Google Cloud Results

• Improves application performance by 8x to 12x; customers can create increasingly complex optimized driving routes in single-digit seconds
• Improves customer satisfaction via increased reliability and greater application performance
• Focuses on adding value to customers by improving software and algorithms, not infrastructure management
• Saves 5x in infrastructure costs

In 2009, Dan Khasis needed to rent an apartment. His search had him driving around the greater New York City area in unfamiliar areas, scattershot-style, often ending up where he started. The frustrating experience led the serial entrepreneur to launch Route4Me, a smartphone navigation app to help consumers create driving routes optimized for multiple stops.

Soon, business users recognized Route4Me’s value and requested enhancements specifically for them. While route optimization apps for big businesses already existed, they were almost exclusively offline desktop programs that were expensive to purchase, deploy, and get trained on. Recognizing the opportunity, Route4Me developed an affordable route optimization solution across various devices, such as smartphones, smartwatches, and telematics devices. The software was tailored to logistics-intensive businesses such as last-mile delivery services and business units conducting field sales, field service, and field marketing functions.

As Route4Me grew its user base, it became clear that its infrastructure of rented, dedicated servers from various providers wasn’t sustainable. “The hardware costs seemed low, but there were many risks and hidden costs,” says Dan Khasis, Co-founder and CEO at Route4Me. For example, “Multi-zone disaster recovery, high availability, automated failover, and on-demand surging of many nodes was simply impossible,“ he adds.

Because under the hood Route4Me’s routing optimization platform requires complex computations, the company needed a globally scalable infrastructure capable of delivering low latency and high throughput. Route4Me also needed to stay competitive by developing and delivering new services as quickly and efficiently as possible.

For these and other reasons, Route4Me moved 100% into the cloud. “Like many entrepreneurial software companies, we test all the latest technologies we can find before upgrading. Typically we go with the fastest technology, with a strong bias towards open source and open standards,” Khasis says. Based on extensive testing, Route4Me selected Google Cloud Platform (GCP). Along with the scalability, flexibility, reliability, and low-cost structure of GCP, Route4Me had already migrated its entire platform to containerized microservices, which Khasis says “are extremely stable and reliable” on Google Kubernetes Engine. While Route4Me has proprietary routing and route optimization engines, it uses Google Maps for high-precision geocoding and as the frontend.

With GCP, Route4Me has reduced its IT infrastructure costs while delivering faster route optimizations and more reliable service to customers. Because of GCP, the company is also planning to add services that will deliver the fastest possible routing simulations and calculations to customers at a price that Khasis says is “impossible without a mature cloud-based platform like GCP.”

Unexpected savings, pleasant surprises

The migration to GCP and Kubernetes Engine required Route4Me to revamp its Service-Oriented Architecture (SOA) and convert millions of lines of code into containerized microservices running on Kubernetes Engine. With more than 150 microservices and thousands of add-on modules and features offered on the Route4Me platform, the migration took several months. But the transition, which began in May 2017 and concluded toward year’s end, went smoothly. “Thanks to the reliability and open source portability of Google Kubernetes Engine, Route4Me experienced one-tenth of the problems that we’ve had when onboarding to other cloud providers,” says Khasis.

Halfway into the migration, Route4Me engineers discovered an unexpected cost savings. The ability to run preemptible virtual machine (VM) instances with Kubernetes Engine resulted in a 90% savings in infrastructure costs, according to Khasis.

The engineering team was also pleasantly surprised by the improved intra-system latency and performance between the Google network and those of third-party systems and other data centers that Route4Me connects to. Overall latency dropped from 8x to 12x. “Where it used to take 8 to 14 seconds to plan a complicated route, now it takes as little as 2 seconds,” Khasis says. Route4Me is also running most of its transactional and operational data through Google BigQuery for a variety of business use cases, including complex machine learning tasks such as geospatial analytics, geospatial pattern detection, and synthetic density.

Scaling while delivering great performance

Route4Me algorithms take into account such data as driving distance, driving time, who’s driving, the day of the week, the vehicle being used, weather conditions, and dozens of other attributes. “All those scenarios and data have to be run in near real time,” Khasis explains. The Route4Me system must access multiple internal and external databases, aggregate all the information in parallel, and deliver it using a high-speed infrastructure platform.

