Like countless other industries, farming is going digital and undergoing big changes—driven by access to more actionable information. The agriculture business can now gather and analyze georeferenced data from satellites, combined with data from IoT sensors in fields, crop rotation and yield histories, weather patterns, seed genotypes and soil composition to help increase the quantity and quality of crops.

This is essential for businesses in the agriculture industry, but it’s also critical to address growing food shortages around the world. 

At S4, we create technology to de-risk crop production. We provide customers seeking agricultural risk management solutions with the tools to make better, data-driven decisions for their crop planning, based on machine learning and proprietary algorithms.

We interpret plant evolution on a global scale with predictive modeling and analytics, and offer super-efficient risk-transferring solutions. Our multi-cloud platform includes a petabyte-scale database, an open source stack, and—after 50 proof-of-concept evaluations—BigQuery for our data warehouse and the Cloud SQL database service to handle OLTP queries to our PostgreSQL database.

These PoCs included, among others, Microsoft Azure Data Lake Analytics, IBM Netezza, Postgres/PostGIS running on IBM bare-metal servers with SATA SSDs and on Google’s Compute Engine with NVMe disks, and on-premises memSQL, CitusData and Yandex ClickHouse. 

Weeding out risk in an uncertain market

According to recent research, climate extreme events like drought, heat waves, and heavy precipitation are responsible for 18-43% of global variation in crop yields for maize, spring wheat, rice, and soybeans. This is a clear trend for other crops as well. Such variation poses risks of food shortages as well as large financial risks to farmers, insurers, and regions dependent on successful crop yields. Also, it creates vast humanitarian difficulties.

Our mission at S4 is to help de-risk crop production by matching the right data with analytics tools so farmers and other participants in the agricultural value chain can plan better, resulting in more reliable food supplies.

In a nutshell, we create indices out of biological assets. These indices measure yield losses on crops that are caused by the effects of weather and other factors, which are then used as underlying assets for products, such as swap/derivative contracts and parametric insurance policies, to transfer risk to the financial markets.

We enable insurers and lenders to buy and sell agricultural risks through the futures market. Also, our other products help farmers and seed and fertilizer companies provide customized genotype recommendations and fertilization requirements. This helps to optimize planting by geography, resources, and crop species, monitor phenological, pests and humidity evolution throughout the crop season, and estimate yields.

Local communities benefit from S4’s technology, as the ability to manage weather risks allows farmers to stabilize their cash flows, invest more to produce more with fewer risks, and develop in a more sustainable manner.

Growing data sources, reducing costs, accelerating performance

With the volume of diverse data sources and analytical complexity both growing at a very fast pace, we decided that using a major cloud services provider with a broad roadmap and global partnerships would be beneficial to S4’s future evolution.

At the same time, we wanted to bring our services to users faster and cut costs by consolidating our on-premises technology stack. When we started evaluating providers, our leading criteria included a powerful geospatial database and data analytics tools along with excellent support, all at a competitive price. GCP prevailed in nearly all criteria categories among the 50 companies we measured. 

Our previous platform architecture included a hybrid relational database that used Compute Engine for virtual machines and Cloud Storage for database backup. The RDBMS was slow. Maintaining our own data warehouse was complex and expensive.

We wanted to use machine learning and neural networks, but couldn’t do so easily and affordably. The complexity of that system meant that products or services requiring small changes or additions to the data model translated to expensive expansions of infrastructure or project time.

Also, agronomical or product teams couldn’t test these changes by themselves, always requiring the intervention on no small part of the IT team, which led to further delays.

We added GCP services like BigQuery as S4’s cloud data warehouse and use BigQuery GIS for geospatial analysis, Cloud Dataflow for simplified stream and batch data processing, and Cloud SQL for queries to the S4 database platform, which have all made a huge impact on our services and bottom line.

Database and analytics costs have decreased by 40% and customers are receiving our analytical results 25% faster. In addition, we’ve eliminated the time-consuming downloading of images, reducing storage and processing costs by 80%, because we no longer need expensive tools licenses, and have greatly reduced classification processing times.

Our customers working in the agriculture industry are also benefiting from this infrastructure change. They are now able to speed up their data analytics using our GCP-based platform.

