As enterprises accelerate their migration to the cloud, they experience more notable mid- and late-phase migration challenges. Specifically, 41% face challenges when optimizing apps in the cloud post-migration, and 38% struggle with performance issues on workloads migrated to the cloud. Further, organizations have also increased reliance on outside consultants and other service providers for early-stage cloud migration tasks to ongoing management post-implementation.1

To help customers through these challenges with a simple, quick path to a successful cloud migration, Google Cloud created our comprehensive Rapid Assessment & Migration Program (RAMP). And we’ve got some exciting developments to share with our customers and partners:

Expanded focus on post-migration TCO/ROI

Given the complex nature of cloud migrations, we are committed to meeting our customers where they’re at in their cloud journeys and partnering with them to achieve their business goals — be it building customer value through innovation, driving cost efficiencies, or increasing competitive differentiation and productivity. RAMP is a holistic framework based on tangible customer TCO and ROI analyses, that supports our customers’ journeys all the way through: from assessing their digital landscapes across multiple sources including on-prem and other clouds, and identifying prioritized target workloads to building a comprehensive migration and modernization plan.

Accelerate positive outcomes with expert partners

Customers can also now expect a more streamlined migration experience through our ecosystem of partners who have completed their cloud migration specialization. Last week, we announced industry-leading updates to our partner funding programs with new assessment and consumption packages that simplify and accelerate our customers’ journey to Google Cloud, at little-to-no cost. These packages offer prescriptive pathways for infrastructure and application modernization initiatives, empowering our partners to support our customers at every stage — from discovery and planning to migration and modernization.

Through our partner ecosystem, our customers can expect:

• Distinct funding packages for assessment, planning, and migration
• Faster approval processes for accelerated deployments
• More partners eligible to participate in RAMP and access these new funding packages

Sustainability through migration

Another major focus area for RAMP is helping enterprises optimize their migration planning and maximize their ROI by including their business and technical considerations early in the process and including any sustainability goals they may have. To aid with their sustainability efforts, we are excited to share that customers can now receive a Digital Sustainability Report along with their IT assessments – enabling sustainability to be built into their migration strategies. The report provides actionable insights to measure and reduce their environmental impact, and is based on some of Google Cloud’s own best practices, having been carbon-neutral for decades and looking to run on carbon-free energy by 2030.

We are committed to solving complex problems for our customers and partners, and these updates are a reflection of the feedback we receive. Simplify your cloud migration strategy today by requesting your free assessment, finding a partner to work with, or talking to your existing partner to get started.

^{1. Forrester Consulting, State Of Public Cloud Migration; A study commissioned by Google, 2022}

When ATB Financial decided to migrate its vast SAP landscape to the cloud, the primary goal was to focus on things that matter to customers as opposed to IT infrastructure. Based in Alberta, Canada, ATB Financial serves over 800,000 customers through hundreds of branches as well as digital banking options. To keep pace with competition from large banks and FinTech startups and to meet the increasing 24/7 demands of customers, digital transformation was a must. To support this new mandate, in 2019, ATB migrated its extensive SAP backbone to Google Cloud. In addition to SAP S/4 HANA, ATB runs SAP financial services, core banking, payment engine, CRM and business warehouse on Google Cloud. 

In parallel, changes were needed to ATB’s legacy data platform. The platform had stability and reliability issues and also suffered from a lack of historical data governance. Analytics processes were ad hoc and manual. The legacy data environment was also not set up to tackle future business requirements that come with a high dependency on real-time data analysis and insights.

After evaluating several potential solutions, ATB chose BigQuery as a serverless data warehouse and data lake for its next-generation, cloud-native architecture. “BigQuery is a core component of what we call our data exposure enablement platform, or DEEP,” explains Dan Semmens, Head of Data and AI at ATB Financial. According to Semmens, DEEP consists of four pillars, all of which depend on Google Cloud and BigQuery to be successful:

