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.”

Some of the most important data at your company isn’t living in databases, but in documents, and most business processes begin, involve or end with a document. 

Yet most companies are still manually entering data and reliant on guesswork to make sense of it all as the volume and variety of data explodes. Organizations are also leaving heaps of value on the table in the form of new and better customer experiences that can be unlocked with artificial intelligence (AI) applied to documents. 

The latest releases of Document (Doc) AI platform, Lending DocAI and Procurement DocAI, built on decades of AI innovation at Google, bring powerful and useful solutions to these challenges. Under the hood are Google’s industry-leading technologies:

• Computer vision (including OCR) and Natural Language Processing (NLP) that creates pre-trained models for high-value, high-volume documents. 
• Google Knowledge Graph to validate and enhance the fields in your documents.
• Training and creation of your own custom document models. 
• Human interaction with AI to ensure accuracy where needed.

Google Cloud DocAI platform, Lending DocAI and Procurement DocAI are now generally available. Thousands of customers have tried these products in the preview phase—and DocAI has already processed tens of billions of pages of documents across lending, insurance, government and other industries.

“The vast majority of enterprise content still resides in unstructured sources like documents. Google Cloud’s Document AI brings a fresh new perspective to the problem informed by the company’s decades of experience making sense of the largest unstructured corpus in the world—the world wide web.” —Ritu Jyoti, VP of AI Research, IDC

Cut document processing costs by up to 60%

Lending DocAI helps banks, mortgage brokers and other lending institutions fast track the loan application process from weeks to days, dramatically reducing the cost of issuing a loan. And Procurement DocAI enables companies to automate procurement data capture at scale, lowering processing costs by up to 60%.

These solutions are built on DocAI platform, a unified console for document processing that lets you quickly access all parsers and tools. From the platform, you can automate and validate documents to streamline workflows, reduce guesswork, and keep data accurate and compliant. 

Get more value from AI with DocAI’s industry-specific solutions

According to Accenture’s AI: Built to Scale report: “Companies that scale successfully see 3x the return on their AI investments compared to those who have not fully rolled out AI capabilities.” 

Core to our strategy at Google Cloud is the creation of industry-specific solutions that help companies get maximum value out of their investments in AI. We announced Lending DocAI, our first solution designed specifically for the financial services industry, at the Mortgage Bankers Association convention last year. It processes borrowers’ income and asset documents using a set of specialized machine learning (ML) models, and automates routine document reviews so that mortgage providers can focus on more important work. 

Lending DocAI is now generally available and includes more specialized parsers for critical loan documents including paystubs, bank statements, and more. Our goal is to provide the right tools to help borrowers and lenders have a better experience and close home loans faster. For more, watch this video.

Procurement DocAI is also now generally available. This solution helps companies accelerate document processing for invoices, receipts, and other valuable documents in the procurement cycle. 

Automating data capture is helping our customers increase accuracy and also lower their procure-to-pay processing costs. We are continually expanding the types of documents Procurement DocAI can process—the latest is a utility parser for electric, water and other bills. In addition, Procurement DocAI leverages Google Knowledge Graph to validate and enrich parsed information to make the data even more useful. Check out this overview video for more details. 

One company that lives and breathes AI-enabled document management is AODocs. It uses Procurement DocAI to simplify invoice processing for enterprise customers and launched a new Gmail add-on, Invoice to Sheet, for SMB customers who just want to track their invoices in Google Sheets.

“Google Cloud’s Procurement DocAI service allows our document management platform to better automate the processing of invoices; AODocs customers who have tested our new account payables workflow estimate that the productivity of their A/P team has more than doubled, thanks to the reduction of manual data input brought by the Procurement DocAI.”—Stéphan Donzé, Founder and CEO, AODocs

The new specialized parsers for Lending and Procurement DocAI can be used alongside our existing AutoML Text & Document Classification and AutoML Document Extraction services. These technologies provide a state-of-the-art toolset for creating new document models and have been widely deployed by customers in financial services and other industries. 

