It may be surprising to know that U.S. natural catastrophe economic losses totaled $119 billion in 2020, and 75% (or $89.4B) of those economic losses were caused by severe storms and cyclones. In the insurance industry, data is everything. Insurers use data to influence underwriting, rating, pricing, forms, marketing, and even claims handling. When fueled by good data, risk assessments become more accurate and produce better business results. To make this possible, the industry is increasingly turning to predictive analytics, which uses data, statistical algorithms, and machine learning (ML) techniques to predict future outcomes based on historical data. Insurance firms also integrate external data sources with their own existing data to generate more insight into claimants and damages. Google Cloud Public Datasets offers more than 100 high-demand public datasets through BigQuery that helps insurers in these sorts of data “mashups.” 

One particular dataset that insurers find very useful is Severe Storm Event Details from the U.S. National Oceanic and Atmospheric Administration (NOAA). As part of the Google Cloud Public Datasets program and NOAA’s Public Data Program, this severe storm data contains various types of storm reports by state, county, and event type—from 1950 to the present—with regular updates. Similar NOAA datasets within the Google Cloud Public Datasets program include the Significant Earthquake Database, Global Hurricane Tracks, and the Global Historical Tsunami Database.  

In this post, we’ll explore how to apply storm event data for insurance pricing purposes using a few common data science tools—Python Notebook and BigQuery—to drive better insights for insurers.

Predicting outcomes with severe storm datasets

For property insurers, common determinants of insurance pricing include home condition, assessor and neighborhood data, and cost-to-replace. But macro forces such as natural disasters—like regional hurricanes, flash floods, and thunderstorms—can also significantly contribute to the risk profile of the insured. Insurance companies can leverage severe weather data for dynamic pricing of premiums by analyzing the severity of those events in terms of past damage done to property and crops, for example. 

It’s important to set the premium correctly, however, considering the risks involved. Insurance companies now run sophisticated statistical models, taking into account various factors—many of which can change over time. After all, without accurate data, poor predictions can lead to business losses, particularly at scale.  

The Severe Storm Event Details database includes information about a storm event’s location, azimuth (an angle measurement used in celestial coordination), distance, impact, and severity, including the cost of damages to property and crops. It documents:

• The occurrence of storms and other significant weather events of sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce.
• Rare, unusual weather events that generate media attention, such as snow flurries in South Florida or the San Diego coastal area.
• Other significant weather events, such as record maximum or minimum temperatures or precipitation that occur in connection with another event.

Data about a specific event is added to the dataset within 120 days to allow time for damage assessments and other analysis.

Driving business insights with BigQuery and notebooks

Google Cloud’s BigQuery provides easy access to this data in multiple ways. For example, you can query directly within BigQuery and perform analysis using SQL. 

Another popular option in the data science and analyst community is to access BigQuery from within the Notebook environment to intersperse Python code and SQL text, and then perform ad hoc experimentation. This uses the powerful BigQuery compute to query and process huge amounts of data without having to perform the complex transformations within the memory in Pandas, for example.

In this Python notebook, we have shown how the severe storm data can be used to generate risk profiles of various zip codes based on the severity of those events as measured by the damage incurred. The severe storm dataset is queried to retrieve a smaller dataset into the notebook, which is then explored and visualized using Python. Here’s a look at the risk profiles of the zip codes:

Another Google Cloud resource for insurers is BigQuery ML, which allows them to create and execute machine learning models on their data using standard SQL queries. In this notebook, with a K-Means Clustering algorithm, we have used BigQuery ML to generate different clusters of zip codes in the top five states impacted by severe storms. These clusters show different levels of impact by the storms, indicating different risk groups. 

The example notebook is a reference guide to enable analysts to easily incorporate and leverage public datasets to augment their analysis and streamline the journey to business insights. Instead of having to figure out how to access and use this data yourself, the public datasets, coupled with BigQuery and other solutions, provide a well-lit path to insights, leaving you more time to focus on your own business solutions.

Making an impact with big data

Google Cloud’s Public Datasets is just one resource within the broader Google Cloud ecosystem that provides data science teams within the financial services with flexible tools to gather deeper insights for growth. The severe storm dataset is a part of our environmental, social, and governance (ESG) efforts to organize information about our planet and make it actionable through technology, helping people make a positive impact together. 