1. Real-time data acquisition: ATB uses BigQuery throughout its data pipeline, starting with sourcing, processing, and preparation, moving along to storage and organization, then discovery and access, and finally consumption and servicing. So far, ATB has ingested and classified 80% of its core SAP banking data as well as data from a number of its third-party partners, such as its treasury and cash management platform provider, its credit card provider, and its call center software. 
2. Data enrichment: Before migrating to Google Cloud, ATB managed a number of disconnected technologies that made data consolidation difficult. The legacy environment could handle only structured data, whereas Google Cloud and BigQuery lets the bank incorporate unstructured data sets, including sensor data, social network activity, voice, text, and images. ATB’s data enrichment program has enabled more than 160 of the bank’s top-priority insights running on BigQuery, including credit health decision models, financial reporting, and forecasting, as well as operational reporting for departments across the organization. Jobs such as marketing campaigns and month-end processes that used to take five to eight hours now run in seconds, saving over CA$2.24 million in productivity. 
3. Self-service analytics: Data for self-service reporting, dashboarding, and visualization is now available for ATB’s 400+ business users and data analysts. Previously, bringing data and analytics to the business users who needed it while ensuring security was burdensome for IT, fraught with recurrent data preparation and other highly manual elements. Now, ATB automates much of its data protection and governance controls through the entire data lifecycle management process. Data access is not only open to more team members but it is faster and easier to acquire without compromising security. And it’s not just raw data that users can access. ATB uses BigQuery to define its enterprise data models and create what it calls its data service layer to make it easier for team members to visualize their data.
4. AI-assisted analytics and automation: Through Google Cloud and BigQuery, ATB has been able to publish data and ML models that provide alerts and notifications via APIs to customer service agents. These real-time recommendations allow customer service agents to provide more tailored service with contextualized advice and suggested new services. So far, the company has deployed more than 40 ML models to generate over 20,000 AI-assisted conversations per month. Thanks to improved customer advocacy and less churn, the bank has realized more than CA$4 million in operating revenue. During the ongoing COVID crisis, the system was also able to predict when business and personal banking customers were experiencing financial distress so that a relationship manager could proactively reach out to offer support, such as payment deferral or loan restructuring. The AI tools provided by BigQuery are also helping ATB detect fraud that previously evaded rules-based fraud detection by using broader sets of timely and accurate data. 

Thanks to the speed and ease of moving data from SAP to BigQuery, ATB is using artificial intelligence (AI) and machine learning (ML) to do things it previously hadn’t thought possible, including sophisticated fraud prevention models, product recommendations, and enriched CRM data that improves the customer experience. 

Using the power of Google Cloud and BigQuery, ATB Financial has been able to draw more value from its SAP data while lowering cost and improving security and reliability. Speed to provide data sets and insights to internal team members has improved 30%. The bank also has seen a 15x reduction in performance incidents while improving data governance and security. Dan Semmens projects that the digital transformation strategy built on Google Cloud and BigQuery has both saved millions compared to its on-premises environment and has also realized millions in new business opportunities. 

Semmens is looking toward the future that includes initiatives like Open Banking and greater ability to provide real time personalized advice for customers to drive revenue growth. “We see our data platform as foundational to ATB’s 10-year strategy,” he says. “The work we’ve undertaken over the past 18 months has enabled critical functionality for that future.” 

Learn more about how ATB Financial is leveraging BigQuery to gain more from SAP data. Visit us here to explore how Google Cloud, BigQuery, and other tools can unlock the full value of your SAP enterprise data.

Over the last decade, financial service organizations have been adopting a cloud-first mindset. According to InformationWeek, lower costs and enhanced scalability were the biggest drivers for cloud adoption in financial services, and cloud-native applications allow access to the latest technology and talent, enabling adopters to rebuild transaction processing systems capable of supporting very high volumes and low latency.

Both Neo4j and Google Cloud have been using relationship-based data representations since the beginning, and we’re dedicated to using this technology to help financial services customers drive business transformation. We are excited about the prospects of financial services (FinServ) cloud systems and believe that graph data in the cloud can help solve significant challenges in the industry.

Data Challenge #1: Risk Management and Compliance

First among the top concerns for any CIO moving to the cloud is risk management and compliance. Disconnected, uncontextualized, or stale data create opportunities for fraud and financial crimes to occur. The fact is when it comes to FinServ, the question is not “if” but rather how often an attack will occur.  Unfortunately, incidents have been trending upward over the last decade, and COVID has only exacerbated this reality. Financial crimes affect the bottom line both in the remediation of these crimes and in intangibles like brand value.  

