As we enter the holiday season, retailers are working behind the scenes to ensure they can provide the best experiences for customers, in store and online. Challenges in retail do not begin or end during the holiday season as sudden shifts in customer preferences, supply chain nuances, and overall demand ebbs and flows take place year round and retailers must be prepared to adapt swiftly.

Google Cloud’s retail customers globally, in total, saw more online traffic in the first six months of 2022 than all of 2019. This year, retailers can expect an early launch to holiday shopping activities, as 50% of consumers plan to start purchasing goods before the traditional Black Friday kick-off.

The very same improvements made to automate and improve retail infrastructure can prepare it for holiday surges and support year-round innovation. Let’s take a look at how Ulta Beauty, the largest beauty retailer in the U.S., is partnering with Google Cloud, MongoDB Atlas, commercetools, and HCLTech to cover these two areas and more.

Architecting for innovation

Creating personalized shopping experiences in stores and online is key to Ulta Beauty’s success. This commitment is best demonstrated through Ulta Beauty’s Virtual Beauty Advisor. Built on Google Cloud, this tool enhances shoppers’ experiences with personalized recommendations in addition to the ability to try on makeup virtually with GLAMLab.

As innovators in support of the best possible guest experience, Ulta Beauty needed to re-architect its infrastructure for greater agility and stability.

To start, Ulta Beauty chose to use Google Kubernetes Engine (GKE) as the backbone and orchestrator to build and deploy cloud-native applications. The Google Cloud deployments coincided with an organizational move from end-to-end application development to one that focuses on individual features, specific modules, and micro-applications.

This strategic change allowed Ulta Beauty to fix bugs, experiment with new offerings, and drive customer experiences faster and more efficiently. Thanks to the transformation and GKE, Ulta Beauty’s developer team now accelerates time to market for new products and services, and delivers new ways to engage with customers more quickly. These efforts all ladder to create ‘WOW’ experiences for the retailers’ guests who have emotional and personal connections to beauty and wellness. They can now discover and experience products that are served to them based on individual preferences.

Adapting to the new environment comes with its own set of challenges. “Microservices are not a silver bullet,” says Sethu Madhav Vure, IT Architect, Ulta Beauty. “For Ulta Beauty, the biggest challenge was how to break up a monolithic environment into multiple applications. We had to evolve our core systems—without impacting today’s services—and address what was needed for the future.”

Google Cloud partner HCLTech provided expert guidance throughout the re-architecting process, defining the solution blueprint and cloud-native deployment architecture through cross-functional workshops. HCLTech then assisted with the actual migration and platform setup, paving the way for fully automated, continuous integration and continuous delivery (CI/CD) pipelines to support faster rollouts and deployment architecture to drive higher availability and scalability.

Ulta Beauty took a domain-driven design approach to identify operations that could be grouped together to reduce complexity and improve scalability. Now, the applications are based on multiple domains, such as Commerce, Promotions, Catalog, Order, Customer, and Inventory. The new architecture prompted a fresh look at storage requirements to scale dynamically alongside its modernized applications.

For Ulta Beauty, MongoDB Atlas proved to be the best database solution for dynamic scaling, ease-of-use, and integrations with Google Cloud. The company also leveraged an entry-level plan to prove the value of MongoDB Atlas before investing in the technology.

“MongoDB Atlas offers a free tier that gave us an opportunity to quickly demonstrate tangible benefits of a proof of concept,” says Vure. “Once we proved the value of MongoDB Atlas, we benefited from the straightforward resource allocation supported by Google Cloud and MongoDB.”

Integrations between MongoDB Atlas and Google Cloud allow Ulta Beauty to take an iterative approach to new projects. The company creates new clusters in an existing project, then piggybacks them onto an existing Private Service Connect setup between a MongoDB project and Google Cloud project.

