Transforming Canadian Healthcare and Medical Research with Google Cloud
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Is Cloud an option for Canadian Healthcare healthcare and medical research organizations?
Yes, Canadian healthcare and medical research organizations are moving to the cloud. The cloud market is expected to grow in Canada significantly through 2027.
There are several reasons why Canadian healthcare and medical research organizations are moving to the cloud.
- Reduce costs by eliminating the need to invest in and maintain on-premises infrastructure.
- Enable the healthcare research community to drive their research more expediently to clinical outcomes
- Improve patient satisfaction by making it easier for patients to access their health information and communicate with their providers.
- Improve the quality of care by providing access to patient data and records from anywhere in the country.
Overall, the transition to the cloud is a positive development for Canadian healthcare and medical research organizations.
Canadian healthcare providers face many challenges before they can move to the cloud, such as addressing security and privacy concerns, data sovereignty issues, and ensuring interoperability. To help them overcome these challenges, it is important to provide Healthcare Data Custodians, Infrastructure Architects, and Research Leads with clear guidance on how the cloud can align with Canadian Healthcare Regulations. This will allow them to have a practical understanding of what is required to enhance their cloud journey and facilitate a smoother transition to the cloud.
iSecurity and MD+A Health are actively assisting Canadian healthcare and medical research organizations in comprehending the risks and exploring pathways to embrace the cloud. Through extensive research and analysis, iSecurity and MD+A Health have evaluated Google Cloud as a suitable platform for healthcare. Their diligent efforts have resulted in the production of comprehensive documents that detail their findings via a Threat Risk Assessment (TRA) and a Privacy Impact Report (PIA).
Why a Threat Risk Assessment?
A threat risk assessment is a process of identifying and evaluating threats to an organization and then determining the likelihood and impact of those threats. The goal of a threat risk assessment is to identify the most serious threats and develop mitigation strategies to reduce the likelihood and impact of those threats.
A threat risk assessment typically involves the following steps:
- Identify threats: The first step is to identify all potential threats to the organization. This can be done by brainstorming, interviewing experts, or reviewing historical data.
- Evaluate threats: Once the threats have been identified, they need to be evaluated in terms of their likelihood and impact. The likelihood of a threat is the probability that it will occur, while the impact of a threat is the severity of the consequences if it does occur.
- Prioritize threats: The threats need to be prioritized based on their likelihood and impact. The most serious threats should be addressed first.
- Develop mitigation strategies: Once the threats have been prioritized, mitigation strategies need to be developed to reduce the likelihood and impact of those threats. Mitigation strategies can include things like implementing security controls, training employees, and developing contingency plans.
- Implement mitigation strategies: The mitigation strategies need to be implemented and tested to ensure that they are effective.
- Monitor and review: The threat risk assessment should be monitored and reviewed regularly to ensure that it is still effective.
Why a Privacy Impact Assessment?
A Privacy Impact Assessment (PIA) is a process that organizations use to identify and assess the privacy risks associated with a new or changed information technology (IT) system or project. The goal of a PIA is to help organizations protect the privacy of individuals whose personal information is collected, used, or disclosed by the IT system or project.
PIAs typically include the following steps:
- Identifying the purpose of the IT system or project and the types of personal information that will be collected, used, or disclosed.
- Identifying the privacy risks associated with the IT system or project.
- Assessing the likelihood and severity of the risks.
- Developing and implementing controls to mitigate the risks.
- Monitoring the effectiveness of the controls.
PIAs are an important tool for organizations to help them follow privacy laws and regulations. They can also help organizations build trust with their patients, employees and the research community by demonstrating their commitment to protecting privacy.
The benefits of conducting a PIA:
- Helps organizations identify and assess privacy risks
- Helps organizations develop and implement controls to mitigate privacy risks
- Helps organizations comply with privacy laws and regulations
- Helps organizations build trust with customers and employees
Why Google Cloud?
Google Cloud is committed to providing Canadian healthcare organisations with an environment to expand both their clinical and research environments. Google Cloud has invested significant resources into building out a cloud environment based on best practices coming from Google’s experience running some of the world’s largest platforms.
Some highlights include:
- Built-in security features that help protect your data and applications from unauthorized access, use, disclosure, disruption, modification, or destruction.
