Scope for Tech Adoption and Advancements in Healthcare are Still High: Google Cloud Research
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Since the start of the COVID-19 pandemic, there’s been a rapid acceleration of digital transformation across the entire healthcare industry. Telehealth has become a more mainstream and safe way for patients and caregivers to connect. Machine learning modeling has helped speed up innovation and drug discovery. And new levels of integration and data portability have helped enable greater vaccine availability and equitable access to those who need it.
Data has been at the crux of this digital transformation — helping people stay healthy, accelerating life sciences research and delivering more personalized and equitable care. We recently unveiled partial results from our research with The Harris Poll, which revealed that nearly all physicians (95%) believe increased data interoperability will ultimately help improve patient outcomes. Today, we’re unveiling the second part of that research.
In February 2020, we commissioned The Harris Poll to survey 300 physicians in the U.S. about their biggest pain points — this was just before the COVID-19 pandemic strained the entire healthcare system and made us all hyper-aware of the risks we take in going to the hospital. In June 2021, we followed-up with those same questions and more. What it unveiled was just how much COVID-19 reshaped technology’s role in the healthcare field and how it’s changing day-to-day operations for physicians.
Here are some of the highlights:
Healthcare organizations accelerated technological upgrades over the course of the pandemic. After a year shaped primarily by the COVID-19 pandemic, use of telehealth saw substantial YOY growth, jumping nearly threefold from 32% in February 2020 to 90% this year. Forty-five percent of physicians say the COVID-19 pandemic accelerated the pace of their organization’s adoption of technology. In fact, more than 3 in 5 physicians (62%) say the pandemic has forced their healthcare organization to make technology upgrades that normally would have taken years. For example, 48% of physicians would like to have access to telehealth capabilities in the next five years. Before the COVID-19 pandemic, about half of physicians (53%) say their healthcare organization’s approach to the adoption of technology would best be described as “neutral” (i.e., willing to try new technologies only if they have been in the market for awhile or others have tried and recommended them).
Despite the technological leaps this year, most physicians still believe the industry lags behind in technology adoption but recognize the opportunity for technological support and advancement. The majority of physicians don’t view the healthcare industry as a leader when it comes to digital adoption. More than half of physicians describe the healthcare industry as lagging behind the gaming (64%), telecommunications (56%), and financial services industries (53%). However, the healthcare industry is not seen to be trailing as much as it was last year behind retail (54% in 2020; 44% in 2021); hospitality and travel (53% in 2020; 43% in 2021); and the public sector (39% in 2020; 26% in 2021).
Better interoperability alleviates physician burnout, improves health outcomes and speeds up diagnoses. The majority of physicians say increased data interoperability will cut the time to diagnosis for patients significantly (86%) and will ultimately help improve patient outcomes (95%.) In addition to better patient experiences and outcomes, more than half of physicians (54%) believe increased access to data via technology has had a positive impact on their healthcare organization overall. A majority believe that technology can alleviate the likelihood of physician “burn-out” (57%) and that efficient tools help decrease friction and stress (84%). And, as a result, 6 in 10 physicians say access to better technology and clinical data systems would allow them to have better work/life balance (60%) and that better access to/more complete patient data would reduce administrative burdens (61%). It is therefore not surprising that nearly 9 in 10 physicians (89%) say they are increasingly looking for ways to bring together all patient data into a single place for a more complete view of health.
Familiarity with new Department of Health and Human Services (DHHS) interoperability rules grows, and many physicians are in favor. Most physicians (74%) say they have at least heard of the new DHHS rules (launched in 2019) to improve the interoperability of electronic health information. This is a clear rise from 2020 (64%), but deeper knowledge is fairly low. Only 30% of physicians say they are somewhat or very familiar with the new rules (though, again, this is a rise from 2020, when only 18% said they were very/somewhat familiar). Similar to in 2020, among those who have heard of the new rules, nearly half are in favor (48% in 2021; 45% in 2020) but a similar proportion remain unsure (46% in 2021; 50% in 2020). And like in 2020, by far the top potential benefit of the rules is thought to be forcing EHRs to be more interoperable with other systems (70%).
