Hackensack Meridian Health (HMH) executive Mark Eimer explains how an ambitiously-timed rollout of a comprehensive suite of Google products helped the entire organization—from doctors to IT staff—achieve better security, cultivate a more equitable work environment, and ultimately, improve patient outcomes.

How does a recently merged, 17-hospital healthcare system fast-track a platform migration and hardware rollout securely and in a way that improves work for everyone, regardless of location or role? These are the questions that kept me up at night in early 2020, when the pandemic demanded a “big bang”—something our legacy laptops and operating systems couldn’t handle.

We began our work with Google in 2020 with the adoption of Chrome as our default browser. As we migrated platforms, keeping patient data safe was of the utmost importance to us, along with providing every staff member with the tools they needed to work virtually. Our staff often experienced issues accessing our web-based applications using Internet Explorer or Edge Browser, a problem that went away when we switched to Chrome. Chrome’s versatile compatibility also made it easier for my team to migrate all of our web-based operations, and Chrome’s security and manageability were key components to making this switch a huge win for the organization.

The success of this migration led us to extend our Google partnership to patient care applications—where Google’s expertise in AI and ML helps scale the use of diagnostics tools and improve other aspects of the patient journey.

Achieving security at every step 

Like so many other healthcare organizations, we’ve been concerned about ransomware attacks. This is part of why we moved to Google Workspace and distributed over 3,000 Chrome OS devices in kiosk mode in March of 2020, when many of us went remote due to the pandemic. We were very concerned about team members accessing corporate applications through home devices that were running EOL operating systems (WIN7), as well as a general lack of antivirus and encryption measures.

We were protected by the fact that Google’s software and hardware both had built-in security features that we needed to stave off sophisticated attackers. For example, Chrome OS automatically updates to the latest security update and encrypts data living outside the cloud on the hardware. These features protected us from security-related disruptions, letting us securely move a huge library of file shares and emails across thousands of accounts to Google Chrome OS in just four months.

A year later, in March 2021, we migrated the enterprise over to Google Workspace and saw an immediate reduction in spam by 30% from the inherent built-in AI/ML. This meant staff were less likely to receive (and click through) phishing attempts. My team could connect, create, and collaborate easily and securely—even as more of us were working from home and needed to access sensitive data remotely. 

Leveling the playing field

As an organization, we were surprised by how many team members didn’t have personal computers at home. We quickly decided that if we needed team members to work from home, the health network would have to supply hardware. Chromebooks’ lower price tag compared to PCs—on top of their built-in security controls—allowed us to purchase, deploy, and support that initial distribution of 3,000 Chromebooks to team members in less than three weeks, providing devices to every eligible remote employee instead of just a select few. This was vital to reaching our equitable technology goal as part of our diversity and inclusion initiative: everybody has the same tools to do good work.

When all employees have what they need to do their jobs well, we get better patient outcomes. Before we began this cloud adoption journey, patient and staff experiences were different within the hospitals and outside of them. 

Now it’s the same wherever our staff is, and we’ve seen efficiency and accessibility benefits extend to the patient side. For example, we built a web-based contact center that supports 80 locations that use Workspace and Chrome OS devices. Since customer service, admin, and providers are all on the same system, it has become a one-stop shop for patients.

Furthermore, through the Grow with Google program, we were able to provide another benefit to employees that drove our equity goals. Google trained 50 non-IT staff members—from environmental services, food and nutrition, and other non-tech areas who were interested in making a career change to IT—on the Google products we were using. They may not have thought about switching to a career in IT before the Grow with Google program came to our organization, but through this partnership, they now have that opportunity.

A strategic, long-term partner

With any large-scale rollout, the work doesn’t end once laptops are in employee hands. Google has shown their commitment to long-term collaboration as they continuously optimize their products for the unique needs of healthcare providers and go the extra mile in tailoring tools to our staff’s workflows. 

For example, on the Chrome OS side, the Google team has helped our registration desks and document centers with device integration for hardware like credit card readers and e-signature pads. They’ve also helped us meet security and privacy requirements mandated by state and federal governments around HIPAA, Medicaid, and Medicare reimbursements. Over this next year, we’ll look at a feature roadmap with Google Cloud to deliver further enhancements, iterating on the product itself to meet our needs for the present and the future.