“S4 products and technologies unlock the full potential of satellite imagery for crop prescriptions, monitoring and yield estimates,” says Nicolás Loria, Manager of Marketing Services, Southern Cone, Corteva Agriscience.

“We’ve worked with S4 for the last three (and starting year number four) crop seasons as its team capabilities, data integration capacities, and analytics insights have allowed Corteva to perform an entire new solution. Thanks to S4’s customized 360° approach, fast response and delivery times, we have safely outsourced our remote crop analytic technical needs.”

Also, this new architecture has allowed us to scale our models and databases with almost no limits, at a fraction of the cost vs. the previous models.

We’ve saved a lot of time on executing processes and reduced work needed by our internal teams to do certain tasks, like preparing images, converting them, validating results, and more. Using Google Earth Engine has decreased the execution time of daily tasks anywhere from 50% to 90% of the previous time, going from an average time of 30 minutes to between four and 15 minutes, depending on the task.

In addition to saving money and time, we are able to focus on innovation with the GCP performance and features we’re using. We’re able to seamlessly add satellite data to analytics using both public datasets and our own private data, and deliver GIS data management, analytics, crop classification and monitoring in real time.

We can do semi-automatic crop classification and classification using spectral signatures with Google Earth Engine. Later this year, we’ll be using neural networks for pattern recognition and machine learning in new applications to improve crop yields and fine-tune risk models. And using GCP and Google Earth Engine infrastructure means we can run models for customers in South America and around the world, since Google Earth Engine has global satellite imagery available. 

We’ve heard from our customer Indigo Argentina that they’re able to bring customers data insights faster.

“We are working with S4 in the development of two different applications for satellite crop monitoring and yield assessment,” says Carlos Becco, CEO, Indigo Argentina. “S4’s technology allowed us to manage and analyze multiple sources and layers of information in real time, letting us uncover valuable insights in Indigo’s own microbiome technologies, and at a very competitive cost.” 

Analytical products and app development thrive with GCP

With GCP, we are updating and improving algorithms that we built manually with machine learning processes to develop drought indices for upcoming crop seasons. Algorithms can recognize specific phases of crop phenology (e.g., bud burst, flowering, fruiting, leaf fall) and correlate them with photosynthetic activity, light, water, temperature, radiation, and plant genetics factors. Other analytical products like crop monitoring, pre-planting recommendations, financial scoring, and yield estimation can now do a lot more for users by offering multiple layers and datasets, faster image processing, and real-time access via APIs.

We also replaced our bare-metal S4 app deployment with the App Engine serverless application platform. It provides tighter integration between the S4 platform and our BigQuery data warehouse for integration with marketplaces and third-party solutions.

We get all of these Google Cloud features with all the benefits of managed cloud services, from multiversioning and security to automatic backups and high availability.

At S4, we trust technology to decode plant growth and help protect farmers and their communities from climate change. With growing food shortages due to increasing populations and intensifying weather, data and analytics can have a huge impact in lowering financial risks and improving agricultural yields. It’s one sector where cloud, database, analytics, and other technologies are combining to improve business outcomes and affect the lives of billions of people. Learn more about S4’s work and learn more about data analytics on Google Cloud.

We’re excited to announce that Google Cloud Next returns on October 11–13, 2022. 

Join us for keynotes from industry luminaries and engage live with Google developers. Explore dynamic content across various learning levels, and dive deep into technologies and solutions spanning the Google Cloud and Google Workspace portfolios. Participate in breakout sessions, demos, and hands-on training. Hear from the world’s leading companies about their digital transformation journeys. You’ll have opportunities to connect with experts, get inspired, and boost your skills. We can’t wait to see you at Next ’22!

It’s too early to determine how the event experience will span the digital and physical worlds, so please stay tuned for updates as we plan with the health and safety of the attendees in mind. In the meantime, mark October 11–13 in your calendar, and visit our event site for updates. For more inspiration, rediscover Next ’21, now available on demand.

Over one third of organizations believe that data analytics and machine learning have the most potential to significantly alter the way they run business over the next 3 to 5 years. However, only 26% of organizations are data driven. One of the biggest reasons for this gap is that a major portion of the data generated today is unstructured, which includes images, documents, and videos. It is estimated to cover roughly up to 80% of all data, which has so far remained untapped by organizations.