- A comprehensive security management platform that helps you assess, prioritize, and address security risks across your organization.
- A team of security experts who can help you design, implement, and manage your security solutions.
- A wide range of security training and resources to help you learn about and stay up-to-date on the latest security threats and best practices.
How Cloud Networks Enable CSPs to Deliver 5G
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Communication services providers (CSPs) are experiencing a period of disruption. Overall revenue growth is decelerating and is projected to remain below 1 percent per year, following a trend that started even before the pandemic.1 At the same time, driven by the pandemic, data consumption in 2020 increased by 30 percent relative to 2019, with some operators even reporting increases of 60 percent.2
The combination of pressure on revenues with rising data traffic costs is forcing operators to innovate in three fundamental ways. First, operators are looking to establish new sources of revenue. Second, increased network utilization must be met with a reduction in network cost. And third, there is an opportunity to gain new customers by improving the customer experience.
Fortunately, 5G offers a path forward across each of these three areas. Concepts such as network slicing and private networks allow CSPs to offer differentiated network services to public sector and enterprise customers. The disaggregation of hardware and software allows new vendors with unique strengths to enter the market and to enable CSPs to build, deploy, and operate networks in fundamentally new ways. And the ability to place workloads at the edge permits CSPs to offer compelling experiences to consumers and businesses alike. In this blog, we will discuss how CSPs can create a solid foundation for their cloud networks.
Understanding telecommunications networks
First, it is useful to consider the way telecommunications networks were traditionally built. Initially, networks were built using physical network functions (PNFs) — appliances that used a tight combination of hardware and software to perform a specific function. PNFs offered the benefit of being purpose-built for a specific application, but they were inflexible and difficult to upgrade. As an example, deploying new features frequently required replacing the entire PNF, i.e., deploying a new hardware appliance.
The first step in improving deployment agility came with the concept of virtualized network functions (VNFs), software workloads designed to operate on commercial off-the-shelf (COTS) hardware. Rather than utilizing an integrated hardware and software appliance, VNFs disaggregated the hardware from the software. As such, it became possible to procure the hardware from one vendor and the software from another. It also became possible to separate the hardware and software upgrade cycles.
However, while VNFs offered advantages over PNFs, VNFs were still an intermediate step. First, they typically needed to be run within a virtual machine (VM), and as such required a hypervisor to interface between the host operating system (OS) and the guest OS inside the VM. The hypervisor consumed CPU cycles and added inefficiency. Second, the VNF itself was frequently designed as a monolithic function. This meant that while it was possible to upgrade the VNF separately from the hardware, such an upgrade, even for a feature that affected only a portion of the VNF, required deployment of the entire large VNF. This created risk and operational complexity, which in turn meant that upgrades were delayed just as they were with PNFs.
Creating the foundation for cloud networks
The trick to establishing your cloud based network resides in the challenge of moving from VNFs to containerized network functions (CNFs) — network functions organized as containers as a collection of small programs, each of which can be independently operated.
The concept of containers is not new. In fact, Google has been using containerized workloads for over 15 years. Kubernetes, which Google developed and open-sourced, is the world’s most popular container orchestration system, and is based on Borg, Google’s internal container management system.3 There are lots of benefits to using containers, but fundamentally, it frees developers from worrying about resource scheduling, interprocess communication, security, self-healing, load balancing, and many other tedious (but important!) tasks.
Consider just a couple examples of benefits that containerization brings to network functions. First, when upgrading the network function to implement new features, you no longer need to re-deploy the entire network function. Instead, you only need to re-deploy the containers that are affected by the upgrade. This improves developer velocity and reduces the risk of the upgrade because, rather than infrequent upgrades that each introduce substantial changes, you can now have frequent upgrades that each deploy small changes. Small changes are less risky because they are easier to understand and to roll back in case of anomaly. Incidentally, this also improves your security posture because it reduces the time between when a security vulnerability is discovered and when a patch is deployed.
Speaking of security, another example of the benefits that containerization brings to network functions is an automatic zero-trust security posture. In Kubernetes, the communication among microservices can be handled by a service mesh, which manages mundane aspects of inter-services communication such as retries in case of failure and providing observability into communication. It can also manage other essential aspects such as security. For example, Anthos Service Mesh, which is a fully-managed implementation of the open-source Istio service mesh (also co-developed by Google), includes the ability to authenticate and encrypt all communications using mutual TLS (mTLS) and to deploy fine-grained access control for each individual microservice.