Google was founded on the idea that bringing more information to more people improves lives on a vast scale. In healthcare, that means creating tools and solutions that make data available in real time to help streamline operations and improve quality of care and patient outcomes. For example, our recently announced Healthcare Data Engine makes it easier for healthcare and life sciences leaders to make smart real-time decisions through clinical, operational, & groundbreaking scientific insights. To find out more about the Healthcare Data Engine, click here.
Survey methodology: The 2021 survey was conducted online within the United States by The Harris Poll on behalf of Google Cloud from June 9 – 29, 2021 among 303 physicians who specialize in Family Practice, General Practice, or Internal Medicine, who treat patients, and are duly licensed in the state they practice. The 2020 survey was conducted from February 18 – 25, 2020 among 300 physicians who specialize in Family Practice, General Practice, or Internal Medicine, who treat patients, and are duly licensed in the state they practice. Physicians practicing in Vermont were excluded from the research. This online survey is not based on a probability sample and therefore no estimate of theoretical sampling error can be calculated. For complete survey methodology, including weighting variables and subgroup sample sizes, please contact press@google.com.
Rethinking retail with Google Cloud Retail Search
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Cloud Retail Search, part of Discovery Solutions For Retail portfolio, helps retailers significantly improve the shopping experience on their digital platform with ‘Google-quality’ search. Cloud Retail Search offers advanced search capabilities such as better understanding user intent and self-learning ranking models that help retailers unlock the full potential of their online experience.
Google Cloud’s Discovery Solutions For Retail are a set of services that can help retailers improve their digital engagement and are offered as part of our industry solutions.
Executive Summary
Retailers are always working on trying to keep up with the ever changing consumer expectations and trying to forecast the next trend that can impact sales and revenue.
The pandemic brought its own (and largely new) set of challenges which further complicated the issue over the last two years. The retailers were forced to adapt to the new consumer (low physical touch) behavior in which the browsing and product research was largely digital (endless aisle) and accelerated other trends such as buy online and pick up in stores (BOPIS), curbside pick up and pick up lockers. According to a McKinsey Global Survey from early last year, the pandemic has accelerated the pace of digital transformation by several years.
The National Retail Federation (NRF) estimates that retail sales are expected to grow between 6% and 8% in 2022 (slower growth rate than in 2021), as consumers spend more on services instead of goods, deal with inflation and higher food & gas prices due to geopolitical disruptions in the world.
And the competition continues to be fierce as ever. Amazon continues its dominance in the U.S. retail world and new PYMNTS data shows that Amazon’s share of US Ecommerce sales hit an all-time high of 56.7% in 2021.
Customers now have more choices than ever on how they want to engage with the retailers, where they want to spend the money and make their purchase. They also have increased expectations from the retailers around providing a high quality product discovery experience, which is forcing the retailers to invest heavily on improving customer engagement on their digital platforms to boost conversion rate and overall customer loyalty.
This is where Retail Search can help by providing an enhanced search experience that uses Google-quality search models to understand the customer intent and takes into account the retailer’s first party data (such as promotions, available inventory and price) for ranking results.
How is Google Cloud Retail Search Different
The Ecommerce platform on-site search use case is not new and retailers have been trying to solve it effectively for the last two decades. Most retailers recognize that search is a critical service on the platform and have spent countless resources to improve and fine tune it over the years. Yet the challenge remains. According to a Baymard Institute study in late as 2019, 61% of sites still required their users to search by the exact product type jargon the site uses.
However, users now expect the same robust and intuitive search features as is offered by Google.com and other popular web platforms, who seem to have the uncanny ability to intelligently interpret and yield relevant results to complex search queries.
Google’s decades of experience and research in search technology benefits Cloud Retail Search solution and that is what differentiates it from the competition.
- Advanced Query Understanding: Retail Search can provide more relevant results for the same query due to better query understanding features and knowing when to broaden or narrow the query results. While most search engines still rely largely on keyword based or matching tokens results, Retail Search has the advantage of being able to leverage Google search algorithms to return highly relevant results for product listings and category pages.
- Semantic Search: Intent recognition is a key requirement for semantic search and identifying what the customers mean when they enter the query is a key strength of Retail Search. This is critical for retailers since this has a direct impact on Clickthrough rate, Conversion rate and the Bounce Rate.
