Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.

Building a strong brand in today’s hyper-competitive business environment takes vision. It also requires a flexible, easily managed approach to digital asset management (DAM), so marketing professionals and other stakeholders can easily share, store, track, and manipulate assets to build the brand.

Many of today’s leading companies, including JetBlue, Slack, TripAdvisor, Lyft, and HealthONE, rely on Brandfolder to deliver consistent, organized, and efficient brand experiences. Brandfolder provides an easy-to-use platform that can scale across an entire company with little end-user training, empowering customers to distribute digital assets wherever they are needed. Customers also gain much greater insight into how those assets are used, and how to use them more effectively in marketing campaigns and brand messaging.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform.”

—Ajay Rajasekharan, Head of Data Science, Brandfolder

Brandfolder is constantly advancing its development efforts to introduce new data-driven features without complicating the user experience. Big data, artificial intelligence (AI), and machine learning (ML) are key to meeting customers’ unique business needs, and essential for Brandfolder to compete in the fast-moving DAM industry. To enhance these capabilities, Brandfolder sought a public cloud provider that could help it scale its data pipeline cost effectively while providing access to advanced AI technologies.

After graduating from the Techstars startup accelerator program in 2013, Brandfolder tried two other cloud providers before standardizing on Google Cloud Platform (GCP).

“We saw a difference with Google Cloud from the very beginning because the interactions felt like a strategic relationship,” says Jim Hanifen, Head of Product at Brandfolder. “Google gave us startup credits and a lot of face-to-face support, which we hadn’t experienced with other cloud providers. We decided to move our entire infrastructure to Google Cloud Platform.”

Building an ML platform for brand intelligence

After performing an initial lift-and-shift migration of virtual machines (VMs) onto Compute Engine, Brandfolder built an ML platform using GCP managed services to seamlessly deliver its data products. The platform leverages Cloud SQL, Cloud Storage as the data lake, Cloud Dataproc for cloud-native Apache Spark computing clusters, Cloud Composer as the batch job scheduler, Cloud Pub/Sub as the backbone data pipeline, Container Registry to store Docker images, and Google Kubernetes Engine (GKE) as the application orchestrator. Cloud Dataflow brings data into the data lake and into BigQuery for analysis.

“Google Cloud made it easy to build an ML platform to quickly iterate through different brand intelligence use cases and release data-driven product features into the Brandfolder platform,” says Ajay Rajasekharan, Head of Data Science at Brandfolder, who describes the architecture in a detailed blog. “We simply ingest raw application and event data on one end and output an ML service on the other.”

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost. We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

—Brett Nekolny, Head of Engineering, Brandfolder

For many general use cases, Brandfolder does not need to build custom ML models, and instead relies on pre-trained API models from GCP. For example, it uses Vision API and Video Intelligence API to auto-tag creative assets on import to enable fast, intuitive searches across images and videos. When more product- and brand-specific modeling is required to address unique customer use cases, Brandfolder builds and trains custom ML models using its GCP pipeline or Cloud AutoML, a suite of products built on Google transfer learning and neural architecture search technology. For example, if a Brandfolder customer makes different types of grills, Brandfolder can use AutoML Vision to train a model to recognize the different grills.

“Moving to Google Cloud Platform allows us to complete more sophisticated data analysis and ML models much faster, and at a much lower cost,” explains Brett Nekolny, Head of Engineering at Brandfolder. “We can create brand-specific ML models 12x faster and get them into production quickly to address our customers’ unique business needs.”

Industry-leading security and performance

Google Cloud’s security model helps Brandfolder give existing and prospective customers peace of mind that their data will be protected. Cloud Identity & Access Management (Cloud IAM) provides enterprise-grade access control, while Cloud Identity-Aware Proxy (Cloud IAP) enables remote users to work more securely without the hassles of a VPN client. GCP also isolates cloud resources into projects, making it easy to assign permissions and keep data and VMs organized and segregated.

“With Google Cloud, everything begins and ends with security, which makes things very easy for us,” says Jim. “If we’re under a security review, we can submit a Google security white paper. If a potential customer has security concerns, we tell them we are hosted on GCP, and those concerns go away.”

To give customers even better application performance for accessing their brand assets, Brandfolder uses Cloud Memorystore, an in-memory data store service for Redis, to cache data and provide sub-millisecond data access for production applications.

“It was much easier for us to use Cloud Memorystore versus running Redis on our compute instances,” says Brett. “The high availability, replication across zones, and automatic failover with no data loss are big for us.”

Global private network interconnects between Google Cloud and the Fastly content delivery network (CDN) dramatically reduce latency, allowing Brandfolder’s customers to deliver and update even very large creative assets quickly around the world.