Automation and orchestration for cloud networks
CNFs bring tremendous benefits, but they also bring challenges. In place of a relatively small number of network appliances, we now have a large number of containers, each of which requires configuration, management, and maintenance. In the past, many of these processes were accomplished using manual techniques, but this is impossible to accomplish economically and reliably at the scale required by CNFs.
Fortunately, there are cloud-native approaches to solving these challenges. First, consider the problem of autonomously deploying and maintaining CNFs. The ideal way is to use the concept of Configuration as Data. Unlike imperative techniques such as Infrastructure as Code, which provide a detailed description of a sequence of steps that need to be executed to achieve an objective, Configuration as Data is a declarative method whereby the user specifies the desired end state (i.e., the actual desired configuration) and relies on automated controllers to continuously drive the infrastructure to achieve that state. Kubernetes includes such automated controllers, and the great news is that this method can be used not just for infrastructure but also for the applications residing on top of it, including CNFs. This cloud-native technique frees you from the toil and associated risk of writing detailed configuration procedures, so you can focus on the business logic of your applications.
As another example, consider the problem of understanding your network performance, including anomaly detection, root cause analysis, and resolution. The cloud-native approach starts with creating a data platform where both infrastructure and CNF monitoring data can be ingested, regularized, processed, and stored. You can then correlate data sets against each other to detect anomalies, and with AI/ML techniques, you can even anticipate anomalies before they happen. AI/ML is likewise indispensable in gaining an understanding of why the anomaly is happening, i.e. performing root cause analysis, and automated closed-loop controllers can be developed to correct the problem, ideally before it even happens.
Architecting for the edge
The transition from VNFs to CNFs is a critical piece in addressing the challenge that CSPs face today, but it alone is not enough. CNFs need infrastructure to run on, and not all infrastructure is created equal.
Consider a typical 5G network. There are some functions, such as those associated with an access network, that need to be deployed at the edge. These functions require low latency, high throughput, or even a combination of the two. In 5G networks, examples of such functions include the radio unit (RU), distributed unit (DU), centralized unit (CU), and the user plane function (UPF). The first three are components of the radio access network (RAN), while the last is a component of the 5G core. At the same time, there are some other control plane functions such as the session management function (SMF) or the authentication and mobility management function (AMF) that do not have such tight latency and high throughput requirements and can thus be placed in a more centralized data center. Furthermore, consider an AI/ML use case where a particular model (perhaps for radio traffic steering) needs to run at the network edge because of its latency requirements. While the model itself needs to run at the edge, model training (i.e., generating the model coefficients) is frequently a compute-intensive exercise that is latency-insensitive and is thus more optimal to run in a public cloud region.
All of these use cases have one thing in common: they call for a hybrid deployment environment. Some applications must be deployed at the edge as close to the user as possible. Others can be deployed in a more centralized environment. Still others can be deployed in a public cloud region to take advantage of the large amount of compute and economies of scale available therein. Wouldn’t it be convenient — if not transformational — if you could use a single environment for deploying at the edge, in a private datacenter, and in public cloud, with a consistent set of security, lifecycle management, policy, and orchestration resources across all such locations? This is indeed what Google Distributed Cloud, enabled by Anthos, brings to the table.
With Google Distributed Cloud, you can architect a 5G network deployment such as the one shown below.
Business benefits of cloud networks
Beyond the technical benefits, consider the business benefits of such an architecture. First, by following the best practices of hardware and software disaggregation, it permits the CSP to procure the infrastructure and the network functions from different vendors, spurring competition among vendors. Second, each workload is placed in precisely the right location, enabling efficient utilization of hardware resources and offering compelling low-latency, high-throughput services to users. Third, because the architecture utilizes a common hybrid platform (Anthos), it makes it easy to move workloads across infrastructure locations. Fourth, the separation of workloads into microservices accelerates time-to-market when developing new features or applications, such as those enabling enterprise use cases. And finally, the container management platform supports the simultaneous deployment of both network functions and edge applications on the same infrastructure, allowing the operator to deploy new experiences such as AR/VR directly on bare metal as close to the user as possible.