By removing complexities within infrastructure management, Ulta Beauty can manage its incredible amount of data, such as member preferences and purchases, that fuels its event-driven architecture. The much more agile infrastructure enables Ulta Beauty to deploy and scale offerings faster than ever.

“We recently had an unplanned traffic surge that impacted our domain services. It took less than an hour for MongoDB Atlas to scale up to the next level of the cluster and manage that traffic,” says Vure. “The on-demand, dynamic scaling, plus GKE, has saved the day more than once.”

Preparing for a happy holiday season

This holiday season, Ulta Beauty has a stronger technical foundation to manage demand surges and provide customers seamless shopping experiences. Previously, the company used 50 pods in a cluster, each with 6 GB of RAM without domain stores, to handle about 100 transactions each second. With domain stores, the same 6 GB of RAM with just 20 GKE pods was able to scale up to 2,400 transactions per second.

With Google Cloud as its technology foundation, Ulta Beauty partnered with Google Cloud partner commercetools to evolve its application APIs as products and properly separate interfaces and capabilities.

Ulta Beauty uses event-based integrations within commercetools to identify how best to leverage Cloud Pub/Sub middleware on top of MongoDB Atlas integrations. Patterns established here were extended into MongoDB change streams and in turn improved business processes.

“Working with the right technology partners has helped us to avoid analysis paralysis that can happen when developer teams spend a lot of time trying to understand and manage every detail,” says Vure. “Instead, we convert a proof of concept into a working solution, and quickly bring it to market. It’s been a major shift in our IT culture as we try out new things weekly and see incredible support from leadership.”

The improvements enable Ulta Beauty to maintain a high level of innovation, performance, and customer service year-round. Now, when the holiday shopping season begins, Ulta Beauty is prepared to handle surges in traffic through auto-scaling with Google Cloud and MongoDB Atlas. Customers get what they want, when they want, free from the frustrations of outages.

“With these changes, we are ready for a holiday season that everyone–even those of us in IT—gets to enjoy. We’re positioned to continuously focus on new, better ways to serve our guests,” says Vure.

Check out MongoDB and commercetools on Google Cloud Marketplace to learn more about what these partners can do for your business.

May was a very busy month for data analytics product innovation. If you didn’t have the chance to attend our inaugural Data Cloud Summit, video replays of all our sessions are now available so feel free to watch them at your own pace. 

In this blog, I’d like to share some background behind the innovations we released in May, why we built them the way we did, and the type of value they can bring your company and your team.

But first, a huge thank you!

This week, we had the honor to announce that Google has been named a Leader in The Forrester Wave™: Streaming Analytics, Q2 2021 report. Forrester gave Dataflow a score of 5 out of 5 across 12 different criteria, stating: “Google Cloud Dataflow has strengths in data sequencing, advanced analytics, performance, and high-availability”. 

Google has more than a decade of experience in building real-time and internet-scale systems for its own needs, and we are excited to see that our ability to provide customers with a reliable, scalable, and performant platform is bearing fruit. 

This announcement comes on the back of the release of The Forrester Wave™: Cloud Data Warehouse, Q1 2021 report, which also named Google Cloud as a Leader.

We couldn’t be more excited about the recognition and appreciate all your feedback and trust in the work that we do to support your goal in accelerating data-powered innovation.

Innovation galore

Your feedback and your passion is the fuel that drives our ambition to deliver more and better services to you. That’s why, this year, we didn’t want to wait until Google Cloud Next to share some great products we have been working on. On May 26, our team announced a slew of new products, services and programs. Watch a quick summary below:

https://youtube.com/watch?v=DG1mOPMXJvw%3Fenablejsapi%3D1%26

Meeting you where you are

An important design principle behind all of our services is “meeting you where you are”. This means we aim to provide you with the tools and software you need to innovate on your own terms. Here are three new services that will help you do just that:

Datastream

Datastream, our new serverless change data capture (CDC) and replication service, allows your company to synchronize data across heterogeneous databases, storage systems, and applications reliably and with minimal latency to support real-time analytics, database replication, and event-driven architectures. Datastream delivers change streams from Oracle and MySQL databases into Google Cloud services such as BigQuery, Cloud SQL, Cloud Storage, and Cloud Spanner, saving time and resources while ensuring your data is accurate and up-to-date. 