One of the goals of Google’s data cloud is to help customers realize value from data of all types and formats. Earlier this year, we announced BigLake, which unifies data lakes and warehouses under a single management framework, enabling you to analyze, search, secure, govern and share unstructured data using BigQuery.

At Next ‘22, we announced the preview of object tables, a new table type in BigQuery that provides a structured record interface for unstructured data stored in Google Cloud Storage. This enables you to directly run analytics and machine learning on images, audio, documents and other file types using existing frameworks like SQL and remote functions natively in BigQuery itself. Object tables also extend our best practices of securing, sharing and governing structured data to unstructured, without needing to learn or deploy new tools.

Directly process unstructured data using BigQuery ML

Object tables contain metadata such as URI (Uniform Resource Identifier), content type, and size that can be queried just like other BigQuery tables. You can then derive inferences using machine learning models on unstructured data with BigQuery ML. As part of preview, you can import open source TensorFlow Hub image models, or your own custom models to annotate the images. Very soon, we plan to enable this for audio, video, text and many other formats, and pre-trained models to enable out-of-the box analysis. Check out this video to learn more and watch a demo.

Create an object table

CREATE EXTERNAL TABLE my_dataset.object_table
WITH CONNECTION us.my_connection
OPTIONS(uris=["gs://mybucket/images/*.jpg"],
object_metadata="SIMPLE", metadata_cache_mode="AUTOMATIC");
​
# Generate inferences with BQML
SELECT * FROM ML.PREDICT(
MODEL my_dataset.vision_model,
(SELECT ML.DECODE_IMAGE(data) AS img FROM my_dataset.object_table)
);

By analyzing unstructured data natively in BigQuery, businesses can

• Eliminate manual effort as pre-processing steps such as tuning image sizes to model requirements are automated
• Leverage the simple and familiar SQL interface to quickly gain insights
• Save costs by utilizing existing BigQuery slots without needing to provision new forms of compute

Adswerve is a leading Google Marketing, Analytics and Cloud partner on a mission to humanize data. Twiddy & Co. is Adswerve’s client – a vacation rental company in North Carolina. By combining structured and unstructured data, Twiddy and Adswerve used BigQuery ML to analyze images of rental listings and predict the click-through rate, enabling data-driven photo editorial decisions.

“Twiddy now has the capability to use advanced image analysis to stay competitive in an ever changing landscape of vacation rental providers – and can do this using their in-house SQL skills.” said Pat Grady, Technology Evangelist, Adswerve

Process unstructured data using remote functions

Customers today use remote functions (UDFs) to process structured data for languages and libraries that are not supported in BigQuery. We are extending this capability to process unstructured data using object tables.

Object tables provide signed URLs to allow remote UDFs running on Cloud Functions or Cloud Run to process the object table content. This is particularly useful for running Google’s pre-trained AI models, including Vision AI, Speech-to-Text, Document AI, open source libraries such as Apache Tika, or deploying your own custom models where performance SLAs are important.

Here’s an example of an object table being created over PDF files that are parsed using an open source library running as a remote UDF.

SELECT uri, extract_title(samples.parse_tika(signed_url)) AS title<br>FROM EXTERNAL_OBJECT_TRANSFORM(TABLE pdf_files_object_table,<br>["SIGNED_URL"]);

Extending more BigQuery capabilities to unstructured data

Business intelligence – The results of analyzing unstructured data either directly in BigQuery ML or via UDFs can be combined with your structured data to build unified reports using Looker Studio (at no charge), Looker or any of your preferred BI solutions. This allows you to gain more comprehensive business insights. For example, online retailers can analyze product return rates by correlating them with the images of defective products. Similarly, digital advertisers can correlate ad performance with various attributes of ad creatives to make more informed decisions.

BigQuery search index – Customers are increasingly using the search functionality of BigQuery to power search use cases. These capabilities now extend to unstructured data analytics as well. Whether you use BigQueryML to produce inference on images or use remote UDFs with Doc AI to produce document extraction, the results can now be search indexed and used to support search access patterns.

