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Google Cloud’s Autism Career Program to Nurture Neurodiverse Talent

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About 2% of the population has autism and these findings could be confounding as many individuals go undiagnosed. Of the diagnosed individuals, only 29 per cent have stable employment. To close this gap, Google Cloud introduces Autism Career Program.

My passion for neurodiversity began 10 years ago, when I became involved with Els for Autism, an organization that works with children and adults who have autism, as well as their families. At the time, I had a friend who was struggling to find resources for his son with autism. The foundation promotes acceptance and inclusion for people on the spectrum, helping them live independently and find jobs that harness their talents and skills. The organization’s focus on autism in the workplace resonated deeply with me, due to the rich experiences I had working with individuals with autism over the course of my career.

Approximately two percent of the population has autism, but it’s estimated this number is actually quite low as many individuals go undiagnosed. Of those that have been diagnosed, only 29% have had any sort of paid work in their lives. Personally, I find this tragic, because individuals with autism can be highly-functioning and contributing professionals in any organization. Too often, though, the interview process can pose challenges due to unconscious bias from a hiring manager or interviewer, for example, if the candidate doesn’t look an interviewer in the eyes or asks for additional time to complete a test. This bias often unintentionally marginalizes great candidates and means businesses miss out on valuable talent who can contribute and enrich the workplace. 

Introducing Google Cloud’s Autism Career Program 

It is in that spirit that I am excited to announce the launch of Google Cloud’s Autism Career Program, designed to hire and support more talented people with autism in the rapidly growing cloud industry.

We are working with experts from the Stanford Neurodiversity Project (part of the Stanford University School of Medicine), which provides consultation services to employers to advise on opportunities and success metrics for neurodiverse individuals in the workplace.

One key pillar of our program is to train up to 500 Google Cloud managers and others who are involved in hiring processes. Our goal is to empower these Googlers to work effectively and empathetically with autistic candidates and ensure Google’s onboarding processes are accessible and equitable. Stanford will also provide coaching to applicants, as well as ongoing support for them, their teammates and managers once they join the Google Cloud team.

We’re taking this approach to break down the barriers that candidates with autism most often face. In addition to bias, there may be challenges with how interviews are structured or conducted without the right tools. For these reasons, we will offer candidates in this program reasonable accommodations like extended interview time, providing questions in advance, or conducting the interview in writing in a Google Doc rather than verbally on a call. These accommodations don’t give those candidates an unfair advantage. It’s just the opposite: They remove an unfair disadvantage so candidates have a fair and equitable chance to compete for the job.

This program is just one example of Google Cloud’s commitment to inclusion, and it is an important step forward to building a more representative team and creating value for customers and stakeholders.

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How-to

Predict User Churn on Gaming Apps with Google Analytics Data using BigQuery ML

User retention can be a major challenge for mobile game developers. According to the Mobile Gaming Industry Analysis in 2019, most mobile games only see a 25% retention rate for users after the first day. To retain a larger percentage of users after their first use of an app, developers can take steps to motivate and incentivize certain users to return. But to do so, developers need to identify the propensity of any specific user returning after the first 24 hours. 

In this blog post, we will discuss how you can use BigQuery ML to run propensity models on Google Analytics 4 data from your gaming app to determine the likelihood of specific users returning to your app.

You can also use the same end-to-end solution approach in other types of apps using Google Analytics for Firebase as well as apps and websites using Google Analytics 4. To try out the steps in this blogpost or to implement the solution for your own data, you can use this Jupyter Notebook. 

Using this blog post and the accompanying Jupyter Notebook, you’ll learn how to:

  • Explore the BigQuery export dataset for Google Analytics 4
  • Prepare the training data using demographic and behavioural attributes
  • Train propensity models using BigQuery ML
  • Evaluate BigQuery ML models
  • Make predictions using the BigQuery ML models
  • Implement model insights in practical implementations

Google Analytics 4 (GA4) properties unify app and website measurement on a single platform and are now default in Google Analytics. Any business that wants to measure their website, app, or both, can use GA4 for a more complete view of how customers engage with their business. With the launch of Google Analytics 4, BigQuery export of Google Analytics data is now available to all users. If you are already using a Google Analytics 4 property, you can follow this guide to set up exporting your GA data to BigQuery.

Once you have set up the BigQuery export, you can explore the data in BigQuery. Google Analytics 4 uses an event-based measurement model. Each row in the data is an event with additional parameters and properties. The Schema for BigQuery Export can help you to understand the structure of the data.  