• Under the hood, Datastream reads CDC events (inserts, updates, and deletes) from source databases, and writes those events with minimal latency to a data destination. It leverages the fact that each database source has its own CDC log—binlog for MySQL and LogMiner for Oracle—which it uses for its own internal replication and consistency purposes. 
• Datastream integrates with purpose-built and extensible Dataflow templates to pull the change streams written to Cloud Storage, and create up-to-date replicated tables in BigQuery for analytics. It also leverages Dataflow templates to replicate and synchronize databases into Cloud SQL or Cloud Spanner for database migrations and hybrid cloud configurations. 
• Datastream also powers a Google-native Oracle connector in Cloud Data Fusion’s new replication feature for easy ETL/ELT pipelining. By delivering change streams directly into Cloud Storage, customers can leverage Datastream to implement modern, event-driven architectures.

Looker and BigQuery Omni on Microsoft Azure

Research on multi cloud adoption is unequivocal — 92% of businesses in 2021 report having a multi cloud strategy. We want to continue supporting your choice by providing the flexibility you need to see your strategy through. 

• This past month, we introduced Looker, hosted on Microsoft Azure. For the first time, you can now choose Azure, Google Cloud, or AWS for your Looker instance. You can also self-host your Looker instance on-premises.
• We also introduced BigQuery Omni for Azure, which along with last year’s introduction of BigQuery Omni for AWS, will help you access and securely analyze data across Google Cloud, AWS, and Azure. 

The cost of moving data between cloud providers isn’t sustainable for many, and it’s still difficult to seamlessly work across clouds. BigQuery Omni represents a new way of analyzing data stored in multiple public clouds, which is made possible by BigQuery’s separation of compute and storage. By decoupling these two, BigQuery provides scalable storage that can reside in Google Cloud or other public clouds, and stateless resilient compute that executes standard SQL queries. 

• Unlike competitors, BigQuery Omni doesn’t require you to move or copy your data from one public cloud to another, where you might incur egress costs. You also benefit from the same BigQuery interface on Google Cloud, enabling you to query data stored in Google Cloud, AWS, and Azure without any cross-cloud movement or copies of data. 
• BigQuery Omni’s query engine runs the necessary compute on clusters in the same region where your data resides. For example, you can query Google Analytics 360 Ads data stored in Google Cloud and query logs data from your ecommerce platform and applications that are stored in AWS S3 and/or Microsoft Azure. 

Then, using Looker, you can build a dashboard that allows you to visualize your audience behavior and purchases alongside your advertising spend. 

Dataplex

We understand that most organizations still struggle to make high-quality data easily discoverable and accessible for analytics, across multiple silos, to a growing number of people and tools within their organization. 

They are often forced to make tradeoffs. For instance, moving and duplicating data across silos to enable diverse analytics use cases or leaving their data distributed but limiting the agility of decisions. 

• Dataplex provides an intelligent data fabric that enables you to centrally manage, monitor, and govern your data across data lakes, data warehouses, and data marts, while also ensuring data is securely accessible to a variety of analytics and data science tools. 
• One of the core tenets of Dataplex is letting you organize and manage your data in a way that makes sense for your business, without data movement or duplication. For that, we provide logical constructs like lakes, data zones, and assets. These constructs enable you to abstract away the underlying storage systems and become the foundation for setting policies around data access, security, lifecycle management, and so on. 
• For example, you can create a lake per department within your organization (e.g. Retail, Sales, Finance, etc.) and create data zones that map to data readiness and usage (e.g. landing, raw, curated_data_analytics, curated_data_science, etc.). 