Delivering the future of healthcare

The benefits we’ve seen around security and usability—and the ability to provide all staff with equal access to Google’s technology—are why we’re expanding our partnership with Google to both the administrative and clinical sides of HMH. In addition to further Google rollouts with corporate, next year we’re distributing Chromebooks to all 350 of our ambulatory clinics.

We’re also working with the Google professional services team to create a custom AI model that analyzes 3D mammogram images. This AI model will enable two providers to read mammograms—which adheres to international best practices but is currently rare in the US—without requiring additional time. Conducting double readings of mammograms will yield better health outcomes for our patients, such as a lower patient recall rate and an increased accuracy in detecting breast cancer.

We’re currently building the model using a variety of Google Cloud products, including Cloud Healthcare API. Once complete, this model is expected to be trained, deployed, and maintained in Google’s Vertex AI, allowing our providers to be more productive as they make clinical decisions with AI support. As the model is proven over time, we plan to make the predictive services accessible to other healthcare organizations.

With Google, we’re able to achieve a unified architecture for storing data as well as training and deploying AI models, which enable our staff to work more efficiently and securely from anywhere. While I may not be able to predict the future as accurately as AI can, I foresee our continued partnership with Google as a key part of HMH’s improved provider and patient outcomes.

Data continues to grow in volume and is increasingly distributed across lakes, warehouses, clouds, and file formats. As more users demand more use cases, the traditional approach to build data movement infrastructure is proving difficult to scale. Unlocking the full potential of data requires breaking down these silos, and is increasingly a top priority for enterprises.

Earlier this year, we previewed BigLake, a storage engine that extends innovations in BigQuery storage to open file formats running on public cloud object stores. This allows customers to build secure multi-cloud data lakes over open file formats. BigLake provides consistent, fine-grained security controls for Google Cloud and open-source query engines to interact with data. Today, we are excited to announce General Availability for BigLake, and a set of new capabilities to help you build a differentiated data platform.

“We are using GCP to build and extend one of the street’s largest risk systems. During several tests we have seen the great potential and scale of BigLake. It is one of the products that could support our cloud journey and drive application’s future efficiency” – Scott Condit, Director, Risk CTO Deutsche Bank.

Build a distributed data lake that spans across warehouses, object stores & clouds with BigLake

Customers can create BigLake tables on Google Cloud Storage (GCS), Amazon S3 and ADLS Gen 2 over supported open file formats, such as Parquet, ORC and Avro. BigLake tables are a new type of external table that can be managed similar to data warehouse tables. Administrators do not need to grant end users access to files in object stores, but instead manage access at a table, row or a column level. These tables can be created from a query engine of your choice, such as BigQuery or open-source engines using the BigLake connector. Once these tables are created, BigLake and BigQuery tables can be centrally discovered in the data catalog and managed at scale using Dataplex.

BigLake extends the BigQuery storage API to object stores to help you build a multi-compute architecture. BigLake connectors are built on the BigQuery storage API and enable Google Cloud DataFlow and open-source query engines (such as Spark, Trino, Presto, Hive) to query BigLake tables by enforcing security. This eliminates the need to move the data to a query engine specific use case and security only needs to be configured at one place and is enforced everywhere.

“We are using GCP to design datalake solutions for our customers and transform their digital strategy to create a data-driven enterprise. Biglake has been critical for our customers to quickly realize the value of analytical solutions by reducing the need to build ETL pipelines and cutting-down time-to-market. The performance & governance features of BigLake enabled a variety of data lake use cases for our customers.” – Sureet Bhurat, Founding Board member – Synapse LLC

BigLake unlocks new use cases using Google Cloud and OSS Query engines

During the preview, we saw a large number of customers use BigLake in various ways. Some of the top use cases include:

Building secure and governed data lakes for open-source workloads – Workloads migrating from Hadoop, Spark first customers, or those using Presto/Trino, can now use BigLake to build secure, governed and performant data lakes on GCS. BigLake tables on GCS provide fine-grained security, table management (vs giving access to files), better query performance and integrated governance with Dataplex. These characteristics are accessible across multiple OSS query engines when using the BigLake connectors.