Here’s an example of search index on data that is parsed from PDF files:

CREATE SEARCH INDEX my_index ON pdf_text_extract(ALL COLUMNS);
​
SELECT * FROM pdf_text_extract WHERE SEARCH(pdf_text, "Google");

Security and governance – We are extending BigQuery’s row-level security capabilities to help you secure objects in Google Cloud Storage. By securing specific rows in an object table, you can restrict the ability of end users to retrieve the signed URLs of corresponding URIs present in the table. This is a shared responsibility security model, for which administrators need to ensure that end users don’t have direct access to Google Cloud Storage, and use signed URLs from object tables as the only access mechanism.

Here’s an example of a policy for PII images that are secured to be first processed through a blur pipeline:

CREATE ROW ACCESS POLICY pii_data ON object_table_images
GRANT TO ("group:admin@example.com")
FILTER USING (ARRAY_LENGTH(metadata)=1 AND
metadata[OFFSET(0)].name="face_detected")

Soon, Dataplex will support object tables, allowing you to automatically create object tables in BigQuery and manage and govern unstructured data at scale.

Data sharing – You can now use Analytics Hub to share unstructured data with partners, customers and suppliers while not compromising on security and governance. Subscribers can consume the rows of object tables that are shared with them, and use signed URLs for unstructured data objects.

Getting Started

Submit this form to try these new capabilities that unlock the power of your unstructured data in BigQuery. Watch this demo to learn more about these new capabilities.

Special thanks to engineering leaders Amir Hormati, Justin Levandoski and Yuri Volobuev for contributing to this post.

The COVID-19 pandemic continues to push the healthcare and life sciences industry in entirely new ways. In record time, we’ve witnessed public health officials, vaccine developers, equipment manufacturers, and essential workers take life saving actions—regularly putting their own lives at risk—to respond to the exceptional challenges of our time. 

Yet after all the turmoil and uncertainty of the pandemic, record breaking levels of investment continue to come into the market to fuel innovations. 

Vaccine development is now measured in weeks rather than years; providers are leveraging telehealth technologies to improve the physician and patient experience; and individuals have embraced a variety of devices to assume greater ownership and control of their personal health. 

With that in mind, here are five of the many innovations I see driving healthcare and life sciences for at least the next 12 months: 

1. Unleashing the power of healthcare and life sciences data

People have arguably never had as much access and understanding to their personal health data than they can in 2022. They have the ability to understand their genetic makeup, medical history, family history, and activity levels to ensure they are living a healthy lifestyle. Taken together, this longitudinal profile can become the basis for more personalized medicine. Add wearable devices to the mix and patients gain real- or near-real-time updates on their health status. 

Further, global regulatory agencies have continued to mandate the need for providers to maintain a longitudinal health record that provides a holistic view of the patient across all the encounters they have had with a health system. Physician surveys conducted by the The Harris Poll and Google Cloud show that a 360-degree view of the patient, across all provider encounters, leads to faster, more accurate diagnosis, and better outcomes. 

If secure data access and interoperability can begin to include insurers, researchers, public health officials, and others in the field, the powerful network effects benefiting patients will only grow. When combined with those longitudinal phenome profiles, the opportunities for personalized and preventative medicine enter a whole new era.

2. Healthcare and life sciences M&A boom continues

Although the pandemic initially slowed activity on mergers and acquisitions in the early part of 2020, the healthcare and life sciences industry has seen a rapid rebound and acceleration of deals ever since. The success of COVID-19 vaccines, the importance of telehealth, and the focus on molecular modeling and genomic-based drug development have all boosted investment as organizations look to enter new markets, develop new therapies, and leverage low interest rates while they last. 

In 2021, the total funding of US digital health startups surpassed $29 billion across 729 deals, according to advisory Rock Health. That’s almost double the levels of 2020, which itself set records. Analysts at PWC meanwhile estimate M&A investments in biopharmaceutical and life sciences could approach $400 billion this year across all sub-sectors

Clearly, the pandemic has been a primary driver of investment as the focus on healthcare has dramatically increased. Yet the increased activity also reflects changing business models and emerging technologies that are now required to compete in the rapidly evolving space. For organizations to capitalize on these investments, it will take not only great vision and intellectual property but also the right technologies—like cloud—and the right data interoperability models, to make partnerships and acquisitions more scalable, feasible, and seamless.

