities that offer a safe place to share mistakes and learn from others.

Do: Experiment first using techniques like multi-region deployments

If you think you can operate systems across clouds, how about you first try doing it across regions in a specific cloud, suggests Corey. Getting a system to properly work across cloud regions isn’t trivial, he says, and that experience can help you uncover where you have architectural or operational constraints that will be even worse across cloud providers.

This is great guidance if your multicloud aspirations involve using multiple clouds to power one application—versus the more standard definition of multicloud where you use different clouds for different applications—but can also surface issues in your support process or toolchain that fail when faced with distributed systems. Start with muti-region deployments and chaos engineering experiments before aggressively jumping into multicloud architectures.

The Google Cloud take

Do the things above. It’s great advice. I’ll add three more things that we’ve learned from our customers.

1. Don’t fear multicloud. You’re already doing it. You don’t single-source everything. As Corey mentioned, you probably already have one cloud for productivity tools, another for source code, another for cloud infrastructure. You’ll use software and application services from a mix of providers for a single app. You have that experience in your team and have been doing that for decades. What people do rightly worry about is using more than one infrastructure service beneath an application, as that can introduce latency, security, and logistical hurdles. Make sure you know which model your team is considering.
2. Embrace the right foundational components, including Kubernetes. Will everything run on Kubernetes? Of course not. Don’t try to do that. But it also represents the closest thing we have to a multicloud API. Companies are using Kubernetes to stripe a consistent experience across clouds. And this isn’t just to orchestrate containers, but also to manage infrastructure and cloud-native services. Also, consider where you need other fundamental consistency across clouds, including areas like provisioning and identity federation.
3. Use Google Cloud as your anchor. Here’s a fundamental question you have to decide for yourself: Are you going to bring your on-premises technology and practices to the cloud, or bring cloud technology and practices on-prem? We sincerely believe in the latter. Anchor to where you’re trying to get to. We offer Anthos as a way to build and run distributed Kubernetes fleets in Google Cloud and across clouds. By using a cloud-based backplane instead of an on-prem one, you’re offloading toil, leveraging managed services for scale and security, and introducing modern practices to the rest of your team.

We learned a lot about multicloud through these discussions, and it seems like others did too. That’s why we’re going to do a second round of interviews with a new crop of experts so that we can keep digging deeper into this topic. Stay tuned!

2020 saw an unprecedented change in consumer behaviour around the world, with shoppers finding new ways of discovering, evaluating and buying products. This has created fresh expectations for both brands and retailers to drive consumer closeness, embrace the digital moment and transform their operations to be more agile and sustainable. These themes have been top of mind in my conversations with our CPG customers, and set the tone at Google Cloud Summit for Retail and Consumer Goods, which concluded this week. I couldn’t be more proud of our team and thankful for our customers that supported us by participating in our sessions and attending the event.

I joined Google Cloud in January 2021 after a long career in the CPG industry, and as I shared in my CPG keynote at the summit, I am thrilled to be helping bring the power of Google to drive industry innovation in CPG. At Google we call this ‘new normal’ the transformation cloud era, where we’re working with customers who want to not just save money on storage or compute, but to use cloud and digital technologies to drive agility across their business. I also call it the era of ‘consumer switching’, because Covid-19 has accelerated the likelihood of consumers to switch brands or the way they shop. Some of these changes are still happening; over 40% shoppers in a recent Google survey reported that in March 2021 they changed brands or shopped online for something they were previously buying in store.

At Google, we have an amazing team of strategists who have been researching, observing, and analyzing the many facets of consumer behavior over the last few years. In the opening keynote at the Summit Capturing the Hearts and Minds of Today’s Consumers, Google’s Human Truths Team kicked off the Retail & Consumer Goods summit by sharing some of their consumer insights, including what behavior patterns they think will “stick” as we move into a post-pandemic world.  I think these insights are especially relevant for brands, as they speak to some of our latest findings on the CPG shopper’s mindset.

Accenture estimates that there could be a 3 trillion dollar shift in value between companies as a result of consumers shifting brands and behaviours. While it is not known who the winners of the shift will be, one thing is certain – those who will be able to leverage data and analytics fastest will benefit the most from these times of rapid change. I shared some of the implications for the CPG industry in my keynote How to grow brands in times of rapid change along with the three key areas in which Google cloud is helping CPG companies drive brand success: 

1. Unlocking consumer growth with data powered insights 
2. Transforming go-to-market in the omnichannel ecosystem
3. Driving connected, efficient, and sustainable operations

Let’s take a quick look at each of them:

Unlocking consumer growth with data-powered insights

The digital marketing ecosystem is transforming for a privacy centric world, and brands are seeing a direct impact in marketing effectiveness. As the CPG industry becomes more consumer-centric and shifts more toward direct-to-consumer (D2C) business models, acquiring and activating consented first-party consumer data presents a clear opportunity to capitalize on new consumer demands. 

As a result, CPGs are turning to consumer data platforms (CDPs) to help them unify, manage, enrich, and secure all of their disparate data from different marketing tools, website analytics, email campaigns, loyalty programs, and more. Google Cloud and our ecosystem of partners can help CPGs build a privacy-centric CDP which brings together all their customer and marketing data into a modern data warehouse, with built-in predictive data visualization tools and models. With a CDP built on Google Cloud, you can integrate data from Google Marketing Platform to drive predictive marketing and media effectiveness. And with pre-built connectors you can also easily integrate non-Google media and data from other enterprise platforms like SAP and Oracle to leverage consumer data for more integrated decision making. Democratize access to data across the organization with our Business Intelligence tool Looker to enable faster decisions in real-time from marketing to supply chain to product innovation.