“To support our data driven organization, Wizard needs a data lake solution that leverages open file formats and can grow to meet our needs. BigLake allows us to build and query on open file formats, scales to meet our needs, and accelerates our insight discovery. We look forward to expanding our use cases with future BigLake features” – Rich Archer, Senior Data Engineer – Wizard

Eliminate or reduce data duplication across data warehouses and lakes – Customers who use GCS, and BigQuery managed storage had to previously create two copies of data to support users using BigQuery and OSS engines. BigLake makes the GCS tables more consistent with BigQuery tables, reducing the need to duplicate data. Instead, customers can now keep a single copy of data split across BigQuery storage and GCS, and data can be accessed by BigQuery or OSS engines in either places in a consistent, secure manner.

Fine-grained security for multi-cloud use cases – BigQuery Omni customers can now use BigLake tables on Amazon S3, and ADLS Gen 2 to configure fine grained security access control, and take advantage of localized data processing, and cross cloud transfer capabilities to do multi-cloud analytics. Tables created on other clouds are centrally discoverable on Data catalog for ease of management & governance

Interoperability between analytics and data science workloads – Data science workloads, using either Spark or Vertex AI notebooks can now directly access data in BigQuery or GCS through the API connector, enforcing security & eliminating the need to import data for training models. For BigQuery customers, these models can be imported back into BigQuery ML to produce inferences.

Build a differentiated data platform with new BigLake capabilities

We are also excited to announce new capabilities as part of this General Availability launch. These include:

• Analytics Hub support: Customers can now share BigLake tables on GCS with partners, vendors or suppliers as linked data sets. Consumers can access this data in place through the preferred query engine of their choice (BigQuery, Spark, Presto, Trino, Tensorflow).
• BigLake tables is now the default table type BigQuery Omni, and has been upgraded from the previous default of external tables.
• BigQuery ML support: BigQuery customers can now train their models on GCS BigLake tables using BigQuery ML, without needing to import data, and accessing the data in accordance to the access policies on the table.
• Performance acceleration (preview): Queries for GCS BigLake tables can now be accelerated using the underlying BigQuery infrastructure. If you would like to use this feature please get in touch with your account team or fill out this form.
• Cloud Data Loss Prevention (DLP) profiling support (coming soon): Cloud DLP can soon scan BigLake tables to identify and protect sensitive data at scale. If you would like to use this feature please get in touch with your account team.
• Data masking and audit logging (Coming soon): BigLake tables now support dynamic data masking, enabling you to mask sensitive data elements to meet compliance needs. End user query requests to GCS for BigLake tables are now audit logged and are available to query via logs.

Next steps

Refer to BigLake documentation to learn more, or get started with this quick start tutorial. If you are already using external tables today, consider upgrading them to BigLake tables to take advantage of above mentioned new features. For more information, reach out to the Google cloud account team to see how BigLake can add value to your data platform.

As digital transformation continues apace, cloud native adoption has skyrocketed — and so has the volume, velocity, and variety of data that organizations must monitor to maintain visibility over their IT environments. Rather than a fixed number of virtual machines (VMs) running a single application each, systems are more flexible and ephemeral — with thousands of containers and microservices emitting data — and services and systems have greater interdependencies. All of this has led to an explosion in complexity that makes it nearly impossible to reliably and efficiently operate cloud-native services using traditional observability solutions without dramatically increasing overhead. 

Built on Google Kubernetes Engine (GKE), Chronosphere is the only cloud-native observability solution that helps teams quickly resolve incidents before they affect the customer experience and the bottom line. And the relationship between Google Cloud and Chronosphere has developed into a growing partnership. Chronosphere brings the ability to rapidly and reliably control observability data and costs to Google Cloud customers, while maintaining open source compatibility. Recently, GV (Google Ventures) participated in Chronosphere’s $115 million round of Series C funding. Chronosphere is also available on Google Cloud Marketplace, making it easy for customers to procure and deploy.