To learn more about this public dataset collaboration between Google Cloud and NOAA, attend the Dynamic Pricing in Insurance: Leveraging Datasets To Predict Risk and Price session at the Google Cloud Financial Services Summit on May 27. You can also check out our recent blog and explore more about BigQuery and BigQuery ML.

The ecommerce playing field has been hard to navigate for most retailers, and Cart.com is on a mission to change that. Traditionally, retailers needing to run their online store, order fulfillment, customer service, marketing, and other essential activities have had to cobble together systems to get the capabilities they need – much less having access to analytics across these functions. The result is costly, siloed ecommerce operations that are difficult to manage and scale.

It’s clearly not a formula for success, yet that’s the reality facing most retailers. Cart.com, in contrast, has set out to democratize ecommerce by giving brands of all sizes the full capabilities they need to take on the world’s largest online retailers. Our end-to-end environment empowers retailers to keep more of their revenue, set up proven strategies for managing all aspects of their business, and act on valuable insights from customer data every step of the way.

Together with our talented team, we’re building a unified ecommerce platform that already provides value to many leading or up and coming brands including Whataburger, GUESS, Dr. Scholl’s, Rowing Blazers, and Howler Bros. 

We’re excited about the opportunity ahead as we reimagine traditional approaches to online sales, fulfillment, marketing, accessing growth capital, providing a unified view of all ecommerce and marketing analytics, and other activities. Expectations for Cart.com are high, and we are building a company that can scale to $100B in revenue and beyond. Supported by the Startup Program by Google Cloud and Google Cloud solutions, we’re establishing a technology platform to transform all aspects of ecommerce for brands worldwide. 

Partner in disruption

At Cart.com, we’re currently targeting an underserved market. Our ideal customer is beyond demonstrating product-market-fit and is now at an inflection point seeking a growth opportunity. Typically, those companies are generating between $1M and $100M in annual revenue. We’ve seen an enthusiastic response from brands and retailers as well as investors, with backing from investors in just over a year totaling $143 million in three funding rounds.

Our strategy is to build an integrated ecommerce model that combines best-of-breed solutions, many of which we gain through acquisitions and then build upon to provide a streamlined and fully integrated experience for our brands. We’ve made seven acquisitions so far to round out our online store, order fulfillment, marketing services, customer service, and we have launched some integral partnerships including easy access to growth capital through our relationship with Clearco and product protection for customers on every purchase with Extend. Instead of acquiring a data company, we’re building our data platform on Google Cloud, across each operating function for a single-view for brands to harness actionable data. We see Google Cloud as the leader for data management, analytics, machine learning (ML) and artificial intelligence (AI).

Other reasons why we’re building our business on Google Cloud include scalability, excellence, security, reach, and data analytics that are far superior to other environments.

We also feel a cultural and mission alignment with Google Cloud and envision leaning into a long-term partnership of marketing, selling, and disrupting the disruptors together. Equally important to us are the investments Google Cloud is willing to make in early-stage companies like ours. The support through the Google Cloud for Startups program has been outstanding.

Built on Google Cloud

A wide range of Google Cloud solutions provide the foundation for our platform. For instance, Cloud Pub/Sub keeps our services communicating with one another. We rely on fully managed relational databases, like Cloud SQL and Cloud Spanner, to securely handle the huge volume of brand and shopper data generated every day.

Cloud Run allowed us to develop inside of containers before our Kubernetes infrastructure was ready to go. Now, we are taking advantage of all the capabilities in Google Kubernetes Engine. BigQuery integrates with all Google Cloud solutions and offers true data streaming natively out of the box, along with Dataflow for advanced analytics. We also use Container Registry to store and manage our Docker container images. Right now, we’re testing Cloud Composer to evaluate using it for data workflow orchestration instead of Apache Airflow.

The openness of the Google Cloud environment is further enabled by Anthos, which we may deploy soon to perform data integrations quickly as we acquire more companies over the next year. For example, if we acquire a company using Azure, we can easily align it with our Google Cloud ecosystem.