“What’s beautiful about the relationship between Google and Fastly is that if one of our customers uploads a new version of an asset, we can propagate that out to Fastly, and the new version will automatically show up in all the places where it’s referenced,” says Brett.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter. Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

—Jim Hanifen, Head of Product, Brandfolder

Improving employee and customer productivity

Brandfolder also uses Google solutions for real-time collaboration and productivity, using G Suite to connect employees with intuitive, cloud-based apps. Teams use Gmail, Calendar, Docs, Drive, Sheets, Slides, and Hangouts Meet every day to move the business forward. Many of Brandfolder’s customers are also G Suite users, and Brandfolder offers a plug-in that allows them to view their creative assets inside of Docs and pull images in as needed. Customers can also log into Brandfolder with their G Suite credentials, making the solution even easier to use.

“We’ve been using G Suite since the beginning, and it’s helped us collaborate efficiently to build a successful, growing company,” says Jim. “Our teams expect to have that kind of close collaboration, and everyone here enjoys the G Suite experience.”

Driving 99 percent annual business growth

With automated tagging and other innovative AI-based features, Brandfolder is helping customers locate and distribute assets faster. As a result, Brandfolder is building customer loyalty and increasing sales, growing its business by 99 percent year-over-year. Since moving to GCP, Brandfolder has been able to scale its analytics and data pipeline 50x without a corresponding increase in costs and has not had to expand its development team.

“The ability to quickly solve problems with AI has a substantial impact on our revenue, and that’s more apparent every quarter,” says Jim. “Few of our competitors are doing product- or brand-specific modeling because it takes a lot of time and resources. We overcame those hurdles with Google Cloud.”

June is the month that holds the summer solstice, and some of us in the northern hemisphere get to enjoy the longest days of sunshine out of the entire year. We used all the hours we could in June to deliver a flurry of new features across BigQuery, Dataflow, Data Fusion, and more.  Let’s take a look!

Simple, Sophisticated, and Secure

Usability is a key tenant of our data analytics development efforts. Our new user-friendly BigQuery improvements this month include:

• Flexible data type casting
• Formatting to change column descriptions 
• GRANT/REVOKE access control commands using SQL

We hope this will delight data analysts, data scientists, DBAs, and SQL-enthusiasts who can find out more details in our blog here.

Beyond simplifying commands, we also recognize that it’s equally important to have more sophistication when dealing with transactions. That’s why we introduced multi-statement transactions in BigQuery.

As you probably know, BigQuery has long supported single-statement transactions through DML statements, such as INSERT, UPDATE, DELETE, MERGE and TRUNCATE, applied to one table per transaction. With multi-statement transactions, you can now use multiple SQL statements, including DML, spanning multiple tables in a single transaction. 

This means that any data changes across multiple tables associated with all statements in a given transaction are committed atomically (all at once) if successful—or all rolled back atomically in the event of a failure. 

We also know that organizations need to control access to data, down to the granular level and that, with the complexity of data platforms increasing day by day, it’s become even more critical to identify and monitor who has access to sensitive data. 

To help address these needs,  we announced the general availability of BigQuery row-level security. This capability gives customers a way to control access to subsets of data in the same table for different groups of users. Row-level security in BigQuery enables different user personas access to subsets of data in the same table and can easily be created, updated, and dropped using DDL statements. To learn more, check out the documentation and best practices.

Simple, Safe, and Smart

Beyond building a simpler, more sophisticated and more secure data platform for customers, our team has been focused on providing solutions powered by built-in intelligence. One of our core beliefs is that for machine learning to be adopted and useful at scale, it must be easy to use and deploy.  

BigQuery ML, our embedded machine learning capabilities, have been adopted by 80% of our top customers around the globe and it has become a cornerstone of their data to value journey.  

As part of our efforts, we announced the general availability of AutoML tables in BigQuery ML.  This no-code solution lets customers automatically build and deploy state-of-the-art machine learning models on structured data. With easy integration with Vertex AI, AutoML in BQML makes it simple to achieve machine learning magic in the background. From preprocessing data to feature engineering and model tuning all the way to cross validation, AutoML will “automagically” select and ensemble models so everyone—even non-data scientists—can use it.   

Want to take this feature for a test drive? Try it today on BigQuery’s NYC Taxi public dataset following the instructions in this blog! 

Speaking of public datasets, we also introduced the availability of Google Trends data in BigQuery to enable customers to measure interest in a topic or search term across Google Search.  This new dataset will soon be available in Analytics Hub and will be anonymized, indexed, normalized, and aggregated prior to publication. 