Why Chronosphere chose to build on Google Cloud

Chronosphere’s founders, CEO, Martin Mao, and CTO, Rob Skillington, wanted to build an observability solution that solved some of the same problems they encountered when leading the observability team at Uber. The challenges at Uber focused on scale, reliability and cost, with a key emphasis on how these were not easily solvable for monitoring of cloud-native applications. Martin and Rob were looking to build a solution that was cloud-native, highly reliable, and capable of managing the high volume of data that modern organizations process every day. For those reasons, Martin and Rob chose to build Chronosphere on top of Google Kubernetes Engine (GKE): 

Cloud-native mindset: When Martin and Rob launched Chronosphere, one of the first considerations was this: A cloud-native problem needs a cloud-native solution. Google Cloud and Chronosphere work together to provide a purpose-built software-as-a-service (SaaS) observability solution platform for cloud-native technologies like Kubernetes. Chronosphere runs much of its critical infrastructure on Google Cloud, using the service’s global infrastructure to deliver secure and reliable services to hypergrowth customers.  

Open platform: Martin and Rob have a commitment to openness — a commitment that Google Cloud makes to its customers as well. All of the ins and outs of Chronosphere are open source compatible –built on GKE, the platform  ingests both metric and trace data in a variety of open source formats, such as Prometheus, OpenTelemetry, and StatsD, so teams can get onboard easily while avoiding vendor lock-in. And they remain fully in control of how much data to ingest, how it’s ingested, how it’s stored, and for how long. That way, customers remain in control of their observability costs while their systems and data continue to grow, and can troubleshoot and remediate issues more quickly. 

Mission-critical performance: Observability is a mission-critical service because it is essential for the reliability and performance of mission-critical systems. For this reason, Martin and Rob wanted to offer the strongest service level agreements (SLAs) possible for availability, reliability, and performance. Google Cloud provides the secure global infrastructure that allows them to do so. 

Data volume: Modern organizations emit huge volumes of data that they have to sift through in near-real time to gain full visibility. Google Cloud offers the same infrastructure and networks that Google uses to support billions of users every day, capable of processing petabytes of data with ease. 

How Chronosphere works with Google Cloud

GKE provides specific technical benefits that Chronosphere leverages to offer customers the performance and availability they need to spend less time managing their observability systems and more time creating value. 

The Chronosphere collector is a Kubernetes-native, Prometheus-compatible agent that collects, processes, and exports telemetry data, including metrics and traces. When you install the collector in a Kubernetes cluster, it automatically discovers all your workloads and starts to gather data from them. 

The Chronosphere UI tailors the experience to each user based on what services that individual owns or which teams that person belongs to. This makes the overall user experience friendlier and less overwhelming while helping people get to the bottom of issues faster.

Running on GKE means that Chronosphere can leverage Google Cloud’s multiple zones in each geographic region to distribute workloads evenly, ensure customer data stores are isolated from each other, and build tolerance for zonal failures. It also allows for data persistence across multiple zones. At the same time, Google Cloud’s global load balancers bring the data physically closer to the customer, shortening the time it takes for a data point to be visible to a user after its emission.

What this means for customers

The partnership brings together the best in cloud-native services and cloud-native observability. With the power of Google Cloud and Chronosphere, observability teams can transform their observability data based on need, context, and utility, storing only the useful data to reduce costs and improve performance. With purpose-built solutions for a cloud-native world, teams gain faster issue detection and resolution, and also benefit from Chronosphere’s 99.99% availability and open source compatibility — eliminating vendor lock-in. 

“Companies growing their online infrastructure risk huge hits to their bottom line if they don’t also manage increased complexity and cost,” says Martin. “Our partnership with Google Cloud brings together the world’s leading cloud services platform with our powerful observability solution to unlock the benefits of a cloud-native world, while optimizing for efficiency, reliability, and cost.”

Google Cloud and Chronosphere: The perfect match

Google Cloud and Chronosphere offer an industry-leading observability solution built from the ground up to abstract away the complexities of cloud computing for already stressed teams. And the future of this partnership is sure to bring new innovations around performance, reliability, and cost efficiency as both companies continue to invest in these three areas. The goal: to deliver the best performance for cost in the industry. 

Learn more about what Chronosphere and Google Cloud are doing for customers, and check out the Chronosphere listing on Google Cloud Marketplace.

Editor’s note: Today’s blog post is from Mayank Singh, Chief Digital Officer and VP Marketing and IT at pizza delivery group Domino’s Indonesia. He explains how Google Maps Platform is enabling their business to optimize their operations and supporting the franchise’s ambitious expansion strategy in one of the world’s most exciting emerging markets.

When I was developing India’s first hyper local on-demand food delivery digital commerce platform for Domino’s Pizza India, I never imagined life would take me to a country with such a different culture to my own.

