About three years ago, JCB, one of the biggest Japanese payment companies, launched a project to develop new high-value services with agility. We set up a policy of starting small from scratch without using the existing system, which we call the concept of “Dejima”, where we focused on improving various aspects such as team structure, risk management, and application and platform development process.

Until now, large Japanese enterprises have built decision-making systems focused on eliminating unnecessary business processes and efficiently increasing quarterly profits. As a result, we are seeing more organizational structures that make it difficult to take on new challenges or experiments with trial and error. We wanted to breathe a new life into this situation, and that is how the concept of Dejima came up. In the Edo period, Japan closed its national border to other countries under its national isolation policy. At the time, Dejima was the only area where special rules were applied and allowed people from different cultures to come and go, and trade. This special rule generated the culture of inclusion and led to Dejima’s prosperity. Like Dejima, we believe that creating an organization that is independent from other business practices can be effective in enabling digital transformation for the organization. 

We have been able to make this transformation with the direct help of the Google Cloud and its products such as Google Kubernetes Engine (GKE), Cloud Spanner and Anthos Service Mesh, applying domain-driven design and microservice architecture. We named this the “JCB Digital Enablement Platform (JDEP),” which now hosts multiple business critical production services.

A key benefit of GKE is that the team can easily add resources and release them when they are finished, allowing them to be flexible to accommodate busy periods and off-seasons. Meanwhile, Anthos Service Mesh helps us manage complex environments easily. With containerization and managed services, we are prepared for the future for when more services go into production, as it would be easy to maintain and provide version upgrade support. At the same time, Cloud Spanner ensures that we maintain a 99.99% availability at all times.

Our initial motivation for introducing SRE practices was to break proverbial walls between business, development and operations, which was a success. Now we are focused on ensuring its reliability and maintaining customer satisfaction with our SRE practices.

To ensure the success of SRE practices that we implemented, there were a few categories we needed to address, from defining the organizational culture and practices to ensuring the policies attached to the new models created were practical enough to be implemented on the ground level. This is so that the Dejima concept remains sustainable for the long run.  

Instilling a culture of measurement 

Here, “appropriate” reliability is the key. According to the conventional way of thinking at JCB, “service failure must not occur” and “SLA should be maintained as high as possible.” We started by discussing what was the “appropriate reliability” that our customers really needed, but it was not as easy as we thought because the level of reliability for user satisfaction differed from application to application. 

Eventually, the business, development and operations teams formulated specific SLIs and SLOs together, something we would never have been able to do if we discussed separately. This is because the business is required to compromise on lower service levels, since our reliability standard used to be too high. The collaboration of development and operations teams is necessary to understand how our system works upon our users’ interactions.

After Google Cloud helped us run a series of workshops where all teams participated, we saw change within the organization. The business team started evangelizing SRE to other members in the business department, and the development and operations teams started collaborating autonomously. We felt like we were working at Google speed, accomplishing so much in a short amount of time. 

Understanding SRE as an entire company is necessary to progress. We are now working on creating internal training materials to spread the SRE concept throughout the company.

Eliminating ambiguity

With the cooperation of Google Cloud, we have created a Team Charter that defines the team’s mission, values and engagement models. We also created policy documents that include Incident Response Policy, Postmortem Policy, On-call Policy, Toil Policy and Error Budget Policy, to eliminate ambiguity in day-to-day operations.

For example, when an incident occurs, we can identify exactly the level of importance, the roles that are assigned to each person, and in what order they need to follow. When to do a postmortem, who owns it? What to do if the error budget is exhausted? How do other teams reach out to SRE when they have problems? The written policy documents will dramatically improve efficiency and motivate teams to adopt a culture of learning from failures.

The format for such policies are written in Google’s SRE book, but when we adopt it, it needs to take into account the circumstances specific to our company. Simply copying an existing policy won’t work, which is why it’s important to formulate a policy that fits the situation each team is in.

Reformalizing teams

Based on these policies, JCB’s SRE team has two sub-teams. One is called Sheriff which works as the platform SRE, and provides infrastructure services for the application team. The other is called the Diplomat which works as the embedded SRE, and participates in the application team to lead productionisation. There is also a team called Architecture that is separate from the SRE teams whose role is to consult SRE on system design and review architecture.

The SRE team was a single role when it was first launched, but now has two sub-teams. This is because as the number of application teams increases, the number of support tasks for the teams also increases, which can result in a shortage of resources to work on overall improvement. Securing people who are not interrupted from day-to-day support tasks and focusing on the main task improves efficiency.

Whereas both sub-teams share the on-call duty, some engineers are not allowed to do it by contract as they are not allowed to get paged. For those who cannot participate in on-call duties, we created what’s called a Toil Shift, which allows them to focus on resolving tickets in our backlogs instead.

This works well so far, but we will keep evolving as our business grows.

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.

The past year has given everyone lots to think about—about our priorities as people and as businesses. As the world retreated behind closed doors, we saw how shared interests and experiences can bring us together. As the world grappled with a common enemy, we witnessed just how differently individuals, communities and indeed entire countries can experience a situation. And as we faced seemingly unending obstacles to making it through the pandemic, we saw how making smart decisions based on data can drive meaningful solutions—fast.

That’s why we here at Google Cloud are so proud to partner Univision, the country’s leading Spanish-language content and media company. By partnering with Google Cloud, Univision will be able to accelerate growth across its portfolio of properties, deliver an enhanced user experience for Spanish-speaking audiences and provide the enterprise solutions needed to create the Spanish-language media company of the future.

According to Instituto Cervantes, there are over 580 million Spanish language speakers worldwide. Those viewers, like people everywhere, are avid consumers of streaming content. In Q4 of 2020 alone, viewing time for that content increased by 44%¹, and in 2020, from 50%² more sources. With that surge in demand, Univision needed a cloud provider whose infrastructure could reach Hispanic viewers around the world. With two-plus decades spent building out its network and data centers, as well as global content-delivery capabilities, Google Cloud has the infrastructure Univision needs to reach viewers across the Spanish-speaking world.

At the same time, with such a diverse audience for their content, Univision needs to target that content to viewers’ specific preferences. By applying Google Cloud’s artificial intelligence (AI) and machine learning (ML) technology across its content, Univision intends to personalize content based on shows users have previously watched, enhancing their engagement and viewing experience. 

And as Univision transforms the user experience, it can use Google Cloud’s data and analytics suite to garner deeper insights into its audience and forge stronger relationships with them on an individual basis. With Looker and BigQuery, Univision employees will have access to real-time data to help them make business decisions about programming.

Univision will also migrate video distribution and production operations to Google Cloud, where we’ll help them streamline media workflows and develop innovative new capabilities. Meanwhile, Google Cloud’s tight business and technical integration with other Google services will help ensure Univision reaches viewers on the device of their choice, wherever they are in the world. For example, in the coming years, Univision will expand its global YouTube partnership and will integrate with entertainment features on Google Search that help people better discover TV shows and movies. The company will also use Google Ad Manager for global ad decisioning and Google’s Dynamic Ad Insertion for PrendeTV and future video-on-demand offerings. Finally, Univision will distribute its content and services on Google Play across Android phones and tablets, as well as Google TV and other Android TV OS devices.

We’re thrilled to partner with Univision to help them reach the Spanish-speaking world with their content. With our cloud portfolio, we can help them reach individual viewers around the world, with personalized content that they can consume however they see fit. Best of all, together, we can help them achieve this vision fast, leveraging established cloud, content delivery, and data analytics technologies. You can learn more about the partnership here.