Once you have your lakes and zones setup, you can attach data to these zones as assets. You can add data from different types of storage (e.g. GCS Bucket and BigQuery dataset) under the same zone. You can also attach data across multiple projects under the same zone. You can ingest data into your lakes and zones using the tools of your choice, including services such as Dataflow, Data Fusion, Dataproc, Pub/Sub, or choose from one of our partner products. Dataplex comes with built-in 1-click templates for common data management tasks. 
To find out more about Dataplex, head to cloud.google.com/dataplex or watch the video below:

https://youtube.com/watch?v=bbFeAt7cw1g%3Fenablejsapi%3D1%26

Helping you innovate everyday

Sharing data is hard. Traditional data sharing techniques use batch data pipelines that are expensive to run, create late arriving data, and can break with any changes to the source data. These techniques also create multiple copies of data, which brings unnecessary costs and can bypass data governance processes. They also fail to offer features for data monetization, such as managing subscriptions and entitlements. Altogether, these challenges mean that organizations are unable to realize the full potential of transforming their business with shared data.

Analytics Hub

To address these limitations, we are introducing Analytics Hub, a new fully managed service that helps organizations unlock the value of data sharing, leading to new insights and increased business value. 

This new service is built on the tremendous experience and feedback we have received over the years. For example, BigQuery has had cross-organizational, in-place data sharing capabilities since its inception in 2010—and the functionality is very popular. Over a 7-day period in April, we had over 3,000 different organizations sharing over 200 petabytes of data. These numbers don’t include data sharing between departments within the same organization.

Analytics Hub takes sharing to the next level, making it easy for you to publish, discover, and subscribe to valuable datasets that you can combine with your own data to derive unique insights. 

This includes: 

• Shared datasets: As a data publisher, you create shared datasets that contain the views of data that you want to deliver to your subscribers. Data subscribers can search through the datasets that are available across all exchanges for which they have access and subscribe to relevant datasets. In addition, the publisher can track subscribers, disable subscriptions, and see aggregated usage information for the shared data.
• Curated, self-service data exchanges: Exchanges are collections used to organize and secure shared datasets. By default, exchanges are completely private, but granular roles and permissions make it easy to deliver data to the right audience—whether internal or public. 

This is just the beginning for Analytics Hub. Please sign up for the preview, which is scheduled to be available in the third quarter of 2021.

Dataflow Prime

At Google Cloud, we have the great privilege of working with some of the most innovative organizations in the world. And this work provides us with a unique perspective into the future of big data processing. Dataflow Prime is a new platform based on a serverless, no-ops, and auto-tuning architecture that brings unparalleled resource utilization and radical operational simplicity to big data processing. This new service introduces a large number of exciting capabilities but I’d like to highlight three key aspects of the product:

• Vertical Autoscaling: Dataflow Prime dynamically adjusts the compute capacity allocated to each worker based on utilization, detecting when jobs are limited by worker resources and automatically adding more resources. Vertical Autoscaling works hand in hand with Horizontal Autoscaling to seamlessly scale workers to best fit the needs of the pipeline. As a result, it no longer takes hours or days to determine the perfect worker configuration to maximize utilization. 
• Right Fitting: Each stage of a pipeline typically has a different resource requirement than the others. Until now, either all workers in the pipeline would have had the higher memory and GPU, or none of them would. Pipelines either had to waste resources or suffer slower workloads. Right Fitting solves this problem by creating stage-specific pools of resources, optimized for each stage. 
• Smart Recommendations: Smart Recommendations automatically detects problems in your pipeline and shows potential fixes. For example, if your pipeline is running into permissions issues, a Smart Recommendation will detect which IAM permissions you need to enable to unblock your job. If you are using an inefficient coder in your job, Smart Recommendations will surface more performant coder implementations that can help you save on costs.