“Our core services and algorithms work much faster on a Google architecture, bringing the total time to solve a complex route problem down to single-digit seconds.” “Many of those steps are resource-intensive,” Khasis adds. “With Kubernetes Engine clusters, we can do much more, scaling up and down as needed, and still deliver great performance to customers around the world.”

Because of its scale, Route4Me built its own automation system for marketing, support, and communications with its customers. “Since we moved our proprietary marketing automation system to GCP, we began delivering our omni-channel marketing communications more reliably, and the correct message reached customers faster and at just the right moment,” says Khasis. “That’s translated to happier customers and increased revenue.”

Customer satisfaction has increased, too, because Route4Me’s users experience far fewer slowdowns than before due to the reliability of GCP. The reliability also means the company spends less time worrying about certain clusters or servers going down for extended periods of time. “We have zero sysadmins, which was the Achilles heel of some of my previous startups,” says Khasis. “So we can focus on software development rather than infrastructure management.”

In order to scale as needed and develop new features, Khasis had expected the company would need to hire more SysAdmin, DevOps, and SecOps staff. “But once we migrated to the modern GCP environment, we didn’t have to make those hires. We saved a lot of money by not having to hire, train, and manage more people,” explains Khasis.

Flexible GCP pricing, in which customers only pay for what they use, has saved Route4Me money on its IT infrastructure. “Preemptible server pricing on GCP is so aggressive,” Khasis says. “If servers are automatically shut off for a certain time period, we don’t pay for them for that period. And if servers are on for a certain amount of time, we get an automatic 30% discount. We’re saving money on the platform with fixed and dynamic workloads.”

Per-second billing with GCP also helps Route4Me cut costs. “If it only takes 25 seconds to do something, we only pay for those 25 seconds,” Khasis says. For the same 25 seconds, other cloud providers might charge for 10 minutes usage or even an hour.”

Road map for the future

In the coming year, Route4Me plans to offer additional add-ons as part of its self-service marketplace, providing customers with transparent pricing on highly complex route optimizations. The service will be extremely valuable to heavy users. For instance, if an organization has to visit 50,000 locations by a certain time, it might wonder if it needs to add 20 people to make that happen and how much it’s going to cost. “Because we’re on GCP, our customer can run a variety of complicated routing scenarios to see which one is the most efficient in seconds instead of minutes,” says Khasis. “As far as I know, none of our competitors can offer that kind of service, giving us an edge as well as a new revenue stream.”

Going forward, Route4Me will begin migrating a huge portion of its core routing optimization platform to Google Google Cloud Spanner. “We want to take further advantage of Cloud Spanner, which comes closest to the CAP theorem and permits us to operate an infinitely scalable and nearly indestructible platform,” Khasis says.

As one example, Route4Me receives telematics data, such as GPS coordinates, from Internet of Things (IoT) devices in smartphones and vehicles, and performs complex algorithmic analysis running on Cloud Spanner. This provides real-time return on investment (ROI) information, so customers can see how much money they’re saving by using Route4Me routing optimization services.

“In order to help as many logistics-intensive businesses as possible, we intend to migrate our proprietary mapping, routing, and route optimization services to Cloud Spanner to take advantage of its extreme reliability and redundancy, and the multi-availability zones of Google Cloud Platform,” says Khasis.

Route4Me also plans to leverage Google machine learning technology, in part to make its routing solution available for use in autonomous and drone vehicles, as well as decentralized edge computing deployments. In addition, Google security and encryption technology will help the company expand its offerings to the heavily regulated medical industry.

Over 60 Route4Me team members use G Suite for almost everything. ”We’re interested in using everything possible with G Suite. We get inspiration from G Suite, too. A lot of thinking and effort went into improving G Suite, and we use that as inspiration to improve own products.”

Google Cloud’s partnership with Sky Group, one of Europe’s largest media and entertainment companies, dates back more than four years to when Sky first became a Google Cloud customer moving diagnostic data from millions of its Sky Q TV boxes to its Google Cloud data platform.