At the Retail & CPG Summit we shared how retailers and brands can drive consumer closeness in a privacy-centric world  featuring Procter & Gamble’s experience building and activating consumer data in a privacy-safe way to serve consumers better and maximize marketing effectiveness and drive growth across their business. And in a demo, Constellation Brands shared how they’re leveraging real-time data from several commercial sources using Looker to unpack insights and develop action plans.

Transforming go-to-market in the omnichannel ecosystem

Amidst COVID-19 restrictions and rolling lockdowns, ecommerce activity has surged past a point of no return. Direct to consumer (D2C)  or digitally native brands were able to minimize consumers switching during the pandemic  by offering a great online experience. While in-person shopping remains important, consumers are expecting more digital and omnichannel experiences and many will continue to explore new brands and purchase online. CPGs that want to maintain their market leadership can no longer afford to ignore the key role omnichannel capabilities will play in capturing the attention of both retailers and consumers.  Even if D2C sales are not a big portion of your business, you will benefit from first-party data that can help you drive insights and product innovation. 

CPG brands want to transform their older, clunky ordering processes into modern digital shopping experiences. Re-platforming legacy solutions on Google Cloud not only accelerates application innovation, but also makes it easier to quickly launch new features and products. At Google Cloud we have transformed ecommerce for large D2C brands and traditional retailers, so we know what a best in class D2C experience looks like. And we can bring it to your brands. We already help some of the biggest brands in retail modernize ecommerce and enhance product discovery with solutions like Visual Search and Recommendations AI. All of these can help you build a best in class omni channel presence for your brands.

We had several sessions on improving your omni channel experience, including 

Why search abandonment is the metric that matters featuring Macy’s and Conversational Commerce with Google with Albertsons, where we shared how Google’s conversational experiences can help consumers message businesses from wherever they are, and whenever they need them.

Driving connected, efficient, and sustainable operations

The superpowers of  AI/ML are not just for marketing.  Did you know that research from MIT and Google Cloud has found companies that use AI/ML can drive 2x more data-driven decisions, 5x faster decision making, and 3x faster execution? By connecting your operations in real time with demand signals like search, trends, weather, mobility and supercharging this data with AI and ML, you can make smarter and quicker business decisions. For example, you can use search trends to drive demand forecasting and ramp up manufacturing for popular products.

Google Cloud can help you modernize legacy business applications by migrating them to the cloud and using AI/ML and smart analytics to drive business outcomes. Take SAP for example – a Forrester study found that modernizing SAP with Google not only resulted in 56% more efficient IT teams – it also generated 160% 3-year ROI. 

SAP data on Google Cloud breaks down silos across SAP, marketing, manufacturing systems, and external data sources for next-level intelligent operations. For example, with SAP and Google Cloud, you can combine product, media, CRM, digital commerce and site data from SAP and non-SAP sources to uncover stronger consumer insights and fuel product discovery along the path to purchase. You can merge SAP product and sales data with consumer,  market data and Google geo trends to drive targeted promotion outcomes, maximizing the ROI of promotional dollars across retail channels. You can also integrate supply chain and manufacturing data from SAP systems with consumer, marketing and Google geo market data to improve demand forecasting and optimize supply chain logistics. The possibilities are endless. This is why we describe SAP modernization as The Gift That Keeps On Giving. Check out the session on SAP from the Summit and hear from Rodan + Fields on their experience of modernizing SAP on Google Cloud. 

Another solution that excites me is Vertex AI, which transforms the demand forecasting process. Traditional demand forecasting accuracy is a challenge for most CPGs. Current forecasting methods do not take into account granular factors that impact demand, like local weather, demographics, or unforeseen events. With our recently launched Vertex Forecast, Google is making it much easier to start using cutting-edge machine learning models for demand forecasting. In our session Demand Forecasting: Time for Intelligence, Not Intuition featuring American Eagle Outfitters, we share how you can adopt a data science approach to demand forecasting that’s customized to your unique needs.

CPG organizations come to Google for help solving their toughest problems, whether it be driving new consumer growth, unlocking new routes to market, or building connected, sustainable operations. And we bring the best of Google: innovation, culture, infrastructure, AI/ML, and a deep understanding of consumer behaviour to help them build best-in-class brands. 

I’d like to end with a topic that’s very close to my heart. This is around Solving for Sustainability in Retail and Consumer Goods. Our research shows that 62% of shoppers cared about at least one sustainability aspect when purchasing online in 2020. In addition, the events of the past year have triggered consumers to re-evaluate their relationships with brands and prioritize those that are more sustainable in the context of the pandemic. Watch this session to learn more about how retailers and consumer goods companies can leverage technology, data, and machine learning to help make sustainability a core part of the recovery.

All our session content is available on demand. Ready to learn more about how we’re helping CPG brands and manufacturers drive results? Learn more about Google Cloud’s consumer packaged good solutions and reach out to your Google Cloud sales executive to set up a deeper conversation on how we can help you grow your brands today and in the future.