What’s next

We’re excited to hear your thoughts and feedback about all these exciting new services. I would also highly recommend that you connect with members of the community to learn more about their story and journey. A good example to start with is the Data To Value customer panel we produced at our inaugural Data Cloud Summit with the Chief Data Officers of Keybank and Rackspace. You can watch it for free below:

https://youtube.com/watch?v=ITI2Q3MkxuA%3Fenablejsapi%3D1%26

Built for a CapEx world, most organizations’ finance systems aren’t set up to take advantage of cloud’s dynamic, OpEx-driven consumption patterns.

It may not be a household name yet, but chances are you’ve crossed paths with OpenX today. OpenX, a leader in programmatic advertising, operates one of the world’s largest ad exchanges, serving over 250 billion ad requests per day, connecting more than 30,000 brands and reaching nearly one billion consumers. To make it happen, in 2019, OpenX migrated entirely out of its data centers and became the first major ad-exchange platform to move completely to the cloud.

The OpenX CTO, Paul Ryan, knew that this cloud transformation initiative had the potential to increase costs faster than its revenues. To be successful, he needed his engineering, finance, and business teams to forge a new “cost-aware” culture, complete with effective cost visibility and controls. In other words, he needed Cloud FinOps — an operational framework and cultural shift that brings technology, finance, and business together to drive financial accountability and realize business benefits through cloud transformation.

Ryan laid out a cloud migration roadmap that included cost governance and controls around project ownership, established cost responsibility with engineering teams to accurately forecast cloud consumption, and challenged developers to lower per-unit costs — while at the same time improving performance, scalability, speed and global reach.

It worked! In just 9 months, OpenX reduced their per-unit cost by over 60%. The framework allowed OpenX to launch new regions in a matter of days, reduce their time to market for new features by over 50%, and complete their migration in record time — seven months! “We are now able to stop worrying about legacy infrastructure and focus more on our growth categories,” said Ryan. “Our tech stack is getting smarter and more sophisticated by the day, and we have the flexibility to scale our infrastructure in real-time as the business scales and evolves.”

Unblocking Cloud’s Potential

Cloud holds the key to a successful digital transformation. In fact, McKinsey forecasts that by 2030, the Fortune 500 alone may realize over $1 trillion of EBITDA value drivers associated with public cloud enablement. But unlike OpenX, many companies struggle to achieve near-term value objectives from their cloud investments. Surveys reflect that more than 30% of cloud spend in 2021 was wasted or inefficient, while upwards of 80% of CIOs have yet to achieve the business benefits of migrating to the cloud.

Traditional IT finance processes are ill-suited for cloud infrastructure: Traditional planning and budgeting processes are challenged to address dynamic consumption patterns and complex migrations. Centralized IT budgets using traditional allocations fail to provide the necessary visibility into sources of cost overruns. CapEx-focused cost controls have little ability to manage largely OpEx-driven spend. Trend-based forecasting often inaccurately predicts cloud costs. And developer teams lack access to cost-aware architecture patterns to deploy the applications more efficiently.

Enter Cloud FinOps

At Google we’ve worked with many companies, like OpenX, to help organizations realize the transformational benefits of the cloud by cultivating a culture of transparency and embedding agile processes to manage costs. We’ve distilled these learnings into a Cloud FinOps operational framework that gives organizations the financial governance and accountability they need to grow their business sustainably.

At a high level, a Cloud FinOps approach depends on five key areas:

1. Accountability and Enablement

Accountability and enablement aim at instilling a cost-conscious culture across the organization. Oftentimes, this means standing up a cross-functional and dedicated team with members from technology, finance and engineering to establish cloud financial best practices and governance. In various organizations, we’ve seen this through an extension of a Cloud Center of Excellence, a Cloud Business Office or simply a Cloud FinOps team. Enablement focuses on empowering IT, finance and business leaders through training to help them better understand the economics of cloud services and the strategies to efficiently deploy and manage them. Cloud financial training guides teams on how to design cost-effective cloud environments, for example, embracing ”cloud-native” design principles such as auto-scaling/elasticity and Infrastructure as a Code.