Enabling ecommerce 2.0

Recently, our team has been experimenting with Google Cloud Vertex AI and the fully managed services of AI deployment and ML operations. The capabilities would save us substantial time in the management of the ML lifecycle which allows us to focus more on developing proprietary AI that will transform commerce at scale.

Because Google Cloud is so far ahead in data science, our teams benefit from deep Google Cloud expertise as we look to provide brands with unmatched insights into customers to improve services and revenue. We’re also planning to test Recommendations AI among other tools to deploy customer product recommendations and personalization as turnkey productized offerings. Moving forward, we will likely use Bigtable to aid in serving machine learning to hundreds of thousands of brands due to its low latency and scalability.

Fanatical about brand success

We know that our work with Google Cloud for Startups and use of Google Cloud solutions for best-in-class data management, analytics, ML, and AI will enable us to offer even more transformative services to brands.

We also see the opportunity to use our platform and customer insights to break down barriers between brands, enabling retailers to share information and work better together when it’s in their best interests. What we’re building today on Google Cloud is fundamentally changing what’s possible for retailers of any size everywhere. 

As a startup, when recruiting talent or working with prospective customers, it helps to share our success with Google Cloud. We view them as an extension of the Cart.com team. It also validates our business as we continue building a more integrated, holistic approach to commerce that opens new opportunities and drives growth for brands worldwide.

For more details about Cart.com’s vision for unified ecommerce, check out our video.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

Apache Spark has become a popular platform as it can serve all of data engineering, data exploration, and machine learning use cases. However, Spark still requires the on-premises way of managing clusters and tuning infrastructure for each job. Also, end to end use cases require Spark to be used along with technologies like TensorFlow, and programming languages like SQL and Python. Today, these operate in silos, with Spark on unstructured data lakes, SQL on data warehouses, and TensorFlow in completely separate machine learning platforms. This increases costs, reduces agility, and makes governance extremely hard; prohibiting enterprises from making insights available to the right users at the right time.

Announcing Spark on Google Cloud, now serverless and integrated

We are excited to announce Spark on Google Cloud, bringing industry’s first autoscaling serverless Spark, seamlessly integrated with the best of Google Cloud and open source tools, so you can effortlessly power ETL, data science, and data analytics use cases at scale. Google Cloud has been running large scale business critical Spark workloads for enterprise customers for 6+ years, using open source Spark in Dataproc. Today, we are furthering our commitment by enabling customers to:

1. Eliminate time spent managing Spark clusters: With serverless Spark, users submit their Spark jobs, and let them do auto-provision, and autoscale to finish.
2. Enable data users of all levels: Connect, analyze, and execute Spark jobs from the interface of users’ choice including BigQuery, Vertex AI or Dataplex, in 2 clicks, without any custom integrations.
3. Retain flexibility of consumption: No one size fits all. Use Spark as serverless, deploy on Google Kubernetes Engine (GKE), or on compute clusters based on the requirements.

With Spark on Google Cloud, we are providing a way for customers to use Spark in a cloud native manner (serverless), and seamlessly with tools used by data engineers, data analysts, and data scientists for their use cases. These tools will help customers on their way to realize the data platform redesign they have embarked on.

“Deutsche Bank is using Spark for a variety of different use cases. Migrating to GCP and adopting Serverless Spark for Dataproc allows us to optimize our resource utilization and reduce manual effort so our engineering teams can focus on delivering data products for our business instead of managing infrastructure. At the same time we can retain the existing code base and knowhow of our engineers, thus boosting adoption and making the migration a seamless experience.”—Balaji Maragalla, Director Big Data Platform, Deutsche Bank

“We see serverless Spark playing a central role in our data strategy. Serverless Spark will provide an efficient, seamless solution for teams that aren’t familiar with big data technology or don’t need to bother with idiosyncrasies of Spark to solve their own processing needs. We’re excited about the serverless aspect of the offering, as well as the seamless integration with BigQuery, Vertex AI, Dataplex and other data services.” —Saral Jain, Director of Engineering, Infrastructure and Data, Snap Inc.