Partner to accelerate your AI deployment and results

Having the right partner to ease the complexity of rolling out your AI-strategy in mortgage document processing is critical to transforming your customers’ experience. We’re excited to announce a partnership with Mr. Cooper, a leader in mortgage servicing, to provide customers with more automation and workflow tools throughout their entire mortgage life cycle. As part of this agreement, both companies will collaborate on digitizing Mr. Cooper’s core mortgage platform, creating a more personal customer experience utilizing AI, and driving a broader culture of innovation to imagine and develop services and solutions that will transform the mortgage experience for American homeowners.

“Over the last few years, we have made substantial investments in our servicing technology and core mortgage platform that have revolutionized the customer experience, while providing dramatic efficiencies in operating cost. Our partnership with Google Cloud AI will build on those advances and help make these technologies available for the mortgage industry.” —Jay Bray, Chairman and CEO, Mr. Cooper Group

This builds upon the robust partner ecosystem we’re creating to help customers revolutionize the home loan experience, which includes last year’s partnership announcement with Roostify.

Integrate human review into ML predictions 

Next up is the general availability of Human-in-the-Loop AI, a new DocAI feature that will help companies achieve higher document processing accuracy with the assurance of human review. Adding human review can increase accuracy and help businesses interpret predictions using purpose-built tools to enable those reviews. 

Processing documents quickly and cost-effectively is important. But it’s often necessary to have a high level of assurance on data accuracy for compliance. CIOs and IT decision-makers need highly accurate ML predictions to fulfill compliance requirements, improve employee experience (e.g. less rework), and raise customer satisfaction (e.g. fewer data errors). Including human participation in ML processes allows AI and humans to work together for the best possible results.

Human-in-the-Loop AI provides the workflow to manage human review tasks and produces a percentage confidence score of how “sure” it is that the AI ingested the document correctly. Document AI extracts data from documents with ML, and when paired with Human-in-the-Loop AI, human reviewers are able to verify the data captured. This system is customizable, providing the flexibility to set different thresholds and assign individual groups of reviewers to various stages of the workflow. With Human-in-the-Loop AI, developers can choose trusted reviewers to assign to the task; these reviewers can be from within their own or partner organizations.

More Document AI resources

To learn more, check out the Document AI webpage and watch a demo of how to process sample forms in AI Platform notebooks to inspect data extraction and confidence scores. For more on how customers and partners like Workday, AODocs, and Mr. Cooper are using Document AI, listen to our fireside chat. And stay tuned for the exciting evolution of these technologies in future releases of DocAI.

It’s 2022 and nanosatellites, NFTs, and autonomous cars that deliver your pizza are in full force. In a world where people rely on simple technology to untangle complex problems, companies must deliver simple experiences to be successful in today’s landscape. For many cloud providers this means enabling tightly integrated data offerings that simplify the data delivery process without losing sight of the sophisticated needs of the modern data consumer. 

But while the name of the game is helping companies reach informed decisions from their data simpler and faster, what about the data practitioners – data analysts, data engineers, database administrators, developers, etc – who use these cloud data tools and technologies everyday? To proactively stay ahead of data cloud market trends in 2022 should data practitioners invest their time in specializing their data cloud skill sets (e.g. go deep in, say, data pipelining skills) or instead invest their time generalizing their data cloud skill sets (e.g. growing proficiencies in a mix of data analytics, databases, AI/ML, and more domains)? 

Skill deep or wide with data – that is the question

For Abdul Razack, VP, Solutions Engineering, Technology Solutions and Strategy at Google Cloud, the answer is a bit of both. 

“Data practitioners need to be broad in terms of their technology skills, but specialized with respect to the domain or domains in which they apply them. The reason why is because many things that used to be separate skill sets are now converging – like business analytics, streaming, machine learning, data pipelines, and data warehousing. Data practitioners need to be able to implement end-to-end workflows that solve specific business problems using skills from each category.”