In this blogpost, we use the public sample export data from an actual mobile game app called “Flood It!” (Android, iOS) to build a churn prediction model. But you can use data from your own app or website. 

Here’s what the data looks like. Each row in the dataset is a unique event, which can contain nested fields for event parameters.

  SELECT *
FROM `firebase-public-project.analytics_153293282.events_*`
TABLESAMPLE SYSTEM (1 PERCENT)
table

This dataset contains 5.7M events from over 15k users.

  SELECT 
    COUNT(DISTINCT user_pseudo_id) as count_distinct_users,
    COUNT(event_timestamp) as count_events
FROM
  `firebase-public-project.analytics_153293282.events_*
count

Our goal is to use BigQuery ML on the sample app dataset to predict propensity to user churn or not churn based on users’ demographics and activities within the first 24 hours of app installation.

data

In the following sections, we’ll cover how to:

  1. Pre-process the raw event data from GA4
    1. Identify users & the label feature
    2. Process demographic features
    3. Process behavioral features
  2. Train classification model using BigQuery ML
  3. Evaluate the model using BigQueryML
  4. Make predictions using BigQuery ML
  5. Utilize predictions for activation

Pre-process the raw event data

You cannot simply use raw event data to train a machine learning model as it would not be in the right shape and format to use as training data. So in this section, we’ll go through how to pre-process the raw data into an appropriate format to use as training data for classification models.

This is what the training data should look like for our use case at the end of this section:

user id

Notice that in this training data, each row represents a unique user with a distinct user ID (user_pseudo_id). 

Identify users & the label feature

We first filtered the dataset to remove users who were unlikely to return the app anyway. We defined these ‘bounced’ users as ones who spent less than 10 mins with the app. Then we labeled all remaining users:

  • churned: No event data for the user after 24 hours of first engaging with the app.
  • returned: The user has at least one event record after 24 hours of first engaging with the app.

For your use case, you can have a different definition of bounce and churning. Also you can even try to predict something else other than churning, e.g.:

  • whether a user is likely to spend money on in-game currency 
  • likelihood of completing n-number of game levels
  • likelihood of spending n amount of time in-game etc.

In such cases, label each record accordingly so that whatever you are trying to predict can be identified from the label column.

From our dataset, we found that ~41% users (5,557) bounced. However, from the remaining users (8,031),  ~23% (1,883) churned after 24 hours:

  SELECT
    bounced,
    churned, 
    COUNT(churned) as count_users
FROM
    bqmlga4.returningusers
GROUP BY 1,2
ORDER BY bounced
boucned

To create these bounced and churned columns, we used the following snippet of SQL code. 

  ...
#churned = 1 if last_touch within 24 hr of app installation, else 0
IF (user_last_engagement < TIMESTAMP_ADD(user_first_engagement, 
      INTERVAL 24 HOUR),
    1,
    0 ) AS churned,
#bounced = 1 if last_touch within 10 min, else 0
IF (user_last_engagement <= TIMESTAMP_ADD(user_first_engagement, 
      INTERVAL 10 MINUTE),
    1,
    0 ) AS bounced,
...

You can view the Jupyter Notebook for the full query used for materializing the bounced and churned labels. 

Process demographic features

Next, we added features both for demographic data and for behavioral data spanning across multiple columns. Having a combination of both demographic data and behavioral data helps to create a more predictive model. 

We used the following fields for each user as demographic features:

  • geo.country
  • device.operating_system
  • device.language

A user might have multiple unique values in these fields — for example if a user uses the app from two different devices. To simplify, we used the values from the very first user engagement event.

  CREATE OR REPLACE VIEW bqmlga4.user_demographics AS (
  WITH first_values AS (
      SELECT
          user_pseudo_id,
          geo.country as country,
          device.operating_system as operating_system,
          device.language as language,
          ROW_NUMBER() OVER (PARTITION BY user_pseudo_id ORDER BY event_timestamp DESC) AS row_num
      FROM `firebase-public-project.analytics_153293282.events_*`
      WHERE event_name="user_engagement"
      )
  SELECT * EXCEPT (row_num)
  FROM first_values
  WHERE row_num = 1 #first engagement
);

Process behavioral features

There is additional demographic information present in the GA4 export dataset, e.g. app_info, device, event_params, geo etc. You may also send demographic information to Google Analytics through each hit via user_properties. Furthermore, if you have first-party data on your own system, you can join that with the GA4 export data based on user_ids. 