- Personalized Search Results: Another key differentiator for Retail Search is its ability to leverage user interaction data and ranking models to provide hyper personalized search results. Retailers are able to optimize search performance to deliver desired outcomes: better engagement, revenue, or conversions.
- Self-Learning and Self-Managed Solution: Retail Search models get better over time because of the self-learning capabilities built into the solution. In addition, the service is fully managed, which saves precious resources needed to keep it running and managing its set up.
- Strong Security Controls: The service runs on Google Cloud and follows security best practices to keep our customers’ data secure. Google never shares model weights or customer data across customers using the Retail API or other Discovery Solution products. For more details about this data use, see a description of Retail API data use.
High Level Conceptual View
Here is a simplified high-level view of Retail Search API. Retailers can call the API for the given search query and get back the results which can then be displayed on their digital properties.
The returned results contains two types of information:
- Search results: Query search results including product listings and category pages based on advanced query understanding and semantic search.
- Dynamic faceted search attributes: Faceted Search is a feature that allows further refinement of the search by providing ways to apply additional filters while returning results.
Retail Search needs the following datasets as input to train its machine learning models for search:
- Product Catalog: Information about the available products including product categories, product description, in-stock availability, and pricing.
- User Events: This is the clickstream data that contains user interaction information such as clicks and purchases.
- Inventory / Pricing Updates: Incremental updates to in-stock availability and pricing as that information is updated.
(Keeping the product catalog up to date and recording user events successfully is crucial for getting high-quality results. Set up Cloud Monitoring alerts to take prompt action in case any issues arise).
Retailers also have the ability to set up business/config rules to customize their search results and optimize for business revenue goals such as Clickthrough rate, Conversion rate, Average size order etc.
How to get started
Retail Search is generally available now and anyone with a Google cloud account can access it. If you don’t already have an account, you can start with a trial account for free here.
Establish a Success Criteria: It’s important to establish a success criteria for measuring the effectiveness of Retail search. Get a consensus on which factor(s) you want to include in scope for measuring the effectiveness of Retail Search. This could include one or two from the following: Search Conversion Rate, Search Average Order Value, Search Revenue Per Visit and Null Search Rate (No Results Found).
- Initial Set Up: Create a Google Cloud Project and set up the Retail API. When you set up the Retail API for a new project, the Google Cloud Console displays the following three panels to help you configure your Retail API project:
- Catalog: displays product catalog and a link to import catalog.
- Event: displays user events and a link to import historical user events.
- Serving configs: contains details on serving configuration and a link to create a new serving configuration.
- Measuring Performance: Retail dashboards provide metrics to help you determine how incorporating the Retail API is affecting the results. You can view summary metrics for your project on the Analytics tab of the Monitoring & Analytics page in Cloud Console.
- Set up A/B Experiments: To measure the performance of Retail Search with another search solution, you can set up A/B tests using a third-party experiment platform such as Google Optimize.
Summary:
As retailers try to navigate through the post-pandemic world where supply chain failures and digital transformation acceleration are major focus areas, they now also have to keep a close eye on the recent geopolitical challenges resulting in rising inflation and costs.
While we can all agree that in-store shopping will continue to be a major source of revenue, it is also important for retailers to tweak the in-store experience for the digital world. Trends such as buy online and pick up in stores (BOPIS), curbside pick up and pick up lockers are here to stay.
Given all the above, consumer engagement and digital experience is more important now than ever before. The cost of search abandonment is way too high and has both short and longer term impact. Retail Search is a great solution to help reduce churn, improve conversion and retention. It provides Google-quality search models to help understand customer intent and the retailers have the ability to set up business/config rules to optimize search results for business revenue goals such as Clickthrough rate, Conversion rate and Average size order.
Google Cloud’s 101 Illustrated References for Cloud Engineers and Architects
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Many people are visual learners; I am definitely one of them, and judging by the tremendous response you all showed to Google Cloud Sketchnotes on LinkedIn and Twitter, you are too.
-Priyanka Vergadia, Lead Developer Advocate, Google
“A picture is worth a thousand words”
Shortly after I started creating and sharing visual explanations of Google Cloud concepts in late 2020, I began receiving overwhelmingly positive feedback from fellow cloud architects and enthusiasts. That feedback led me to think about pulling these sketches together into a reference guide — a one-stop shop for cloud learning fun. So here it is!