Add to this the complexity of international banking, which makes “compliance” a moving target. Penalties due to noncompliance are a constant concern to any FinServ organization.

The tabular representation of information with a fixed number of columns that never change prevents a description of an ever changing world with changing characteristics. Relational databases are great if the world you describe does not move fast but have limitations when data structures are highly interlinked and not homogeneous.

Neo4j Aura on Google Cloud provides a foundation for creating dynamic, futureproof, scalable applications that adhere to the security standards and protocols today’s financial services organizations require to meet the challenges of finding and preventing bad actors. This also includes enterprise scalability; reaching over 1 Billion nodes and relationships to streamline queries and provide solutions that meet regulatory and privacy compliance across geographies. Neo4j has helped some organizations save billions of USD in fraud in the first year of deployment alone.  

What makes graph technology the best choice for fraud detection use cases is that the relationships between the data-points are as important as the data-points themselves. Let’s take as an example, one John Smith approaches a multi-national banking institution to manage the primary account for his new holding corporation.  

While no one has any record of John R Smith Holdings LLC, the bank’s application built on graph technology understands that there are several well-known entities owned by John Smith Holdings. The application also identifies several well-known board members who bank with this institution. Due to this relationship-driven approach, the bank now understands John R Smith is not “John Smith,” who previously attempted to open an account for his holding corporation, which had no information associated with it prior to two months ago.

Data Challenge #2 Manual Processes and Inefficiencies 

The ubiquity of the cloud offers an opportunity to deploy automation at unprecedented levels to tackle the errors and inefficiencies that manual processing allows to creep into processes. When data comes from disparate, perhaps legacy systems – which may have become siloed and “untouchable” over the years – further complexity arises. As an example, if someone in sales types “John Smith” into a CRM system not knowing that John R Smith is the spelling in the customer data master, it may result in two separate and potentially conflicting records. Being able to join those records together in a mastered view helps to solve this problem. In addition, low data quality equates to an increase in risk, costs, and implementation times for new systems. 

Neo4j Aura on Google Cloud provides automation and artificial intelligence (AI) that reduces manual processes and the errors that accompany them. In this graph architecture each node, which can represent a person, will have labels, relationships, and properties associated with it. This allows for the use of AI which can easily understand that John Smith in the CRM is the same John R Smith in the customer master. The information contained in Neo4j can be connected bi-directionally to ensure consistency across applications and data sources. 

One of the benefits of this approach is that linking information allows organizations to keep the full value of the data, rather than forcing the data into predetermined tabular representations, with the risk of losing valuable information and insights.

Data Challenge #3: Customer Engagement and Insight

Another significant concern is the high expectations today’s customers have for every interaction. End users are accustomed to predictable experiences on their digital devices, and FinServ apps are no exception. Added to this, the “Covid economy” has driven digital adoption significantly across demographics; even among customers who might traditionally have used in-person services. This also equates to increased expectations for personalized, predictable experiences with every digital interaction. We know that latency has always been a key consideration for financial trading, but a recent ComputerWeekly study showed that every financial organization should ensure their visible latency is at 10 milliseconds or less. Customers no longer accept their broadband is at fault.

Finally, blind spots in the customer journey often result in dissatisfaction, which ultimately leads to increased churn. Without gaining actionable insights from your customers, there is no room to innovate and iterate on what they are looking for in your products and services. And this translates to losing market share and competitive advantage.

The NoSQL architecture, specifically the dynamic schema and structure of Neo4j Aura gives you the ability to take charge of your data and make changes according to your development cycles or newer data models. This equates to faster builds, more comprehensive releases and a wider, richer data-set that can be contextualized and understood instantly. Graph technology is the logical choice for building a Customer 360 application. Under this approach organizations not only get valuable insight into the individual client’s behavior and patterns, but also those of their family, friends and colleagues. This allows for stronger personalization, targeted campaigns and successful execution, resulting in increased customer satisfaction and retention levels.