To extract user behavior from the data, we looked into the user’s activities within the first 24 hours of first user engagement. In addition to the events automatically collected by Google Analytics, there are also the recommended events for games that can be explored to analyze user behavior. For our use case, to predict user churn, we counted the number of times the follow events were collected for a user within 24 hours of first user engagement: 

  • user_engagement
  • level_start_quickplay
  • level_end_quickplay
  • level_complete_quickplay
  • level_reset_quickplay
  • post_score
  • spend_virtual_currency
  • ad_reward
  • challenge_a_friend
  • completed_5_levels
  • use_extra_steps

The following query shows how these features were calculated:

  WITH
  events_first24hr AS (
    SELECT
      e.*
    FROM
      `firebase-public-project.analytics_153293282.events_*` e
    JOIN
      bqmlga4.returningusers r
      ON
        e.user_pseudo_id = r.user_pseudo_id
    WHERE
      TIMESTAMP_MICROS(e.event_timestamp) <= r.ts_24hr_after_first_engagement
  )
SELECT
  user_pseudo_id,
  SUM(IF(event_name = 'user_engagement', 1, 0)) AS cnt_user_engagement,
  # ... repeated for all behavior data ... 
  SUM(IF(event_name = 'use_extra_steps', 1, 0)) AS cnt_use_extra_steps,
FROM
  events_first24hr
GROUP BY
  1

View the notebook for the query used to aggregate and extract the behavioral data. You can use different sets of events for your use case. To view the complete list of events, use the following query:

  SELECT
    event_name,
    COUNT(event_name) as event_count
FROM
    `firebase-public-project.analytics_153293282.events_*`
GROUP BY 1
ORDER BY
   event_count DESC

After this we combined the features to ensure our training dataset reflects the intended structure. We had the following columns in our table:

  • User ID:
    • user_pseudo_id
  • Label:
    • churned
  • Demographic features
    • country
    • device_os
    • device_language
  • Behavioral features
    • cnt_user_engagement
    • cnt_level_start_quickplay
    • cnt_level_end_quickplay
    • cnt_level_complete_quickplay
    • cnt_level_reset_quickplay
    • cnt_post_score
    • cnt_spend_virtual_currency
    • cnt_ad_reward
    • cnt_challenge_a_friend
    • cnt_completed_5_levels
    • cnt_use_extra_steps
    • user_first_engagement

At this point, the dataset was ready to train the classification machine learning model in BigQuery ML. Once trained, the model will output a propensity score between churn (churned=1) or return (churned=0) indicating the probability of a user churning based on the training data.

Train classification model 

When using the CREATE MODEL statement, BigQuery ML automatically splits the data between training and test. Thus the model can be evaluated immediately after training (see the documentation for more information).

For the ML model, we can choose among the following classification algorithms where each type has its own pros and cons:

model

Often logistic regression is used as a starting point because it is the fastest to train. The query below shows how we trained the logistic regression classification models in BigQuery ML.

  CREATE OR REPLACE MODEL bqmlga4.churn_logreg
TRANSFORM(
  EXTRACT(MONTH from user_first_engagement) as month,
  EXTRACT(DAYOFYEAR from user_first_engagement) as julianday,
  EXTRACT(DAYOFWEEK from user_first_engagement) as dayofweek,
  EXTRACT(HOUR from user_first_engagement) as hour,
  * EXCEPT(user_first_engagement, user_pseudo_id)
)
OPTIONS(
  MODEL_TYPE="LOGISTIC_REG",
  INPUT_LABEL_COLS=["churned"]
) AS
SELECT
  *
FROM
  bqmlga4.train

We extracted month, julianday, and dayofweek  from datetimes/timestamps as one simple example of additional feature preprocessing before training. Using TRANSFORM() in your CREATE MODEL query allows the model to remember the extracted values. Thus, when making predictions using the model later on, these values won’t have to be extracted again. View the notebook for the example queries to train other types of models (XGBoost, deep neural network, AutoML Tables).

Evaluate model

Once the model finished training, we ran ML.EVALUATE to generate precision, recall, accuracy and f1_score for the model:

  SELECT
  *
FROM
  ML.EVALUATE(MODEL bqmlga4.churn_logreg)
row

The optional THRESHOLD parameter can be used to modify the default classification threshold of 0.5. For more information on these metrics, you can read through the definitions on precision and recall, accuracy, f1-score, log_loss and roc_auc. Comparing the resulting evaluation metrics can help to decide among multiple models.Furthermore, we used a confusion matrix to inspect how well the model predicted the labels, compared to the actual labels. The confusion matrix is created using the default threshold of 0.5, which you may want to adjust to optimize for recall, precision, or a balance (more information here).