🥁 Introducing “Visualizing Google Cloud: 101 Illustrated References for Cloud Engineers and Architects”
🤔 What is this book about?
This book is my way of making Google Cloud technical concepts fun and interesting via visual storytelling. It covers Google Cloud essentials from end to end, with a visual explanation of each concept, how it works, and how you can apply it with other concepts to meet the needs of your real-world use case. After reading thousands of pages of Google Cloud documentation and experimenting with virtually every Google Cloud product, I distilled that experience down to this book of accessible, bite-sized visuals.
The sketches are spread across five broad categories:
- Infrastructure, storage, and databases: Run and scale applications seamlessly
- Data analytics and machine learning: Derive actionable insights from data
- Networking: Connect, scale, secure, modernize, and optimize your infrastructure
- Application development and modernization: Build apps using containers and microservices
- Security: Protect data, users, and applications in cloud
🙋♀️ Who is this book for?
It is for all cloud enthusiasts. It is for anyone who is planning a cloud migration or new cloud deployment, preparing for cloud certification, or looking to make the most of Google Cloud. If you are a cloud solutions architect, an IT decision-maker, or a data and machine learning engineer, you will find this book a good starting point. In short, this book is for YOU!
When you get the book you’ll not only help yourself, you’ll also help provide meals for schoolchildren who need them. All the books’ proceeds go directly to the awesome folks at Wiley and a charity that fights malnutrition and supports the right to education.
🪜 Next Steps
I hope this book helps you on your Google Cloud journey by making it both easier and more fun. Are you ready to dive in? Order here, learn more about how the idea of the book came about here and please share your thoughts with me on Twitter or LinkedIn.
Google Extends Support for Windows Server Containers on Anthos for Faster App Modernization and Consistent Dev Experience
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Today, many applications in organizations’ data centers run on Windows Server. Modernizing these traditional Windows apps onto Kubernetes promises a host of benefits: a consistent platform across environments, better portability, scalability, availability, simplified management and speed of deployment, just to name a few. But how? Rewriting traditional .NET applications to run on Linux with .NET Core can be challenging and time-consuming. There is, however, a lower-toil, more developer friendly option.
Last year, we announced support for Windows Server containers running on Google Kubernetes Engine (GKE), our cloud-based managed Kubernetes service, which lets you take the advantage of containers without porting your apps to .NET core or rewriting them for Linux. Today, we’re going a step further with support for Windows Server containers on Anthos clusters on VMware in your on-premises environment. Now available in preview, you can consolidate all your Windows operations across on-prem and Google Cloud.
Bringing Windows Server support to our family of Kubernetes-based services—GKE running on Google Cloud, and Anthos everywhere—with the same experience, lets you modernize apps faster and achieve a consistent development and deployment experience across hybrid and cloud environments. Further, by running Windows and Linux workloads side by side, you get operational consistency and efficiency—no need to have multiple teams specializing in different tooling or platforms to manage different workloads. The single-pane-of-glass view and the ability to manage policies from a central control plane simplifies the management experience, while bin packing multiple Windows applications drives better resource utilization, leading to infrastructure and license savings.
With all these benefits, it’s no surprise that customers such as Thales, a French multinational firm specializing in aerospace and security services, have been able to reap significant benefits by moving Windows applications to GKE.
“We moved our Windows applications from VMs to Windows containers on GKE and now have a unified mechanism for Linux and Windows-based application management, scaling, logging, and monitoring. Earlier, setting up these applications in VMs and configuring them for high availability used to take up to a week, and the applications were not easily scalable,” said Najam Siddiqui, Solutions Architect at Thales. “Now with GKE, the setup takes only a few minutes. GKE’s automatic scaling and built-in resiliency features make scaling and high-availability setup seamless. Also, manually maintaining the VMs and applying security patches used to be tedious, which is now handled by GKE.”
Let’s take a deeper look at the architecture that lets you run your Windows container-based workloads on-prem.