In June 2019, a few years into their cloud adoption journey, Sky was faced with a challenge they had anticipated from the start. Their recent bill across all major cloud providers had been increasing rapidly, reaching their planned yearly budget after only six months. Sky wasn’t sure if they’d undershot their forecasts, if they were overspending, or both.

“In the beginning, we were given a brief to investigate internal cloud spend with the aim of finding out where we could make savings, but in reality we didn’t know what we would expect to find,” said Nathan King, a cloud architect in the Cloud Enablement Center and now Head of Cloud Financial Management (FinOps) at Sky since the start of 2020.

Nathan assembled a small team who started to explore Google Cloud spend using the Cloud Billing tool. At first, they drilled into their biggest Google Cloud cost categories and discovered some immediate cost optimizations with BigQuery, Compute Engine and Cloud Storage. Over the course of the next six months, through careful analysis, they managed to find over $1.5m in immediate savings, exceeding expectations.

Yet they soon realized this was just the tip of the iceberg—it was clear there were millions of pounds more savings to be made, but actually achieving them at scale would require careful planning. “We formed a FinOps function to target these savings, but with 600 to 700 projects for Google Cloud alone, spanning four Google Cloud organizations, it would have been a manual process and difficult for teams to digest our recommendations,” Nathan said.

After attending a Google-led FinOps workshop and shaping their FinOps strategy, Nathan’s team focused on iterating through the FinOps lifecycle phases of Inform, Optimize, Operate and generating savings over time. Here’s how they did it:

Inform: Make Information Visible

The first step was focused on developing a clear vision for cost allocation and recharge, which required partnering closely with the finance, procurement and tax teams (particularly for international and affiliates) to understand the supporting business logic and processes. With a lot of hard work, the team managed to break down barriers to implement and embed new processes into broader business functions like finance.

WIth the recharge model in place, the team ran a number of pilots to find the right FinOps tooling to meet their needs. They ran a number of pilots, including using Data Studio and visualizing BigQuery exports. Given their ambitions to scale across the enterprise globally, the team chose Google Cloud’s Looker to realize their vision, building intuitive dashboards to visualize spend and recommendations across all cloud providers. “We wanted one view across all clouds, where customers can dynamically see cloud spend and intelligent optimization recommendations in just one place,” Nathan said.

After less than three weeks of development, the Looker dashboards were ready to go and have been a game changer ever since. “The moment our leadership and different departments started seeing the Looker dashboards, the value we were adding as a FinOps team became immediately clear,” Nathan said.

There are different report pages for each stakeholder group, each custom developed and automated using Looker and BigQuery. The BigQuery Optimization page, for example, provides insights on Slots consumed across the organization, down to granular query data like the cost of each query, how it was written, who submitted it and number of slots utilized. The dashboards also highlight potential areas of optimization, like BigQuery datasets without retention policies set or where data isn’t partitioned.

A recent breakthrough has been building pages for business teams, showing the related cloud spend contributing to a business unit of value, such as the cost per live stream or per subscriber in Sky’s case. Although this is an inherently difficult metric to capture, the opportunity has been made possible with the FinOps team’s progress and is starting to drive business investment decisions.

Optimize: Drive Cloud Efficiency

The second stage of the FinOps lifecycle focuses on delivering optimizations. As Sky’s FinOps dashboards were operationalized and highlighted savings opportunities, they enabled users to generate more than $3 million in Google Cloud savings alone in 2020 and over $800,000 in other cloud providers.

The team began with focusing on the top four products by spend: BigQuery, Compute Engine, Cloud Dataflow and Cloud Storage. Working with their Google account team and studying Google whitepapers and blog posts like Cloud cost optimization: principles for lasting success, they developed their own best practice guidance and embedded recommendations into the dashboards.

Creating their own recommenders and leveraging Google Cloud’s recommenders, the team discovered a plethora of cost optimization opportunities. “Key examples were overly expensive queries, storage buckets set without retention policies, and VMs without autoscaling enabled,” Nathan said. Teams were then empowered to make their own savings, like the NowTV business unit that had been forecast to overspend for the year until they received their dashboard with thousands of optimization recommendations. After just three weeks, the team had implemented more than 90% of recommendations and brought their spend under budget for the year, saving more than 50%.