  SELECT
  expected_label,
  _0 AS predicted_0,
  _1 AS predicted_1
FROM
  ML.CONFUSION_MATRIX(MODEL bqmlga4.churn_logreg)
expected

This table can be interpreted in the following way:

actual

Make predictions using BigQuery ML

Once the ideal model was available, we ran ML.PREDICT to make predictions. For propensity modeling, the most important output is the probability of a behavior occurring. The following query returns the probability that the user will return after 24 hrs. The higher the probability and closer it is to 1, the more likely the user is predicted to return, and the closer it is to 0, the more likely the user is predicted to churn.

  SELECT
  user_pseudo_id,
  returned,
  predicted_returned,
  predicted_returned_probs[OFFSET(0)].prob as probability_returned
FROM
  ML.PREDICT(MODEL bqmlga4.churn_logreg,
  (SELECT * FROM bqmlga4.train)) #can be replaced with a proper test dataset

Utilize predictions for activation

Once the model predictions are available for your users, you can activate this insight in different ways. In our analysis, we used user_pseudo_id as the user identifier. However, ideally, your app should send back the user_id from your app to Google Analytics. In addition to using first-party data for model predictions, this will also let you join back the predictions from the model into your own data.

  • You can import the model predictions back into Google Analytics as a user attribute. This can be done using the Data Import feature for Google Analytics 4. Based on the prediction values you can Create and edit audiences and also do Audience targeting. For example, an audience can be users with prediction probability between 0.4 and 0.7, to represent users who are predicted to be “on the fence” between churning and returning.
  • For Firebase Apps, you can use the Import segments feature. You can tailor user experience by targeting your identified users through Firebase services such as Remote Config, Cloud Messaging, and In-App Messaging. This will involve importing the segment data from BigQuery into Firebase. After that you can send notifications to the users, configure the app for them, or follow the user journeys across devices.
  • Run targeted marketing campaigns via CRMs like Salesforce, e.g. send out reminder emails.

You can find all of the code used in this blogpost in the Github repository:

https://github.com/GoogleCloudPlatform/analytics-componentized-patterns/tree/master/gaming/propensity-model/bqml

What’s next? 

Continuous model evaluation and re-training

As you collect more data from your users, you may want to regularly evaluate your model on fresh data and re-train the model if you notice that the model quality is decaying.

Continuous evaluation—the process of ensuring a production machine learning model is still performing well on new data—is an essential part in any ML workflow. Performing continuous evaluation can help you catch model drift, a phenomenon that occurs when the data used to train your model no longer reflects the current environment. 

To learn more about how to do continuous model evaluation and re-train models, you can read the blogpost: Continuous model evaluation with BigQuery ML, Stored Procedures, and Cloud Scheduler

More resources

If you’d like to learn more about any of the topics covered in this post, check out these resources:

Or learn more about how you can use BigQuery ML to easily build other machine learning solutions:

Let us know what you thought of this post, and if you have topics you’d like to see covered in the future! You can find us on Twitter at @polonglin and @_mkazi_.Thanks to reviewers: Abhishek Kashyap, Breen Baker, David Sabater Dinter.

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Google Announced Leader in 2021 Gartner Magic Quadrant for Cloud Infrastructure and Platform Services

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For the fourth time in a row, Google Cloud's customer centric innovations, performance, sustainability, cost and support, wins hands down at the 2021 Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services!

For the fourth consecutive year, Gartner has positioned Google as a Leader in the 2021 Gartner Magic Quadrant for Cloud Infrastructure and Platform Services (formerly titled as Magic Quadrant for Cloud Infrastructure as a Service upto 2014 Infrastructure as a Service, or (IaaS).

With our customers and communities adjusting to new ways of working and doing business, Google Cloud has remained focused on building services and platforms that help you be more resilient and derive even more value from your cloud infrastructure. We believe Gartner’s analysis and recognition gives our customers the confidence needed to choose Google as the platform for customer-centric innovation. Here are just a few recent examples.