2. Measurement and Business Value Realization

Effective measurements not only create awareness and enable agile processes, but also support a culture that celebrates success and rewards teams for achieving business objectives. As such, measurement in the service of business value realization is about developing a comprehensive set of long-term benefits and cost KPIs to quantify the total net value of the return on digital transformation. Organizations often start with cost-related KPIs and eventually evolve those KPIs into business value metrics that are mapped to targeted business outcomes.

3. Cloud Cost Optimization

Cloud cost optimization is an iterative and continuous process that provides a consistent methodology to manage cloud consumption cost-effectively. There are three key areas of optimization:

Resource optimization – Model cost-effective cloud usage based on utilization and consumption patterns.
Pricing optimization – Manage cloud spend through a continuous analysis of various pricing models. In a Google Cloud context, that might mean Committed Use Discounts, BigQuery flat rate reservations, etc.
Architecture optimization – Build applications with a cost-aware architecture by leveraging newer generation compute instances (like Tau VMs, which offer an industry-leading 42% better price-performance versus comparable offerings), or using managed services and serverless technology to offload operational overhead.
For example, video hosting, sharing and services platform provider Vimeo built transcoding pipelines by using Google Cloud Spot VMs to optimize their infrastructure spend. To do so, they created fault-tolerant workloads that could withstand preemptions, and in exchange, got up to a 91% discount compared to using regular on-demand instances.

4. Planning and Forecasting

In the cloud, accurately forecasting your finances requires rethinking of traditional approaches to depreciation and trend-based forecasting of maintenance and licensing costs. One way to improve the accuracy of your dynamic cloud needs is to use workload-specific forecasting models that leverage a combination of trend-based models for steady-state workloads, driver-based models for scaling applications, as well as monthly variance analysis. In other words, you can define cloud budgets and forecasts by monitoring cloud consumption trends, allocating cloud cost pools with a proper tagging strategy that’s mapped to a chart of accounts in a general ledger, and conducting a cost-benefit analysis based on cloud infrastructure, implementation, and support costs.

5. Tools and Accelerators

Without the proper tools and processes in place, understanding and managing cloud costs can be complex — and this especially true as organizations scale their business in the cloud. By deploying proper cloud cost management tools and accelerators such as Looker Cloud Cost Management and automation scripts to set guardrails and enforce cost control policies, organizations can effectively manage and track cloud spend with access to near-real-time billing and cost data to make better informed business decisions.

The key objectives of Google Cloud Cost Management tools are to make it as simple as possible for organizations to get visibility into their current and forecasted costs with built-in reporting and customizable dashboards; help drive greater accountability for cloud spending across the organization by providing flexible ways to organize cloud resources and allocate costs; provide strong financial governance controls to reduce the risk of overspending; and offer intelligent recommendations for optimizing cloud costs and usage.

Start Saving with Cloud

Businesses are continuously seeking to better operate and manage their cloud environments and the need is ever increasing to transparently manage cloud spend, optimize costs, and obtain their desired business agility. By enhancing your Cloud FinOps capabilities and adopting principles of continuous cost optimization, you too can accelerate the business value of cloud computing.

Special thanks to Bruce Warner, Daniel Petibone, Nihar Jhawar and FinOps Foundation community for their contributions and sharing their domain expertise to this important Cloud FinOps topic.

The COVID-19 pandemic brought dramatic changes to the financial services industry. Already under pressure from nimble young fintechs to modernize, established banks and insurers were undergoing incremental digital transformation. But in 2020, they hit the gas pedal. Branches closed and remote work became the norm. Almost overnight, employees needed secure remote access to corporate systems, and customers expected to be able to complete even complex transactions whenever they wanted, on whatever device. Even as things return to normal, many of these shifts are likely to be permanent. According to Forrester Research, nearly 90% of global financial services CIOs and SVPs believe that improving their application portfolio is key to improving customer experience and driving revenue.¹ The problem? Replacing legacy systems with cloud-based SaaS enterprise software is a massive, time- and resource-intensive process. 

