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.”

The Home Depot, Inc., is the world’s largest home improvement retailer with annual revenue of over $151B. Delighting our customers—whether do-it-yourselfers or professionals—by providing the home improvement products, services, and equipment rentals they need, when they need them, is key to our success.

We operate more than 2,300 stores throughout the United States, Canada, and Mexico. We also have a substantial online presence through HomeDepot.com, which is one of the largest e-commerce platforms in the world in terms of revenue. The site has experienced significant growth both in traffic and revenue since the onset of Covid-19.

Because many of our customers shop at both our brick-and-mortar stores and online, we’ve embarked on a multi-year strategy to offer a shopping experience that seamlessly bridges the physical and digital worlds. To maximize value for the increasing number of online shoppers, we’ve shifted our focus from event marketing to personalized marketing, as we found it to be far more effective in improving the customer experience throughout the sales journey. This led to changing our approach to marketing content, email communications, product recommendations, and the overall website experience.

Challenge: launching a modern marketing strategy using legacy IT

For personalized marketing to be successful, we had to improve our ability to recognize a customer at the point of transaction so we could—among other things—suspend irrelevant and unnecessary advertising. Most of us have experienced the annoyance of receiving ads for something we’ve already purchased, which can degrade our perception of the brand itself. While many online retailers can identify 100% of their customer transactions due to the rich information captured during checkout, most of our transactions flow through physical stores, making this a more difficult problem to solve.

Our old legacy IT system, which ran in an on-premises data center and leveraged Hadoop, also challenged us since maintaining both the hardware and software stack required significant resources. When that system was built, personalized marketing was not a priority, so it took several days to process customer transaction data and several weeks to roll out any system changes. Further, managing and maintaining the large Hadoop cluster base presented its own set of issues in terms of quality control and reliability, as did keeping up with open-source community updates for each data processing layer.

Adopting a hybrid approach

As we worked through the challenges of our legacy system, we started thinking about what we wanted our future system to look like. Like many companies, we began with a “build vs. buy” analysis. We looked at several products on the market and determined that while each of them had their strengths, none was able to offer the complete set of features we needed.

Our project team didn’t think it made sense to build a solution from scratch, nor did we have access to the third-party data we needed. After much consideration, we decided to adopt a solution that combined a complete rewrite of the legacy system with the support of a partner to help with the customer transaction matching process.

Building the foundation on Google Cloud

We chose Google Cloud’s data platform, specifically BigQuery, Dataflow, DataProc, Cloud Storage, and Cloud Composer. Google Cloud platform empowered us to break down data silos and unify each stage of the data lifecycle from ingestion, storage, and processing to analysis and insights. Google Cloud offered best-in-class integration with open-source standards and provided the portability and extensibility we needed to make our hybrid solution work well. The open standards of BigQuery’s BQ Storage API allowed us to leverage fast BQ storage layers to be utilized with other compute platforms, e.g., DataProc.

We used BigQuery combined with Dataflow to integrate our first- and third-party data into an enterprise data and analytics data lake architecture. The system then combined previously siloed data and used BigQuery ML to create complete customer profiles spanning the entire shopping experience, both in-store and online.

Understanding the customer journey with the help of Dataflow and BigQuery

The process of developing customer profiles involves aggregating a number of first- and third-party data sources to create a 360-degree view of the customer based on both their history and intent. It starts with creating a single historical customer profile through data aggregation, deduplication, and enrichment. We used several vendors to help with customer resolution and NCOA (Change of Address) updates, which allows the profile to be house-holded and transactions to be properly reconciled to both the individual and the household. This output is then matched to different customer signals to help create an understanding of where the customer is in their journey—and how we can help.

The initial implementation used Google Dataflow, Google’s streaming analytics solution, to load data from Google Cloud Storage into BigQuery and perform all necessary transformations. The Dataflow process was converted into BQML (BigQuery Machine Learning) since this significantly reduced costs and increased visibility into data jobs. We used Google Cloud Composer, a fully managed workflow orchestration service, to help orchestrate all data operations and DataProc and Google Kubernetes Engine to enable special case data integration so we could quickly pivot and test new campaigns. The architecture diagram below shows the overall structure of our solution.

Taking full advantage of cloud-native technology

In our initial migration to Google Cloud, we moved most of our legacy processes in their original form. However, we quickly learned that this approach didn’t take full advantage of the cloud-native and more improved features Google Cloud offered such as auto scaling of resources, flexibility to decouple storage from the compute layer, and a wide variety of options to choose the best tool for the job. We refactored our Hadoop-based data pipelines written in Java-based Map Reduce and our Pig Latin jobs to Dataflow and BigQuery jobs. This dramatically reduced processing time and made our data pipeline code concise and efficient.

Previously, our legacy system processes ran longer than intended, and data was not used efficiently. Optimizing our code to be cloud-native and leveraging all the capabilities of Google Cloud services resulted in reduced run times. We decreased our data processing window from 3 days to 24 hours, improved resource usage by dramatically reducing the amount of compute we used to possess this data, and built a more streamlined system. This in turn reduced cloud costs and provided better insight. For example, DataFlow offers powerful native features to monitor data pipelines, enabling us to be more agile.

Leveraging the flexibility and speed of the cloud to improve outcomes

Today, using a continuous integration/continuous delivery (CI/CD) approach, we can deploy multiple system changes each week to further improve our ability to recognize in-store transactions. Leveraging the combined capabilities of various Google Cloud systems—BigQuery, DataFlow, Cloud Composer, Dataproc, and Cloud Storage–we drastically increased our ability to recognize transactions and can now connect over 75% of all transactions to an existing household. Further, the flexible Google Cloud environment coupled with our cloud-native application makes our team more nimble and better able to respond to emerging problems or new opportunities.

Increased speed has led to better outcomes in our ability to match transactions across all sales channels to a customer and thereby improve their experience. Before moving to Google Cloud, it took 48 to 72 hours to match customers to their transactions, but now we can do it in less than 24 hours.

Making marketing more personal—and more efficient

The ability to quickly match customers to transactions has huge implications for our downstream marketing efforts in terms of both cost and effectiveness. By knowing what a customer has purchased, we can turn off ads for products they’ve already bought or offer ads for things that support what they’ve bought recently. This helps us use our marketing dollars much more efficiently and offer an improved customer experience.

Additionally, we can now apply the analytical models developed using BQML and Vertex AI to sort customers into audiences. This allows us to more quickly identify a customer’s current project, such as remodeling a kitchen or finishing a basement, and then personalize their journey by offering them information on products and services that matter most at a given point through our various marketing channels. This provides customers with a more relevant and customized shopping journey that mirrors their individual needs.

Protecting a customer’s privacy

With this ability to better understand our customers, we also have the responsibility to ensure we have good oversight and maintain their data privacy. Google’s cloud solutions provide us the security needed to help protect our customers’ data, while also being flexible enough to allow us to support state and federal regulations, like the California Customer Privacy Act. This way we can provide a customer the personalized experience they desire without having to fear how their data is being used.

With flexible Google Cloud technology in place, The Home Depot is well positioned to compete in an industry where customers have many choices. By putting our customers’ needs first, we can stay top of mind whenever the next project comes up.