Ready for the most demanding, mission-critical workloads

Our enterprise-ready cloud provides you the uptime, performance, and scale to run even your most demanding workloads. Examples of recent launches: 

Saves you money

Save money with a transparent and innovative approach to pricing and intelligent recommendations. In the past year, we’ve launched several innovations to help you save costs:

  • Tau VMs, which offer the best price-performance among leading clouds for scale-out workloads 
  • Machine-learning-driven predictive auto-scaling for VMs and GKE Autopilot, enabling infrastructure to scale up and down as needed with minimal waste 
  • Standard network tier which routes traffic over the internet for cost optimization 

Open

We have a long history of leadership in open technologies—from projects like Kubernetes, the industry standard in container orchestration and interoperability, to TensorFlow, a platform to help anyone develop and train machine learning models. Here are a few recent improvements we’ve made to ensure your cloud is an open cloud: 

Secure

Google Cloud’s trusted infrastructure uses layers of security to protect your data with advanced technologies and operations, keeping your organization secure and compliant. For example, we offer:

Sustainable

Google Cloud helps customers transform their business sustainably. We operate the cleanest cloud in the industry to make sure your digital footprint doesn’t leave a carbon one. Here are a few proof points:

Supporting our customers

Most importantly, our field organizations and partner organizations work with a singular focus to ensure customer success. This has made Google Cloud the fastest growing hyperscaler, with a rapidly expanding customer base across all geos and industries.

Since launching Customer Care last year, we consolidated and simplified the post-sales engagement with customers, increased the support channels, created an API to allow programmatic case creation, and combined product specific support into a single package for all of Google Cloud. Enterprises with Customer Care continue to report high levels of satisfaction with their focused technical account managers (TAMs), helping them get the most business value out of Google Cloud.

We are committed to sustaining and accelerating the pace of customer-centric innovation. You can download a complimentary copy of the 2021 Magic Quadrant for Cloud Infrastructure and Platform Services on our website. 

Join us to learn much more about Google Cloud at the upcoming Google Cloud Next ‘21 digital conference.  

Gartner, Magic Quadrant for Cloud Infrastructure and Platform Services,  Raj Bala | Bob Gill | Dennis Smith | Kevin Ji | David Wright, 27 July 2021
Gartner, Solution Scorecard for Google Kubernetes Engine,  Tony Iams | Traverse Clayton | Megan Bain, 12 April 2021
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Case Study

What Are India’s Biggest Companies Doing on Google Cloud?

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Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization. Here's what Tata Steel and L&T Financial Services are doing among others.

In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.

At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.

Here are some companies and their stories.

Tata Steel: Mining data and maximizing its power

Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage. 

To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.

They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.

“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.

Helping L&T Financial Services reach customers in rural communities

In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.

L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.

“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.

Creating conversational connections for Digitate’s customers

Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.

“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.

Indian enterprises going G Suite

The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.

A hybrid and multi-cloud future in India

IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions  — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.

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Earth Week: Google Cloud at the Heart of Sustainability

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Google Cloud ensures every business' digital transformation is sustainable through strategic partnerships, product releases, upgrades and more. To celebrate the Earth Week, read the blog for a summary of sustainability initiatives and its impact!

Today’s Google Doodle reminds us of the enormous changes our planet is experiencing due to climate change. Everyone, from businesses to governments to technologists, has the opportunity to meet this challenge — transforming themselves and their organizations to be more sustainable. For this Earth Day 2022, and indeed Earth Week, Google Cloud celebrates the organizations and individuals who are fighting climate change with innovative technology. We don’t want you to miss a thing, so here’s a recap of all our news in one handy location.

We asked global CEOs: what is it going to take to make progress on sustainability in your org?
In a survey of 1,500 CXOs across 16 countries, many executives say they are willing to do what it takes to have more sustainable practices. But despite their ambition, real measures of impact are lacking. To see what will change that, check out the blog.

We announced a new innovation challenge supporting climate science and research…
Our blog on Monday announced the Climate Innovation Challenge Research Credits program, to support researchers as they work to better understand climate change, increase climate resilience and develop new, promising solutions to urgent climate challenges. You can apply for research credits here.

…and shared stories of researchers making a difference
We interviewed Dr. Richard Fernandes from Natural Resources Canada, who built the LEAF toolbox that maps and assesses vegetation with satellite data from Google Earth Engine. You can read our Q&A here.

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. Cloud computing allows us to manage all this data in a useful and accessible way.

Dr. Fernandes, Research Scientist

We also published a story about the U.S. Department of Agriculture’s Forest Service, and how they use Google Cloud processing and analysis tools to help sustainably manage 193 million acres of land.