Windows Server running on-prem with Anthos
The diagram below illustrates the high-level architecture of running Windows container-based workloads in an on-prem GKE cluster with Anthos. Windows server node-pools can be added to an existing or new Anthos cluster. Kubelet and Kube-proxy run natively on Windows nodes, allowing you to run mixed Windows and Linux containers in the same cluster. The admin cluster and the user cluster control plane continue to be Linux-based, providing you a consistent orchestration experience and management ease across Windows and Linux workloads.
Get started today
When considering modernizing your on-prem Windows estate, we recommend running Windows Server containers on Anthos in your own data center. If you are new to Anthos, the Anthos getting started page and the Coursera course on Architecting Hybrid Cloud with Anthos are good places to start. You can also find detailed documentation on our website, and our partners are eager to help you with any questions related to the published solutions, as is the GCP sales team. And as always, please don’t hesitate to reach out to us at anthos-onprem-windows@google.com if you have any feedback or need help unblocking your use case.
How to Pick a Database that is Suitable for Your Application
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Picking the right database for your application is not easy. The choice depends heavily on your use case—transactional processing, analytical processing, in-memory database, and so on—but it also depends on other factors. This post covers the different database options available within Google Cloud across relational (SQL) and non-relational (NoSQL) databases and explains which use cases are best suited for each database option.
Relational databases
In relational databases information is stored in tables, rows and columns, which typically works best for structured data. As a result they are used for applications in which the structure of the data does not change often. SQL (Structured Query Language) is used when interacting with most relational databases. They offer ACID consistency mode for the data, which means:
- Atomic: All operations in a transaction succeed or the operation is rolled back.
- Consistent: On the completion of a transaction, the database is structurally sound.
- Isolated: Transactions do not contend with one another. Contentious access to data is moderated by the database so that transactions appear to run sequentially.
- Durable: The results of applying a transaction are permanent, even in the presence of failures.
Because of these properties, relational databases are used in applications that require high accuracy and for transactional queries such as financial and retail transactions. For example: In banking when a customer makes a funds transfer request, you want to make sure the transaction is possible and it actually happens on the most up-to-date account balance, in this case an error or resubmit request is likely fine.
There are three relational database options in Google Cloud: Cloud SQL, Cloud Spanner, and Bare Metal Solution.
Cloud SQL: Provides managed MySQL, PostgreSQL and SQL Server databases on Google Cloud. It reduces maintenance cost and automates database provisioning, storage capacity management, back ups, and out-of-the-box high availability and disaster recovery/failover. For these reasons it is best for general-purpose web frameworks, CRM, ERP, SaaS and e-commerce applications.
Cloud Spanner: Cloud Spanner is an enterprise-grade, globally-distributed, and strongly-consistent database that offers up to 99.999% availability, built specifically to combine the benefits of relational database structure with non-relational horizontal scale. It is a unique database that combines ACID transactions, SQL queries, and relational structure with the scalability that you typically associate with non-relational or NoSQL databases. As a result, Spanner is best used for applications such as gaming, payment solutions, global financial ledgers, retail banking and inventory management that require ability to scale limitlessly with strong-consistency and high-availability.
Bare Metal Solution: Provides hardware to run specialized workloads with low latency on Google Cloud. This is specifically useful if there is an Oracle database that you want to lift and shift into Google Cloud. This enables data center retirements and paves a path to modernize legacy applications.
Non-relational databases
Non-relational databases (or NoSQL databases) store compex, unstructured data in a non-tabular form such as documents. Non-relational databases are often used when large quantities of complex and diverse data need to be organized. Unlike relational databases, they perform faster because a query doesn’t have to access several tables to deliver an answer, making them ideal for storing data that may change frequently or for applications that handle many different kinds of data.
For example, an apparel store might have a database in which shirts have their own document containing all of their information, including size, brand, and color with room for adding more parameters later such as sleeve size, collars, and so on.
Qualities that make NoSQL databases fast:
- Eventual consistency: stores usually exhibit consistency at some later point (e.g., lazily at read time)
- Horizontal scaling, usually using hashed distributions
- Typically, they are optimized for a specific workload pattern (i.e., key-value, graph, wide-column)
- Typically, they don’t support cross shard transactions or flexible isolation modes.