The next generation cloud network is now
There is a lot more we could say, but perhaps the most important takeaway is that this architecture is not a future dream. It exists today, and Google is working with leading CSPs and network vendor partners to deploy it, helping them realize the promise of 5G to deliver new revenues, reduce operating costs, and enable new customer experiences.
To learn more, watch the video series on the cloudification of CSP networks.
Discover what’s happening at the edge: How CSPs Can Innovate at the Edge.
1.Statista, Forecast growth worldwide telecom services spending from 2019 to 2024
2 PricewaterhouseCoopers, Global entertainment and media outlook 2021-2025
3. Borg: The Predecessor to Kubernetes
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Google Cloud Helps DSW Engage Over 28M Shoe Lovers in Real Time
DSW Shoes turned to Google Cloud and Google Cloud Partner – Slalom to develop a realtime, engaging loyalty program that serves more than 28 million active members instantly.
The major move from on premise to a cloud-based, data-driven platform that offers speed, flexibility, and scalability has helped increase DSW’s new customer rate by 9% and allows the company to achieve a best-in-class retention rate.
Watch the video to understand how DSW Shoes did this.
SevenRooms: Building a profitable, sustainable future in food and beverage with Google Cloud
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Finding ways to increase customer loyalty and profitability in a post-COVID world is top of mind for hotels, bars, and restaurants. Unfortunately, many food and beverage services providers struggle to deliver the personalized experiences that keep guests coming back for more. The reality is that most traditional hospitality apps offer only limited insight into guest activities, preferences, underlying operating costs, and other essential details.
We built SevenRooms to help food and beverage operators create truly memorable experiences by managing and personalizing every step of the guest journey. With SevenRooms, restaurants in more than 250 cities globally leverage technology and data to provide unforgettable hospitality experiences with a human touch. That includes seating guests at their favorite table, pouring complimentary glasses of wine from a preferred vintage, offering special menu options, and personalized experiences for special occasions.
Scaling guest experience and retention on Google Cloud
When developing SevenRooms, we needed a technology partner that would enable our small team to securely scale services, automate manual tasks, and accelerate time to market while reducing IT costs. That’s why we started working with Google App Engine—and later became more involved with the Google for Startups Cloud Program.
We soon realized many traditional apps lacked the integrations and capabilities needed to respond to the unique challenges facing food and beverage operators that the Google for Startups Cloud Program services provide. With guidance from experts on the Google Startups Success team, we quickly transformed SevenRooms from a beta restaurant and nightclub reservation and guest management point solution into a full-fledged guest experience and retention platform that analyzes actionable data to automatically build and continuously update detailed guest profiles.
The combination of Google App Engine and other Google Cloud solutions makes everything easier to build and scale. We’re seeing our total cost of ownership (TCO) decline by 10-15%, so we can shift additional resources to R&D to help customers create one-of-a-kind interactions with their guests. And importantly, we can bring new products to market 200-300% faster than competitors.
Because SevenRooms handles a lot of sensitive guest data, stringent security protocols are vital. We store all data on Google Cloud, taking advantage of its highly secure-by-design infrastructure and built-in support for international data privacy laws such as GDPR. We use BigQuery and Looker for data analysis and reporting, and power our NoSQL database with Firestore. We also scale workloads and run Elasticsearch on Google Compute Engine (GCE)—and seamlessly integrate our reservation booking engine with Google Maps and Google Search.
In the future, we’re looking to further market actionable guest data with the help of advanced machine learning (ML) models with TensorFlow and Google Cloud Tensor Processing Units (TPUs). Cloud TPUs and Google Cloud data and analytics services are fully integrated with other Google Cloud offerings, including Google Kubernetes Engine (GKE). By running ML workloads on Cloud TPUs, SevenRooms will benefit from Google Cloud’s leading networking and data analytics technologies such as BigQuery.
We’re also exploring additional Google Cloud solutions such as Anthos to unify the management of infrastructure and applications across on-premises, edge, and multiple public clouds, as well as Google Cloud Run to deploy scalable containerized applications on a fully managed serverless platform. These solutions will enable us to continue to quickly expand our services and offer customers a variety of new benefits.