It’s true, thousands of customers are choosing Google’s data cloud because it offers a unified and open approach to cloud that enables their practitioners to break down silos, begin and end projects without leaving the data platform, and innovate faster across their organization. 

The data practitioners who mirror Google data cloud’s frame of mind of being smart and agile across data domains in their skilling and learning will reap the benefits of solving more nuanced problems – building out internet-scale applications, fine tuning smart processes with analytics and AI, constructing data meshes that make product building simple, etc – at a larger scale than they would if they specialized in just one or two areas alone.

“Of course at the end of the day it depends on what tools a data practitioner is using to complete their workflows. There’s only so much you can learn and skills you can develop when you’re using limited tools. Growing data proficiencies across the board is made a lot easier when you’re using a data platform like BigQuery to address all these needs. BigQuery eliminates the choices you have to make – for instance you don’t have to choose between streaming data and data at rest, batch and realtime, or business intelligence and data science. This freedom gives data professionals a huge advantage when they’re building their skill sets and taking on more complex projects.” -Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud

Knowing your value is half the battle when upskilling

While some experts think technology is the limiting factor of whether or not you can even go wide or go deep in the first place, others like Google Cloud’s Head of Data and Analytics Bruno Aziza purport that it also depends on who you are, who you wish to be, and what investments your company is making to ensure you can become that person. 

“If you wish to set yourself up to be a Chief Data Officer, then you’ll want to understand how technologies fit together across your data estate first” said Aziza. “Only after you feel like you’re the go-to ‘data person’ can you then decide which part of the technology stack you want to double-down on.”

But technology isn’t everything. Aziza notes, “Make sure you focus on the  business impact that your data work provides.  You want to spend as much time as you can with your business counterparts to understand their business goals and challenges. The Harvard Business Review provides great guidance on how to succeed as a Chief Data Officer.”

Even if you don’t have your sights set on a C-suite role, both Aziza and Razack contend that the number one skill data practitioners should tackle in 2022 is actually a broad and perhaps abstract one: develop and exercise the curiosity to solve problems with a data-driven strategy. 

That is, today’s data practitioners should always be interested in educating themselves in the industry and continually upskilling in something. And their employers should also be invested in helping practitioners develop those interests, most likely through exposure to learning materials, engaging in career conversations, subsidized courses, or incentives attached to pursuing a new certification or skill.  

“Every industry is going through a digital transformation and the ability to identify what data to collect, how to prepare the data, and how to derive insights from it is critical. Therefore, the ability to find business challenges and formulate a data-driven approach to address those problems is the most important skill to have.” Abdul Razack – VP, Solutions Engineering, Technology Solutions and Strategy, Google Cloud.

Take the example of the “Data Mesh” I just wrote about in VentureBeat.  You’ll find 3 types of attitudes towards this new concept. There are Disciples who encourage continued learning only from the source – like the author of a new book or the creator of a theory. There are Distractors who tell you that new skills, trends, and technologies are fake news. And there are Distorters like vendors who will sell you one easy fix solution. But it’s the data practitioner who needs to proceed with caution when interacting with all three types  and forge their own path to discovering the truth when they’re learning and building skills. And for better or worse, this comes with trial and error, experimentation, and an eagerness to grow relative to where they began.”

Ready to start data upskilling? Start here.

For those interested in keeping up their data curiosities, check out our Data Journeys video series. Each week Bruno Aziza investigates a new authentic customer’s data journey – from migrating to cloud or  building a data platform to carrying out new data for good initiatives. Learn how they did it, their data dos and don’ts, and what’s next for them on their journey. These videos include a flavor of both specializing your data competencies and broadening your data competencies. 

For those interested in deepskilling, connect with Google’s data community at our upcoming virtual event: Latest Google Cloud data analytics innovations. Register and save your spot now to get your data questions answered live by GCP’s top data leaders and watch demos from our latest products and features including BigQuery, Dataproc, Dataplex, Dataflow, and more.

If you have any questions or need support along your learning journey – we’re here for you! Sign up to be a Google Cloud Innovator, and join the Google Cloud Data Analytics Community.