Want to ensure your end-cap displays are relevant to your local audience?  You can take signals from what people are looking for in your market area to inform what items to place. Want to understand what new features could be incorporated into an existing product based on what people are searching for?  Terms that appear in these datasets could be an indicator of what you should be paying attention to.

All this data and technology can be put to use to deploy critical solutions to grow and protect your business. For example,  it can be difficult to know how to define anomalies during detection. If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML. 

But what if you don’t know what kind of anomaly to expect, and you don’t have labeled data? Unlike typical predictive techniques that leverage supervised learning, organizations may need to be able to detect anomalies in the absence of labeled data. 

That’s why, we were particularly excited to announce the public preview of new anomaly detection capabilities in BigQuery ML that leverage unsupervised machine learning to help you detect anomalies without needing labeled data.  

Our team has been working with a large number of enterprises who leverage machine learning for better anomaly detection. In financial services for example, customers have used our technology to detect machine-learned anomalies in real-time foreign exchange data.  

To make it easier for you to take advantage of their best practices, we teamed up with Kasna to develop sample code, architecture guidance, and a data synthesizer that generates data so you can test these innovations right away. 

Simple, Scalable, and Speedy

Capturing, processing and analyzing data in motion has become an important component of our customer architecture choices. Along with batch processing, many of you need the flexibility to stream records into BigQuery so they can become available for query as they are written.  

Our new BigQuery Storage Write API combines the functionality of streaming ingestion and batch loading into a single API. You can use it to stream records into BigQuery or even batch process an arbitrarily large number of records and commit them in a single atomic operation.

Flexible systems that can do batch and real-time in the same environment is in our DNA: Dataflow, our serverless, data processing service for streaming and batch data was built with flexibility in mind.  

This principle applies not just to what Dataflow does but also how you can leverage it—whether you prefer using Dataflow SQL right from the BigQuery web UI, Vertex AI notebooks from the Dataflow interface, or the vast collection of pre-built templates to develop streaming pipelines.

Dataflow has been in the news quite a bit recently. You might have noted the recent introduction of Dataflow Prime, a new no-ops, auto-tuning functionality that optimizes resource utilization and further simplifies big data processing. You might have also read that Google Dataflow is a Leader in The 2021 Forrester Wave™: Streaming Analytics, giving Dataflow a score of 5 out of 5 across 12 different criteria.  

We couldn’t be more excited about the support the community has provided to this platform. The scalability of Dataflow is unparalleled and as you set your company up for more scale, more speed, and “streaming that screams”, we suggest you take a look at what leaders at Sky, RVU or Palo Alto Networks have already accomplished.

If you’re new to Dataflow, you’re in for a treat: this past month, Priyanka Vergadia (AKA CloudGirl) released a great set of resources to get you started. Read her blog here and watch her introduction video below!

https://youtube.com/watch?v=WRspZRG9e90%3Fenablejsapi%3D1%26

Simple structure that sticks together

We thrive to be the partner of choice for your transformation journey, regardless where your data comes from and how you choose to unify your data stack.  

Our partners at Tata Consultancy Services (TCS) recently released research that highlights the importance of a unifying digital fabric and how data integration services like Google Cloud Data Fusion can enable their clients to achieve this vision.

We also  announced SAP Integration with Cloud Data Fusion, Google Cloud’s native data integration platform, to seamlessly move data out of SAP Business Suite, SAP ERP and S4/HANA. To date, we provide more than 50 pipelines in Cloud Data Fusion to rapidly onboard SAP data.  

This past month, we introduced our SAP Accelerator for Order to Cash.  This accelerator is a sample implementation of the SAP Table Batch Source feature in Cloud Data Fusion and will help you get started with your end-to-end order to cash process and analytics. 

It includes sample Cloud Data Fusion pipelines that you can configure to connect to your SAP data source, perform transformations, store data in BigQuery, and set up analytics in Looker. It also comes with LookML dashboards which you can access on Github.

Countless great organizations have chosen to work with Google for their SAP data. In June, we wrote about ATB Financial’s journey and how the company uses data to better serve over 800,000 customers, save over CA$2.24 million in productivity, and realize more than CA$4 million in operating revenue through “D.E.E.P”, a data exposure enablement platform built around BigQuery.

Finally, if you are an application developer looking for a unified platform that brings together data from Firebase Crashlytics, Google Analytics, Cloud Firestore, and third party datasets, we have good news!  

This past month, we released a unified analytics platform that combines Firebase, BigQuery, Google Looker and FiveTran to easily integrate disparate data sources,  and infuse data into operational workflows for greater product development insights and increased customer experience. This resource comes with sample code, a reference guide and a great blog!  We hope you enjoy it. See you all next month!

https://youtube.com/watch?v=L25Vfzr2Ciw%3Fenablejsapi%3D1%26