Dataproc Serverless for Spark

Per IDC, developers spend 40% time writing code, and 60% of the time tuning infrastructure and managing clusters. Furthermore, not all Spark developers are infrastructure experts, resulting in higher costs and productivity impact. With serverless Spark, developers can spend all their time on the code and logic. They do not need to manage clusters or tune infrastructure. They submit Spark jobs from their interface of choice, and processing is auto-scaled to match the needs of the job. Furthermore, while Spark users today pay for the time the infrastructure is running, with serverless Spark they only pay for the job duration.

Spark through BigQuery

BigQuery, the leading data warehouse, now provides a unified interface for data analysts to write SQL or PySpark. The code is executed using serverless Spark seamlessly, without the need for infrastructure provisioning. BigQuery has been the pioneer for serverless data warehousing, and now supports serverless Spark for Spark-based analytics.

Spark through Vertex AI

Data scientists no longer need to go through custom integrations to use Spark with their notebooks. Through Vertex AI Workbench, they can connect to Spark with a single click, and do interactive development. With Vertex AI, Spark can easily be used together with other ML frameworks like TensorFlow, Pytorch, Sci-kit learn, and BigQuery ML. All the Google Cloud security, compliance, and IAM are automatically applied across Vertex AI and Spark. Once you are ready to deploy the ML models, the notebook can be executed as a Spark job in Dataproc, and scheduled as part of Vertex AI Pipelines.

Spark through Dataplex

Dataplex is an intelligent data fabric that enables organizations to centrally manage, monitor, and govern their data across data lakes, data warehouses, and data marts with consistent controls, providing access to trusted data and powering analytics at scale. Now, you can use Spark on distributed data natively through Dataplex. Dataplex provides a collaborative analytics interface, with 1-click access to SparkSQL, Notebooks, or PySpark, and the ability to save, share, search notebooks and scripts alongside data.

Flexibility of consumption

We understand one size does not fit all. Spark is available for consumption in 3 different ways based on your specific needs. For customers standardizing on Kubernetes for infrastructure management, run Spark on Google Kubernetes Engine (GKE) to improve resource utilization and simplify infrastructure management. For customers looking for Hadoop style infrastructure management, run Spark on Google Compute Engine (GCE). For customers, who’re looking for no-ops Spark deployment, use serverless Spark! 

ESG Senior Analyst Mike Leone commented, “Google Cloud is making Spark easier to use and more accessible to a wide range of users through a single, integrated platform. The ability to run Spark in a serverless manner, and through BigQuery and Vertex AI will create significant productivity improvement for customers. Further, Google’s focus on security and governance makes this Spark portfolio useful to all enterprises as they continue migrating to the Cloud.”

Getting started

Dataproc Serverless for Spark will be Generally Available within a few weeks. BigQuery and Dataplex integration is in Private Preview. Vertex AI workbench is available in Public Preview, you can get started here. For all capabilities, you can request for Preview access through this form.

You can work with Google Cloud partners to get started as well.

“We are excited to partner with Google Cloud as we look to provide our joint customers with the latest innovations on Spark. We see Spark being used for a variety of analytics and ML use cases. Google is taking Spark a step further by making it serverless, and available through BigQuery, Vertex AI and Dataplex for a wide spectrum of users.” —Sharad Kumar, Cloud First data and AI Lead at Accenture

For more information, visit our website or the watch announcement video and our conversation with Snap at Next 2021.

Today, a typical enterprise uses over 100 SaaS applications while large organizations commonly use over 400 apps. These applications contain valuable data on customers, suppliers, employees, products and more, offering the potential for valuable insights and powerful workflows. However, in the past, this data has often remained siloed and underutilized, even within an application.

Customer demand is driving an unprecedented wave of innovation across the SaaS industry, with providers building intelligence into their applications through rich analytical and AI/ML capabilities, and enabling their customers’ data ecosystems through real time data sharing. The data cloud platform powering these applications is a critical factor in companies’ ability to innovate and optimize efficiency, while keeping its customer’s data secure.

Today, over 700 tech companies including Zoominfo, Equifax, Exabeam, Bloomreach, and Quantum Metric power their products and businesses using Google’s data cloud. This week at the Data Cloud Summit, we announced the Built with BigQuery initiative which helps ISVs get started building applications using data and machine learning products. By providing dedicated access to technology, expertise and go to market programs, this initiative allows tech companies to accelerate, optimize and amplify their success.