3. Transforming the patient experience at a new rate

The pandemic has shined a spotlight on the inefficiencies and complexities that exist in healthcare markets across the globe. As wave after wave has surged, global healthcare systems remain overwhelmed on most every aspect of patients’ treatment journeys. Even before COVID-19, healthcare was already one of the largest spend areas for governments around the world. The pandemic has only exacerbated the known issues. 

Clearly, administrators, regulators, physicians, nurses, and patients would agree that the processes and models need to change. There’s a need to maintain this momentum and even increase the tempo to achieve lasting change.

Take telehealth. Within months of the start of the pandemic, providers moved to provide more remote capabilities so physicians could still meet with patients virtually to ensure health and safety on all sides. Payers recognized the importance of telehealth and began to update reimbursement rules. And organizations are now reimagining policies in areas such as prior authorization, submission, and adjudication to reduce complexity and bureaucracy while improving responsiveness.

Looking beyond the system, organizations are also recognizing and deepening their understanding of the structural and social determinants of health that impact patient care and health outcomes, especially for historically underserved communities.  Private and public sectors are learning from, and increasingly partnering with, the social sciences, public health, biomedical informatics, computer science, public policy and community groups around how to build a mI’ore equitable and inclusive consumer products and Health IT strategies. 

The newfound levels of transparency, visibility, and accountability that patients, caregivers, and organizations are achieving will ultimately increase competition and provide a more effective, equitable, efficient and, above all, healthier marketplace for all patients. As we move past the worst of the pandemic, regulators and organizations should keep fighting for progress over business as usual.

4. AI is now a core competency for Drug Development

The ability of organizations like Pfizer, Moderna, Johnson & Johnson, and Astrazeneca to develop COVID-19 vaccines has been a remarkable accomplishment—particularly the historic speed with which they were created and deployed. This innovation acceleration was largely enabled by the use of new drug development platforms that allow researchers to use artificial intelligence and machine learning to model protein and cellular interactions to rapidly advance the science.

No longer must researchers rely on traditional laboratory testing (and retesting). With their improved understanding of the molecular and genetic structure of a patient and, for example, their tumor, researchers can use AI to enable simulations on computers rather than testing in live conditions. This technology can process thousands, even millions of simulations to help  identify high-potential candidates for treatment consideration and subsequent analysis. 

AI-enabled drug discovery models can eliminate months and years from the research process, which can reduce the time to develop a drug and accelerate the time to treatment for an individual patient. As just one example, consider the work on AlphaFold2 by Google’s DeepMind unit who leverages AI to predict effective protein shapes for new drugs. Healthcare and life sciences organizations already recognize the potential of AI. Now comes the investments to leverage this rapidly evolving technology to support their efforts now and in the future.   

5. Ecosystem partnerships tackle complexity and spur innovation

As the importance and growth of the healthcare and life sciences industry continues, we will see even more new players and partnerships emerging to address old problems in new ways. This trend will touch all aspects of the healthcare value chain and will, increasingly, see three- and four-player partnerships emerge to address the complex challenges of today’s healthcare marketplace.

Technology will continue to play a key role as capabilities and platforms will transform all aspects of the marketplace. Cell phones, wearable devices, and other technologies will provide real-time updates and notifications to patients on everything from glucose levels to payments for healthcare services. Voice recognition software will document physician and patient discussions to reduce the burden of record keeping. Real-world data will be used to simplify and confidentially recruit patients for participation in clinical trials. 

New players will continue to enter the market to improve health outcomes and reduce costs. Major retailers are among the companies extending their pharmacies to provide additional diagnostic and concierge services, saving patients from additional appointments while boosting prevention. Community organizations are emerging to help identify and care for underserved communities whose health outcomes are significantly lower than the average patient. 

For all the exhausting and heart-wrenching challenges of the past two years, the opportunities the pandemic has laid bare cannot be overlooked. We owe it to those who have worked and fought so hard for every life to forge even more new partnerships—and make it easier to do so—so that the next crisis, when it does arise, will never be as bad as the one we’re now conquering. Technology can be the enabler in this effort and help bring us together to continue to conquer the challenges that lie ahead.