The FinOps team still searches for new recommendations every day and have been collaborating with Google product managers to take their insights to the next level. “We’ve loved partnering with Google product managers, who encourage us to give feedback on new features before they go to market. We’ve also shared some of our in-house recommenders to influence the features being developed by Google, including the Idle VM and Idle Persistent Disk Recommenders as part of Active Assist,” Nathan said.

Operate: Embed FinOps & Drive Self-Sufficiency

Now that teams could visualize their cloud spend and make real-time decisions based on cost optimization recommendations, the FinOps team has begun working on embedding processes, leveraging machine learning, and improving efficiency in their own ways of working.

Looker’s extensive capabilities continue to play a role in this. “Before we started using Looker, our most popular report was an electricity bill showing customers’ detailed monthly cloud spend, previous month comparisons and forecasts for months ahead,” Nathan said. “This report took days, sometimes weeks to run. With Looker, we’ve automated the entire process and brought that time down to just minutes.”

More teams are embedding the dashboards into their own processes, like finance, which now uses the interactive dashboards in meetings instead of static report snapshots, or in-house Google Cloud architects, who use the recommendations to optimize their cloud spend before deploying any technology.

As the FinOps team continues to operate like a product function, designing with CX/UX in mind and iteratively releasing new features like anomaly reporting, budget alerts, and forecasting based on machine learning, it’s becoming clear that Cloud Financial Management is a key capability and mindset that can impact wide-reaching parts of the business at scale.

Elevating Sky’s FinOps journey to the next level
Indeed, as more business teams collaborate with the FinOps function, the opportunities are growing. “The FinOps team has changed the way we view and manage cloud spend, enabling us to partner with finance and show digestible reports to the CFO. We’re now looking further to broaden our range of insights, like elevating our dashboards to understand how using Google Cloud is supporting Sky’s Net carbon zero ambitions by incorporating Google’s data center sustainability metrics,” says Vince Marco, Architecture Manager at Sky.

So, after being unsure of drivers for their increasing cloud spend in 2019, 18 months later Sky is far more confident about its investment decisions. The team knows that every dollar spent is being used optimally and driving maximum value for its investment.

If you’re an enterprise using cloud, but want to better manage cloud costs, consider setting up a FinOps capability and creating a FinOps mindset. Looker can help you get started by providing reporting and insights into cloud expenditures to identify initial savings. As you learn more and scale, empower teams to make their own savings utilizing built-in actionality for monitoring and customizing for business billing activity nuances and department-specific chargebacks. Reimagine how cloud finances can be managed and optimized as Sky is doing.

To learn more about Looker’s Cloud Cost Management Block visit Looker Marketplace.

The financial services industry is evolving at a rapid pace, with shifting consumer expectations, new technologies, and developing regulatory requirements. Financial services firms need the right technology to help them stay agile and prepare for the future. 

The cloud is a key point of leverage for firms looking to improve performance across a broad range of activities. Moving to the public cloud can advance operational resiliency, improve staff productivity, increase regulatory compliance and enhance business model innovation. 

However, there are a number of financial services companies that are still hesitant in their cloud journeys. The barriers to adoption vary, from the complexity of legacy systems, to trust and skills gaps, regulatory uncertainty, and fragmentation of compliance requirements. Although many companies have embraced the benefits of cloud technology, more robust cloud adoption—especially around core back-office functions—will require additional facilitation, including through regulatory harmonization and streamlining.

A new comprehensive study on cloud adoption in financial services

To better understand the challenges and opportunities of cloud adoption in financial services Google Cloud, together with the Harris Poll, surveyed more than 1,300 leaders from the financial services industry across the United States, Canada, France, Germany, United Kingdom, Hong Kong, Japan, Singapore and Australia. 

There were five noteworthy takeaways from the study:  

1. A vast majority of financial services companies are already using some form of public cloud. A large number of surveyed financial services companies (83%) report they are deploying cloud technology as part of their primary computing infrastructures. Of those using cloud technology, the most popular architecture of choice is hybrid cloud (38%), followed by single cloud (28%), and multicloud (17%). Notably, of respondents without a multicloud deployment, 88% reported they are considering adopting a multicloud strategy in the next 12 months.