“Enabling customers to gain superior insights and intelligence from data is core to the ZoomInfo strategy,” ZoomInfo CEO Henry Schuck says. “We are excited about the innovation Google Cloud is bringing to market and how it is creating a differentiated ecosystem that allows customers to gain insights from their data securely, at scale, and without having to move data around. Working with the Built with BigQuery initiative team enables us to rapidly gain deep insight into the opportunities available and accelerate our speed to market.”

Building intelligent SaaS applications with a unified data platform

Google’s data cloud provides a complete platform for building data-driven applications, from simplified data ingestion, processing and storage to powerful analytics, AI/ML and data sharing capabilities, all seamlessly integrated with Google Cloud’s open, secure, sustainable platform. With a huge partner ecosystem and support for multicloud, open source tools and APIs, we provide technology companies the portability and extensibility they need to avoid data lock-in.

Through the Built with BigQuery initiative, we are helping tech companies to build the next-gen SaaS applications on Google’s data cloud with simplified access to technology, dedicated engineering support and joint go to market programs.

Exabeam, a next generation cybersecurity solution, is a great example. The company leverages Google’s data cloud to provide their customers with a “limitless-scale” cybersecurity solution.

“Built with Google’s data cloud, Exabeam’s limitless-scale cybersecurity platform helps enterprises respond to security threats faster and more accurately” said Sanjay Chaudhary, VP of Products at Exabeam. “We are able to ingest data from over 500 security vendors, convert unstructured data into security events, and create a common platform to store them in a cost effective way. The scale and power of Google’s data cloud enables our customers to search multi-year data and detect threats in seconds.”

Foster innovation and unlock new business models through data sharing

Data becomes even more valuable when shared. According to research, the global data monetization market size is growing at a CAGR of 47.9% and is projected to reach $11.7 Billion by 2026. It’s a compelling proposition for SaaS companies as it enables them to deliver increased value for their customers while expanding their own partner ecosystem, increasing stickiness and unlocking new revenue streams.

Google Cloud’s strategy for data sharing encompasses three key areas: secure data sharing in BigQuery, the ability to create private and public exchanges alongside commercial and public datasets in Analytics Hub, and a robust ecosystem of premier partner and Google data.

Through the real-time data sharing capabilities of BigQuery, SaaS companies are enabling their customers to combine their data with the customer’s own proprietary data, and other third-party data sources, to derive 360-degree insights. Over 4,500 organizations share more than 250 Petabytes of data weekly in BigQuery, not accounting for intra-organizational data sharing*.

For example, manufacturers can get real-time visibility into their entire supply chain by combining datasets from ISVs, such as supply chain innovator, Blume Global, and data publishers.

“Our partnership with Google Cloud is helping us achieve our mission to build the next-generation supply chain operating system. Blume Maps, our digital twin of the supply chain world built with Google’s data cloud, allows our customers to generate accurate lead times, real-time shipment location and ETAs” said Blume Global CEO Pervinder Johar. “We apply the power of Google Cloud’s data and analytics capabilities to our growing database of over 1.5 million global data points to feed our lead time and dynamic ETA engine. We are also able to create data twins of unique logistics data and share with users around the globe.”

Google Cloud’s Analytics Hub is a fully-managed service built on BigQuery that allows organizations to efficiently and securely exchange valuable data and analytics assets across any organizational boundary. With unique datasets that are always-synchronized and bi-directional sharing, you can create a rich and trusted data ecosystem between business units or partnerships–one in which everyone gains value immediately.

“As external data becomes more critical to organizations across industries, the need for a unified experience between data integration and analytics has never been more important. We are proud to be working with Google Cloud to power the launch of Analytics Hub, feeding hundreds of pre-engineered data pipelines from hundreds of external datasets,” said Dan Lynn, SVP Product at Crux. “The sharing capabilities that Analytics Hub delivers will significantly enhance the data mobility requirements of practitioners, and the Crux data integration platform stands ready to quickly integrate any external data source and deliver on behalf of Google Cloud and its clients.”

Innovate, optimize and amplify with Google Cloud

The Built with BigQuery initiative provides access to technology, expertise and go-to-market:.