Because of these properties, non-relational databases are used in applications that require large scale, reliability, availability, and frequent data changes.They can easily scale horizontally by adding more servers, unlike some relational databases, which scale vertically by increasing the machine size as the data grows. Although, some relations databases such as Cloud Spanner support scale-out and strict consistency.
Non-relational databases can store a variety of unstructured data such as documents, key-value, graphs, wide columns, and more. Here are your non-relational database options in Google Cloud:
- Document databases: Store information as documents (in formats such as JSON and XML). For example: Firestore
- Key-value stores: Group associated data in collections with records that are identified with unique keys for easy retrieval. Key-value stores have just enough structure to mirror the value of relational databases while still preserving the benefits of NoSQL. For example: Datastore, Bigtable, Memorystore
- In-memory database: Purpose-built database that relies primarily on memory for data storage. These are designed to attain minimal response time by eliminating the need to access disks. They are ideal for applications that require microsecond response times and can have large spikes in traffic. For example: Memorystore
- Wide-column databases: Use the tabular format but allow a wide variance in how data is named and formatted in each row, even in the same table. They have some basic structure while preserving a lot of flexibility. For example: Bigtable
- Graph databases: Use graph structures to define the relationships between stored data points; useful for identifying patterns in unstructured and semi-structured information. For example: JanusGraph
There are three non-relational databases in Google Cloud:
- Firestore: Is a serverless document database which scales on demand and acts as a backend-as-a-service. It is DBaaS that increases the speed of building applications. It is perfect for all general purpose uses cases such as ecommerce, gaming, IoT and real time dashboards. With Firestore users can interact with and collaborate on live and offline data making it great for real-time application and mobile apps.
- Cloud Bigtable: Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, enabling you to store terabytes or even petabytes of data. It is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at sub-millisecond latency, and it is an ideal data source for MapReduce operations. It also supports the open-source HBase API standard to easily integrate with the Apache ecosystem including HBase, Beam, Hadoop and Spark along with Google Cloud ecosystem.
- Memorystore: Memorystore is a fully managed in-memory data store service for Redis and Memcached at Google Cloud. It is best for in-memory and transient data stores and automates the complex tasks of provisioning, replication, failover, and patching so you can spend more time coding. Because it offers extremely low latency and high performance, Memorystore is great for web and mobile, gaming, leaderboard, social, chat, and news feed applications.
Conclusion
Choosing a relational or a non-relational database largely depends on the use case. Broadly, if your application requires ACID transactions and your data structure is not going to change much, select a relational database.
In Google Cloud use Cloud SQL for any general-purpose SQL database and Cloud Spanner for large-scale globally scalable, strongly consistent use cases. In general, if your data structure may change later and if scale and availability is a bigger requirement than consistency then a non-relational database is a preferable choice. Google Cloud offers Firestore, Memorystore, and Cloud Bigtable to support a variety of use cases across the document, key-value, and wide column database spectrum.
For more comparison resources on each database check out the overview. For more hands-on experience with Bigtable, check out our on-demand training here and learn about migrating databases to managed services check out this whitepaper.
https://youtube.com/watch?v=2TZXSnCTd7E%3Fenablejsapi%3D1%26
For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.
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The Transformative Journeys of Financial Firms on Google Cloud: Watch Video
The reliance on cloud-based architectures, high performance computing, big data and more are accelerating in the banking, capital, insurance and financial services industries. Google Cloud had a strong role in transforming many businesses especially in the pandemic to smoothly transition into the digital space and understand their customers. Two years since then, financial firms have been able to design better products based on intelligent, real-time insights and leverage many capabilities of Google Cloud to deliver tailored experiences. So, how big of an impact Google Cloud has on the future of the financial services? The answer is huge and endless.
Watch this video to dive into the state of global financial services companies that leveraged modern cloud architecture for their sensitive data, platforms, devices and products while they increase revenues, stay compliant and curb costs.
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Linking the Middle East with Southern Europe and Asia: Google’s New Subsea Cables to Be Ready by 2024!
Today, we’re announcing that we are collaborating with Sparkle and others to build and operate two submarine cable systems linking the Middle East with southern Europe and Asia: the Blue Submarine Cable System connecting Italy, France, Greece, and Israel; and the Raman Submarine Cable System connecting Jordan, Saudi Arabia, Djibouti, Oman and