Building a profitable, sustainable future in food and beverage
Our work with the Google Startups Success team has been instrumental to helping us get where we are today. Their responsiveness is incredible and stands out compared to services from other technology providers. Google Cloud gives us a highly secure infrastructure and next-level training to evolve our infrastructure using solutions such as BigQuery and Backup and Disaster Recovery.
We also work with Google Cloud partner, DoiT International, to further scale and optimize our operations. In particular, DoiT provided expert scripts to short-cut lengthy processes, while actively troubleshooting any issues or questions that came up. DoiT continues to share guidance in key areas for future products and features. They have provided expertise for architecture, infrastructure and cost management. Moving forward, we’re excited to work with Google Cloud and DoiT to handle a growing surge in users which we anticipate in 2023 and beyond.
With Google Cloud, SevenRooms is revitalizing food and beverage service delivery by enabling businesses to cultivate and maintain direct guest relationships, deliver exceptional experiences, and encourage repeat visits. We’ve compiled many case studies that demonstrate how our customers see great results by personalizing their interactions with guests, from $1.5M in cost savings, to $400K of additional revenue, to a 68% jump in email open rates.
Demand for our guest experience and retention platform keeps growing as we help our customers take a people-first approach by delivering unique and tailored dining experiences. We can’t wait to see what we accomplish next as we expand our team and reach new markets worldwide.
If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.
Casper on Google Cloud: Revolutionizing Web3 Development with Flexibility & Security
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Casper Labs announced a collaboration with Google Cloud that will allow developers to launch public and/or private Casper nodes directly from Google Cloud. This enables a much more seamless and highly secure process for the millions of developers who want to build in blockchain environments without having to learn new, highly specialized programming languages. Additionally, Google Cloud will provide its scalable and reliable infrastructure to developers building on the Casper Protocol.
Blockchain technology is maturing
As blockchain technology matures, a growing number of businesses are embracing it as a key way to drive new efficiencies and realize cost savings.
According to a recent Casper Labs study, 87% of executives polled in the United States, United Kingdom and China reported plans to invest in a blockchain solution in 2023. This is due in no small part due to recent innovations that help organizations overcome the so-called Blockchain Adoption Trilemma, which previously held that it was impossible for any blockchain to be simultaneously a) decentralized, b) scalable, and c) secure.
Thanks to the rise of proof-of-stake blockchains like Casper, new models have emerged that enable a more scalable and secure architecture that no longer forces a compromise on decentralization.
Another trend facilitating these growing adoption rates is the rise of WebAssembly (WASM) as a baseline technology for newer blockchains, including Casper. WASM (created by W3C) makes application development in blockchain environments far more accessible and interoperable to the millions of developers worldwide who specialize in languages like Java, Javascript, C++ and Rust. Previously, any blockchain-based build required a high degree of specialized developer knowledge, which made it a much more challenging option for most organizations.
Meet Casper
Casper is a permissionless, decentralized public blockchain based on WASM that was built explicitly to foster enterprise adoption of blockchain technology. Beyond its more accessible model, Casper is the first and only blockchain to offer native upgradable smart contracts. This means that organizations can have the option to securely and consistently update software code even after it is running on Casper. This gives organizations the control and flexibility to use industry best practices, such as continuous deployment and continuous integration, which are already in use in their IT departments. Casper is also highly configurable and allows organizations to support public, private, and/or hybrid deployments.
Casper is also noteworthy for the presence of Casper Labs, a software development and professional services firm that supports organizations building on the Casper network. Unlike most blockchains that follow a more traditional open-source project, Casper Labs provides around-the-clock support and bespoke software development for enterprise organizations. Recently, Casper Labs helped patent management company IPwe execute the largest-ever blockchain deployment, featuring more than 25 million patents being added as custom NFTs to the Casper Blockchain.
How to get started with Casper on Google Cloud
Developers who want to start building on Casper can find a comprehensive series of tutorials here.
The Casper Association also recently announced a $25 million grant program to support projects and developers building on Casper. Interested participants can apply here.
Regulatory-induced Challenges Create Hurdles to Cloud Adoption for Financial Services Firms
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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:
- help adapt to changing customer behaviors and expectations,
- enhance operational resilience,
- support the creation of innovative new products and services,
- enhance financial services institutions’ data security capabilities, and
- 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.
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