• Access to Technology: Get started fast with a Google-funded, pre-configured sandbox. Access Cloud Credits to fund your, and your customer’s, POCs and apply for innovative pricing models designed for ISVs.
• Access to Expertise: Accelerate and optimize product design and architecture with access to designated experts in building data-driven SaaS applications from the ISV Center of Excellence, providing insight into key use cases, architectural patterns and best practices. Access experts from across Google to enable co-innovation with products such as Earth Engine and Google Marketing Platform.
• Access to Go-to-market: Amplify your success with joint marketing programs to drive awareness, generate demand and increase adoption.

Get Started

Start building your applications on Google Cloud with $300 in free credits or apply for the Built with BigQuery initiative.

*As of November, 2021, within a typical 7 day period in BigQuery, and not accounting for intra-organizational data sharing.

The music industry is rapidly changing. Only 20 years ago, the availability of music and the infrastructure that was required to make an album a sales success were incredibly complex and expensive. With the decline of physical sales and a fundamental shift to digital, music streaming now accounts for more than half of all sales globally.

At the same time, technology has democratized music-making; in many ways, it has made the musical landscape more diverse. Artists can upload their music with the click of a button. But while it’s easier for creators to share their songs with audiences, getting paid has become more fragmented.

Although music is booming, people no longer buy it outright. Instead, listeners download music digitally or subscribe to streaming services to have their libraries with them at all times. To monetize digital content effectively, artists need to know when, where, and how often their songs are played on each service. To help them navigate this complicated royalties landscape and maximize their profits, Berlin-based international music company BMG provides customized, transparent, and fair services to songwriters and artists.

With publishing and recording divisions under one roof, the subsidiary of international media giant Bertelsmann works with both emerging artists and established stars, including John Legend, Kylie Minogue, Mick Jagger, and Keith Richards. With the MyBMG web and mobile application, clients can view and analyze their royalty details in real time and collect payment. When a new record is released, BMG uses data to maximize its impact and revenue for its creators.

“We make sure that everyone who uses our clients’ music knows who needs to be paid the associated royalties, then we collect these royalties and share them out quickly and transparently,” explains Sebastian Hentzschel, Chief Information Officer at BMG. “When our artists release new music, we make sure that it’s marketed and promoted effectively around the world.”

“We needed a scalable solution for our royalty workloads that was intuitive for our developers. We also wanted a partner, not a client-vendor relationship. With autoscaling via BigQuery, excellent customer support, and a clean and simple user interface, Google Cloud ticks every box for us.”

—Gaurav Mittal, Vice President Group Technology, BMG

Getting up to speed with a new way of paying artists

In this digital world, artists aren’t just paid every time a fan buys an album—they’re paid a small amount, or royalty, for each song downloaded or streamed by a listener. So, when the industry shifted to digital, the volume of data that BMG needed to handle grew exponentially. “One CD sale is equivalent to about 1,500 streamed songs or plays,” says Gaurav Mittal, Vice President Group Technology at BMG. “That means IT departments have to process 1,500 times the amount of data to calculate payments for artists, and this makes scalable micropayment processing very important.”

Until 2019, BMG’s infrastructure was entirely hosted on-premises. Hardware limitations made it challenging to scale on-demand, making it harder to handle the data peaks that royalty processing can bring. “With our on-premises infrastructure, we were going to hit a ceiling in a few years,” says Gaurav. “We still managed to process royalty payments for our clients, but it was increasingly time consuming and expensive. To keep focusing on our clients, rather than our infrastructure, we decided to migrate to Google Cloud.”

From the outset, Gaurav and his team had a clear vision for the partnership: “Most importantly, we needed a scalable solution for our royalty workloads that was intuitive for our developers. We also wanted a partner, not a client-vendor relationship,” he says. “With autoscaling via BigQuery, excellent customer support, and a clean and simple user interface, Google Cloud ticks every box for us.”

Keeping artists happy with business-as-usual payouts during migration

To move applications to the cloud while keeping payment cycles on track for its artists, BMG teamed up with Google Cloud partner Rackspace Technology. “We selected Rackspace Technology because it combines strong technical muscle and a global footprint, with the customer service of a local boutique firm,“ shares Gaurav.