IDG’s recently completed white paper, Financial Services Spotlight: Elevating agility and security in the cloud, highlights an alternative to the all-or-nothing approach to replatforming: “lifting and shifting” on-premises applications and workloads to the cloud without rewriting them. In this way, you keep your organization’s familiar architecture, but give it the scalability and cutting-edge technology of a modern cloud environment. That’s the promise of Google Cloud VMware Engine brings to the financial services industry.

Here’s a quick overview of the insights that the IDG study uncovers. Download the complete white paper.

Simpler migration, rich rewards

Google Cloud VMware Engine helps financial services companies seamlessly migrate and run 

VMware workloads natively on Google Cloud. Once in the cloud, firms can take advantage of Google Cloud services, access a robust third-party cloud ecosystem, and use the same VMware tools, processes, and policies their teams already know. The IDG study found that migrating to Google Cloud with Google Cloud VMware Engine offers multiple benefits:

1. Create new customer experiences. Migrating to Google Cloud puts modern, cloud-native architectures and technologies — such as containers and microservices — easily within reach. These make it possible for financial institutions to quickly and securely launch new applications and update them on a continuous basis using DevOps pipelines. They also allow firms to craft more personalized customer experiences across channels using Google Cloud’s native AI and data analytics.
2. Deliver new services. After migrating, financial services organizations can connect to multiple third-party service providers via cloud-based APIs to bring new, diverse services to their customers — without having to build from scratch. 
3. Make the best use of IT resources. When your data and applications reside in Google Cloud, you’re no longer constrained by the physical storage and compute limits of on-premises infrastructure. This means your company can match capacity to demand — even during unexpected peaks. You also gain more visibility into your hybrid cloud environment with Google Cloud’s operations suite, which offers intelligent analysis and easier troubleshooting for your platform and applications.
4. Gain fresh insights. The key to understanding what customers need and when they need it resides within your data, and data analytics in the cloud help you uncover those insights. Your company can connect to Google Cloud’s serverless data warehouse, BigQuery, which leverages data to deliver valuable insights for personalized customer experiences, rich compliance reporting, new product development, intelligent fraud detection, and more. 
5. Choose what to move. Data governance regulations and requirements specific to the financial services industry mean that some data must remain on premises. Google Cloud VMware Engine lets you easily manage a hybrid cloud/on-premises environment to keep sensitive data fully under your control.
6. Become more resilient. Google Cloud VMware Engine gives financial services firms a distributed architecture and centralized control for their applications to support vital business continuity functions, such as backup and disaster recovery. This is on top of the performance and availability of Google Cloud’s global infrastructure.
7. Improve security. Using cloud-native application frameworks, administrators can issue patches and software updates centrally and automatically across their organizations. This reduces the risk of errors and security vulnerabilities. Firms also tap into the security features and capabilities of Google Cloud, including always-on encryption and AI-powered threat detection.
8. Redirect IT resources. Migrating virtualized workloads to the cloud can free up talent and budget to develop new products and services — time that was previously spent on maintaining complex on-premises infrastructure. That means less effort spent keeping the lights on, and more resources directed toward creating innovative and differentiating customer experiences.

IDG research concluded that migrating business applications to Google Cloud with Google Cloud VMware Engine can help financial services companies stay ahead of change without incurring further technical debt from their legacy IT systems. Working with cloud-based systems can give your financial services company much of the scale, speed, and agility of a startup while still enjoying the benefits of being an established organization.

Read the complete white paper to learn more about the ways in which Google and VMware work together to accelerate digital transformation for financial services firms.

^{1. Vmware-forrester-financial-services-modern-app-report.pdf, A commissioned study conducted by Forrester Consulting on behalf of VMware, 2020}