But in 2018, when Domino’s Indonesia invited me to drive its expansion strategy as Senior General Manager to lead Digital, IT and Marketing functions, I jumped at the chance to try my hand in another gigantic emerging nation that brims with entrepreneurial hunger and innovative energy.

Indonesia is a massive archipelago of more than 17,500 islands and 270 million people, one of the world’s most promising developing markets. Here, the opportunities for a global brand, such as Domino’s, are almost boundless. At the same time, we’re extremely cautious. We want to make sure that any new store we open has the correct demographic profile to be profitable.

This is one of the key areas in which Google Maps Platform enables us to satisfy the hunger of a vibrant, fast-moving society. The Google Maps Platform APIs empower us with much more than the ability to maintain Domino’s 30-minute delivery pledge. They play an instrumental role in our value chain before a store even begins selling pizzas, providing vital insights in our hunt for new shop locations.

Mapping the dizzying pace of change in one of Asia’s fastest growing economies

Under the Domino’s Indonesia model, every outlet has a defined territory and can only take orders from that zone. Instead of customers picking a store, we assign them one based on their location. That’s why selecting zones that have the highest profit potential (due to factors such as population, income, education, and business activity) is essential to our success.

The first step is casting a net around the test location that covers anywhere within a nine-minute drive to any point in the proposed zone. This is enabled by the Google Maps Platform APIs, including Distance Matrix API.

Things become really interesting and complicated when considering Indonesia’s dizzying pace of change. Social, economic, and infrastructure transformation in Indonesia literally unfolds before your eyes, meaning the map in any area can change overnight.

A place that had little to no development yesterday might be tomorrow’s next desirable suburb—populated by families drawn to new schools, but also increased traffic congestion caused by community growth. By overlaying our data, such as population, average income, and age range onto Google Maps, our team can better visualize the revenue potential in that zone compared to neighboring stores. Since the definition of a profitable zone is in constant flux, we need our local insights to be ahead of the game.

Charting Indonesia’s transformation with speed and accuracy is crucial in our mission to carve out delivery zones that serve communities that need our service most while maximizing our revenues.

Retrofitting Indonesian delivery zones to optimize Domino’s success

Expansion is just one side of the story. Just as important, we must keep an eye on existing outlets to make sure that traffic conditions, road configurations, prominent sites, public infrastructure, and more, hit the sweet spot for timely delivery and revenue optimization.

A successful delivery area, for example, might fall victim to its own popularity, causing congestion that prevents timely delivery to once viable addresses. In such cases, the specified drive time in the delivery matrix shrinks. We accordingly must downsize the service zone, while adding micro-zones to satisfy demand. Google Maps Platform features such as Traffic Layer in Maps JavaScript API enable the real-time traffic intelligence that helps us make precision calls in the evolving mission of zone optimization.

Conversely, we might want to expand a store territory thanks to a new highway or roadworks that improve traffic circulation. Here, too, Google Maps Platform is the fastest in mapping such shifts in the urban landscape. We’re even planning to experiment with a new feature whereby delivery zones change in size over the course of a day, based on factors such as peak traffic and roadworks. This new project will be enabled by Google Maps Platform real-time road intelligence.

Enabling Indonesia’s pandemic support with contactless delivery and support for frontline healthcare

Google Maps Platform also helps us respond quickly to societal changes. When the pandemic hit Indonesia, for example, our team was all hands on deck developing ways to deliver pizzas in the safest possible way. We overhauled our order system to make contactless delivery and contactless takeaway the default method of connecting customers to their pizzas. This was done to reassure customers how safe it is to order with Domino’s. Customers can locate us using the map UI, view the ordering options and have a contactless takeaway experience.

Google Maps Platform products such as Places API, which provides rich places details and location information as well as the Static Street View API which provides a more accurate visual of the location, now enable our drivers to pinpoint a drop spot, such as ‘the porch with the red roof’ with precision accuracy.

Our team is also proud of how we supported healthcare workers at the frontline of the pandemic. Google Maps Platform was an invaluable solution in enabling us to deliver pizzas to these vital frontline workers. It gave all of us at Domino’s Pizza Indonesia great satisfaction to see smiles on the faces of heroes working hard to keep people safe and well in this immense nation.

For more information on Google Maps Platform, visit our website.