BMG’s own technology team put together the outline for the Google Cloud architecture, which they passed on to Rackspace Technology for optimizations and the ultimate stamp of approval. Whenever Gaurav and his developers needed support, Rackspace Technology was ready to go. “So far, we’ve migrated 17 applications successfully, and Rackspace Technology has been 100% spot-on with each suggestion,” says Gaurav. “I can’t recall a single flaw in a Rackspace Technology-approved architecture, and that really says something.”

When BMG began its migration in August 2019, the team developed an ambitious two-year plan. Only 14 months later, however, the project is more than 75% complete. Among the solutions that BMG is using today are Cloud Storage to securely store 130 TB of data, and Cloud SQL as its standard database technology. The web applications run on Compute Engine, App Engine, and Google Kubernetes Engine.

“After successfully moving a few applications, it was clear that with the strong teamwork of BMG and Rackspace Technology, together with the ease of use of Google Cloud, we could speed up the project without sacrificing quality,” says Gaurav. “We’re set to complete our migration six months before schedule, helping us to quickly move out of our hybrid environment.”

“Our income-tracking teams are very savvy on the data, and the simplicity of Google Cloud empowers them to self-serve analytics, rather than wait for IT. Our teams are much more productive.”

—Gaurav Mittal, Vice President Group Technology at BMG

Royalty reporting and processing with BigQuery and Dataproc

So far, all of BMG’s critical workloads are up and running on Google Cloud. Royalty calculations, for example, which require incredible processing power and the collection of many micropayments to ensure full and timely payout, run entirely on Dataproc with output stored on BigQuery for downstream integration and reporting.

Enabling more harmonious workflows through self-serve analytics

As the new beating heart of BMG’s royalty reporting, BigQuery changed the rhythm of collaboration company-wide. In the past, income tracking teams had to contact IT departments if they needed deeper data insights for their work. By integrating Data Catalog with BigQuery, BMG has made the data more accessible to all teams. This helps them detect missing income and new revenue streams independently, maximizing profits for artists.

“Our income-tracking teams are very savvy on the data, and the simplicity of Google Cloud empowers them to self-serve analytics, rather than wait for IT,” says Gaurav. “Our teams are much more productive.”

“Google Cloud enables us to be more client focused and deliver better features faster. We believe it’s just the beginning. We offer rights and royalty services for music publishing, recorded music, neighboring rights, and books. Without scalability limitations, it’s absolutely conceivable to offer our platform as a service to other companies or industries, such as gaming. Google Cloud has opened a world of possibilities.”

—Sebastian Hentzschel, Chief Information Officer, BMG

With a leaner IT environment, BMG can focus its effort on the needs of its clients. Beyond improvements in royalty processing, it can concentrate on app development, releasing new features and enhancements more frequently. By hosting applications on Google Kubernetes Engine, App Engine, and Compute Engine, BMG has built a CI/CD pipeline with automated deployments and testing to significantly speed up workflows.

“In our old system, it could take several weeks to set up an environment,” says Gaurav. “With Google Kubernetes Engine, any of our developers can complete the process in a few clicks. Having that autonomy makes our developers more motivated and self-driven.”

“Google Cloud enables us to be more client focused and deliver better features faster,” adds Sebastian. “We believe it’s just the beginning. We offer rights and royalty services for music publishing, recorded music, neighboring rights, and books. Without scalability limitations, it’s absolutely conceivable to offer our platform as a service to other companies or industries, such as gaming. Google Cloud has opened a world of possibilities.”

With the migration almost complete, BMG is looking forward to its next technology project. It plans to leverage AutoML to further scale and automate royalty tracking with machine learning. On the marketing side, advanced analytics will help BMG determine the effectiveness of promotional campaigns around the world, further increasing profits for artists. By connecting Google Data Studio to BigQuery, BMG will increase the quality of its analyses, helping musicians better understand the reach of their music around the world.

In the end, Gaurav shares, helping musicians is what it all comes down to. “We’re a new kind of music company because we build our services around our artist, songwriter, and publisher clients, not the other way around,” he says. “Google Cloud is helping us maintain strong relationships with our clients, and that’s music to our ears.”