We turned the lights on at new clean energy projects in four countries…
We shared details of our battery project in Belgium, solar projects in Denmark, and wind projects in Chile and Finland. Our battery project in Belgium is the first of its kind, enabling us to switch from diesel generators to a cleaner backup solution that will keep the internet up and running in the event of a power disruption. These will all help us continue to operate the cleanest cloud in the industry.

The Rødby Fjord solar project under construction.

…and made it easier to learn how to build applications more sustainably
We launched a new lab that walks users through our Carbon Sense suite of products. From using our region picker app to make low-carbon architecture decisions, to analyzing the carbon footprint of your Google Cloud app with Carbon Footprint, we’re building sustainability into the tools you use every day. You can also find Carbon Footprint training in the new Data Warehouse Cloud On-Board.

We formed an ecosystem of partners to help accelerate sustainability projects…
The Google Cloud partner ecosystem is critical to helping our customers act sustainably today. A new whitepaper produced in partnership with Enterprise Strategy Group shares real-world solutions that could make an immediate impact — not in the next decade, but right now.

…and shared stories of innovative startups changing the game with Google Cloud.
Take Enexor and its partners, who are producing clean and sustainable energy from discarded plastics and agro-waste. The blog from Lee Jestings, Enexor Founder & CEO, shares how Google for Startups got them started, and which Google Cloud tools help them build predictive models. Check out their story.

Or Nuuly, the rental and resale business created by the URBN portfolio, which also includes Urban Outfitters, Anthropologie, and Free People. In the blog you can read how Nuuly is using technology to provide a sustainable experience to employees and customers — from upcycling clothing, to recyclable and reusable packaging.

Whether you’re a startup, scientist, executive or developer, at Google Cloud we’ll continue to work hard to help make your digital transformation a sustainable one.

Learn more about our sustainability work here, and don’t miss the inaugural Cloud Sustainability Summit this June. Register now.

Case Study

Lucent Bio: Boosting collaboration and sustainability with Google Workspace

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Lucent Bio, an ag-tech company, collaborated with Google Workspace to streamline operations and meet its sustainability goals. Read to know how Team Google has been an essential part of their innovation and growth strategy.

From electrifying transportation to shifting the grid to renewable energy, environmental sustainability is one of the greatest challenges of our generation. A critical but often forgotten goal is the development of sustainable agricultural practices, especially given increasing water shortages and soil degradation around the world.

Lucent Bio was born to solve some of these threats to humanity’s ability to feed itself. It delivers crop nutrition solutions that accelerate the transition to sustainable agriculture. Lucent Bio developed novel technology for bioactive crop nutrition products, including its flagship product—Soileos®—which boosts nutrient density in crops, regenerates soil, avoids polluting agro-ecosystems, and enables the circular economy. Soileos is a plant-based product that is made by upcycling food processing co-products such as lentil and pea hulls into bioactive nutrients that then create the next harvest.

As a start-up, Lucent Bio has used Google Workspace since day one to drive collaboration, streamline operations, and scale. As an organization, it is also closely aligned with Google’s sustainability goals. Google is carbon-neutral for its operations and has made a public commitment with detailed plans to run on carbon-free energy by 2030, meaning clean power every hour of every day.

Google Workspace has been an invaluable resource to fuel collaboration between the engineers at the Lucent Bio pilot plant, the scientists at the research lab and greenhouse, and on-field agronomists as they conduct trials on Soileos across North America. Without the use of Google Workspace during the COVID 19 pandemic, Lucent Bio would not have been able to achieve their current scale.

“In just 18 months, Lucent Bio has been able to scale manufacturing from 1 kg to 1,000 kg of product per day. This year, we’re poised to take the next big step to 20,000 kg per day. During this scale-up process, we went through several iterations of improvements, resulting in a completely zero-waste manufacturing process—a hugely difficult but impressive achievement in line with our mission to accelerate the transformation of agriculture to sustainability. Every step of the way, Google Workspace has been an essential part of our innovation and growth strategy.”
Michael Riedijk, CEO Lucent Bio

Google Workspace runs on the cleanest cloud in the industry. It helps teams of all sizes, and across all industries, connect, create, and collaborate from anywhere. Workspace recently launched AI-generated summaries in Spaces, which help distributed teams stay focused while quickly catching up on chat messages they might have missed. Innovations like these keep the scientists at Lucent Bio collaborating as they build a more sustainable future for agriculture.

You can find more about Google’s commitment to sustainability here, including our goal to become not just carbon-neutral, but carbon-free by 2030, efforts to combat deforestation, and how we’re supporting clean energy.

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