2. Financial services institutions in North America are leading in cloud adoption. Of the financial services companies who are implementing a cloud strategy, the highest levels of cloud workload adoption were reported in North America, with institutions in the U.S. (54%) and Canada (52%) leading the way. The lowest level of cloud adoption was reported in Japan (42%).

3. As financial services companies continue to use the cloud, more core functionalities can and will be migrated. While many financial services companies have migrated substantial workloads to the cloud, the industry is far from full adoption when it comes to core, back-office workloads. Of financial services companies currently using a majority cloud strategy in the United States, for example, only half (54%) of their workloads are fully deployed in the cloud. Data and IT security (74%), regulatory reporting (57%), and fraud detection and prevention (57%) rank among the highest workload adoption. Core underwriting activity (40%) and data reconciliation (48%) ranked lowest. Across Europe, cloud usage for core activities like underwriting also scored low with the UK listing only 30% adoption.

4. Among respondents, there is a very strong positive perception of the potential for cloud technology to assist in business operations and regulatory compliance. Nearly all respondents (>88%) agreed that cloud adoption can:

1. help adapt to changing customer behaviors and expectations,  
2. enhance operational resilience, 
3. support the creation of innovative new products and services, 
4. enhance financial services institutions’ data security capabilities, and 
5. better connect siloed legacy software infrastructure within financial services institutions. 

5. Certain regulator-induced challenges, including the complexity of sectorial compliance frameworks and fragmentation, create hurdles to cloud adoption for financial services companies. While 88% of respondents had a positive view of current regulatory efforts to provide guidance and clarity for cloud implementation, the results showed that more needs to be done to facilitate adoption. Most respondents (84%) agree that regulatory reviews and approvals take too long because of regulatory fragmentation across regulatory bodies. And 78% say that regulatory uncertainty over the use of public cloud prevents their organizations from adopting cloud technologies that would otherwise provide benefit to them. Additionally, a third of all on-premises respondents (38%) say that the large investment of resources for the regulatory approval process is a reason why they’re not using cloud services.

“While many banks have already deployed hybrid cloud environments, others are still in various stages of planning and deploying,” said Jerry Silva, research vice president for IDC Financial Insights. “Clearly, hybrid infrastructure is a reality, and financial institutions must focus not only on leveraging the modern infrastructure model to gain efficiencies, resilience and agility, but also on taking the necessary steps to manage such environments, including the security and compliance of cloud services.”

Future recommendations for financial services regulators

Financial services firms should continue to maximize the potential of technology by migrating more core workloads to the cloud, and actively considering multicloud and hybrid-cloud strategies. Such strategies enhance resiliency of existing IT infrastructure and reduce concerns over vendor lock-in. 

The research also points to steps that regulators could take to provide additional clarity and guidance, such as aligning regulatory reviews across agencies to avoid fragmentation; developing regulatory “safe harbors” for cloud adopters based on adherence to accepted standards and best practices; training regulatory staff on emerging tech; and advancing data reporting requirements via cloud and related technologies.

In the past few years, many regulators across the globe have taken a robust approach to rationalizing rules and guidance to cloud adoption in the financial sector, which has helped significantly stimulate adoption. But further assurances and harmonization of best practices around supervision is needed to advance risk-based and secure digital innovation.  

At Google Cloud, we’re committed to working with financial services customers and regulators to provide them with controls and assurances on risk management, data locality, transparency, and compliance. We are constantly engaging with regulators to share information, respond to their considerations and concerns, and address questions in the interest of transparency and building trust. 

To learn more about these findings and more, download our infographic and our full report. 

^{Research methodology}

^{The survey was conducted online by the Harris Poll on behalf of Google Cloud, from December 7, 2020, to January 4, 2021, among 1,363 senior executives in France (n=113), Germany (n=178), the UK (n=192), Hong Kong (n=99), Indonesia (n=100), Japan (n=142), Singapore (n=71), Australia (n=134), Canada (134), and the United States (n=200) who are employed full-time, part-time, or self-employed whose main functional role is in risk/compliance or IT at a company in the banking, finance, or financial services industry with a title of director level or higher. The data in each country were weighted by the number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}