Graph Technology on Google Cloud

Neo4j is a recognized leader in graph database technology and the only fully integrated graph solution on Google Cloud, helping to fill a common need for Google Cloud customers. Both Neo4j and Google Cloud are invested in continuing to grow our partnership and mutual product direction.  

You can find and deploy the Neo4j graph database straight from the Google Cloud marketplace, whether you want to download the software for an on-premises deployment, use the virtual machine image, or use the hosted solution, Aura on Google Cloud, the graph database-as-a-service. In any deployment, you get the same enterprise-grade scalability, reliability, and connectivity along with successful, repeatable use cases you can rely on to resolve your particular challenges and integrated billing. 

For a real-world example of how graph technology can optimize financial services, you can read our Case Study with fintech Current. Current, a leading U.S. financial technology platform with over three million members, used Neo4j Aura on Google Cloud to create a personalization engine based on client relationships. 

To learn more about Neo4j Aura on Google Cloud for FinServ organizations, register for our webinar on Thursday, December 16 with Jim Webber, Chief Scientist, CTO Field Ops at Neo4j and Antoine Larmanjat, Technical Director, Office of the CTO, Google Cloud. 

Click here to Register

As the pace of digital transformation accelerates, our partnership with VMware continues to focus on helping customers successfully navigate their cloud journey and achieve their business objectives through seamless and rapid migration of business critical VMware workloads.

We announced the general availability of Google Cloud VMware Engine in May 2020. Since then we have worked closely with VMware to make it easier for customers to quickly migrate and run business-critical, VMware-based workloads on Google Cloud. Customers are already leveraging the service across a variety of use cases including application migration, datacenter exit, virtual desktop infrastructure, disaster recovery, and spinning up new capacity quickly to meet business needs.

For example, retailer Carrefour migrated its on-premises VMware workloads to Google Cloud without disruption to shoppers or employees while reducing operating costs by 40% and energy consumption by 45%. Once in the cloud, Carrefour was able to leverage its data and AI to deliver innovative customer experiences across online and in-store channels. Similarly, telecommunications provider Mitel migrated thousands of VMware instances across 30 data centers to Google Cloud in less than 90 days, quickly achieving increased stability, scale, and security.

Today, we announced the continued growth of the Google Cloud and VMware partnership with the addition of Google Cloud VMware Engine within VMware Cloud Universal. Google Cloud VMware Engine delivers a cloud-native VMware experience and enables you to rapidly migrate to the cloud without changes to your apps, policies, or tools. Once you migrate your VMware workloads to Google Cloud, you can accelerate digital transformation through seamless access to services such as BigQuery for real-time data analytics and cloud-native container-based architectures on Kubernetes.

With VMware Cloud Universal, you will be able to accelerate migrations of your workloads and applications to Google Cloud through purchase of Google Cloud VMware Engine from VMware and its partners, allowing you to flexibly purchase credits, and leverage existing spend and unused VMware Cloud Universal credits. The program will offer the following benefits:

• Financial flexibility by letting you redeem VMware Cloud Universal credits for Google Cloud VMware Engine
• Streamlined consumption by enabling you to burn down your Google Cloud commits while purchasing from VMware
• Use of existing VMware licensing investments through the VMware Cloud Universal program for Google Cloud VMware Engine

With Google Cloud VMware Engine, you can take advantage of Google Cloud’s highly performant, scalable infrastructure with fully redundant and dedicated 100 Gbps networking, providing 99.99% availability to meet the needs of the most demanding workloads at very low costs. By providing a consistent VMware environment natively in Google Cloud, you can quickly migrate your VMware workloads to Google Cloud without changes. Deep and unique networking integrations and capabilities such as multi-region and multi-VPC connectivity, further ease the migration of complex enterprise networking topologies to Google Cloud. With rapid provisioning of private clouds across 13 global regions, you can also take advantage of on-demand capacity to serve your infrastructure needs with a cloud-native VMware environment in Google Cloud.Once in the cloud, you can take advantage of other Google Cloud services such as BigQuery and Cloud Operations to gain data-driven insights and unify operations.

Getting started

With Google Cloud VMware Engine as part of VMware Cloud Universal, Google Cloud is a compelling cloud destination for your VMware workloads. You can learn more about how to get started with the service and get additional detail around use cases and pricing on our website.