When confronted with challenges, resilient organizations adapt and innovate to address changing customer needs. Through its partnership with Google Cloud, the Wisconsin Department of Workforce Development exemplifies this evolutionary approach. 

 Just a few months ago, challenges created by the antiquated unemployment insurance (UI) system threatened the agency’s ability to serve constituents who lost their jobs during the pandemic. Now, and in the months ahead, that continuing evolution will transform how the DWD serves its constituents with the modernization of their UI system, which includes customer experience workflows, predictive analytics, and streamlining and expediting paper applications.

Like many workforce development agencies nationwide, last year, DWD experienced a deluge of claims filed in response to the COVID-19 economic fallout. Unfortunately, DWD’s legacy UI infrastructure — largely written in the COBOL computing language and hosted on a mainframe server – was no match for the volume and complexity of cases. Wisconsin’s multiple technology systems required a large amount of manual processing, with staff using spreadsheets to manually calculate benefit adjustments. As a result, the state just couldn’t keep up with the surge of claims, and it needed to pivot quickly to a new solution to keep up with demand. 

DWD initially responded by staffing up call centers, hiring UI application adjudicators, and deploying other personnel–all told, hiring, contracting with, or reassigning more than 1,300 individuals. However, while this enabled DWD to respond to approximately 7 million calls per month, the massive number of incoming claims surged to create a  backlog of more than 750,000 claims. Legacy technology issues continued to significantly slow the query-response time. 

DWD leaders recognized that staffing alone was not the solution and that innovation was needed to overcome past, inadequate IT investment. To address the inherited issues, Wisconsin turned to Google Cloud. Working together, we were able to scale the state’s response to claims and speed up overall response time. We were also successful in screening out fraudulent claims so that the UI program could be administered–with integrity–to Wisconsinites who needed financial assistance.   

Year-to-date, Wisconsin has now disbursed $2 billion in unemployment benefits, in addition to successfully clearing its entire 2020 UI backlog. As a result of our partnership, the state is now processing an average of 157,000 claims each week and releasing most payments to citizens within two to three business days. Before the new system was in place, the response time could be weeks or even months.

Here is how Wisconsin and Google Cloud are modernizing the state’s current legacy system through a modular approach:

• Artificial intelligence (AI)/ machine learning (ML) for predictive analytics: Through the use of Google AI/ML, the state is creating predictive analytics based on historical data to shorten adjudication decision-making for UI claims, enabling it to release payments to eligible claimants faster. Comprehensive data models and confidence scores analyze the backlog data to determine the shortest route to approval and payments, with a high level of confidence and accuracy. DWD is using Google Cloud technology to identify where in the process the claimant was getting stuck in one of the “hold buckets” for processing a UI claim. Using Google Cloud’s data and AI/ML tools, Wisconsin is able to identify problem areas and quickly resolve those issues. This informed DWD’s rewrite of the UI claim application process. And it also helped DWD identify fraudulent claims.
• Document AI (DocAI) for streamlining paper applications: DWD is also partnering with Google Cloud to streamline paper applications and fax documents as part of UI claims processing–enabling documents to be submitted online instead of by fax or hard-copy mail. Our DocAI solution helps Wisconsin staff make faster decisions by rapidly extracting critical data from documents, saving time, and removing manual processes, which allows employees to focus on high-priority activities. 

These modernization steps in Wisconsin are resulting in a bold new vision for state unemployment systems across the United States. Through a combination of design thinking, deep partnership with state officials, and modern technology, DWD’s solutions are tailored to maximize benefits to the constituents they’re designed to serve. By joining forces with Google Cloud and utilizing modernizing technology to make informed, data-driven decisions, Wisconsin DWD is helping residents have a better experience that is easier to understand and navigate–all while better serving the community overall.

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.

Retailers are becoming more skilled at making individual customers feel heard and valued. This is a necessity given the fact that 66% of respondents to a McKinsey survey stated that they expect email marketing messages to be tailored to their needs. While marketing personalization expertise is growing, it’s still difficult to manage, especially at scale.

Ocado Retail, one of the world’s largest dedicated online grocery retailers, delivering to over 645,000 customers in the U.K., has made personalization integral to its success. 

Let’s look at how Ocado retail worked with Google Cloud and partner Cognizant to develop a new data platform to power its personalization efforts from the ground up. 

Unifying data for more holistic, powerful personalization

To achieve its goals of personalization at scale, Ocado Retail needed a central data warehouse that could turn all forms of merchandising, advertising, business, and customer intelligence data into actionable insights. It wanted a means to accelerate customer segment identification, as well as the ideation and launch of relevant campaigns. 

“We standardized on Google Cloud, including BigQuery, as the foundation for our data platform because we knew it was the right solution for now and the future,” says Kieren Johnson, Head of IT at Ocado Retail. “We have an incredibly lean team and we needed a partner with exceptional expertise to help build an ambitious enterprise data warehouse to provide powerful insights. Cognizant was also the clear choice to help us get there.”

Cognizant worked closely with Ocado Retail to make sure its expertise in Google Cloud and other technologies aligned with Ocado Retail’s vision to drive more advanced personalization at higher scales using machine learning. The partner helped build the foundation on BigQuery, and then incorporated other Google Cloud tools such as Cloud Run, Vertex AI, and Vertex AI Natural Language to provide no-ops, all-code warehousing, and analytics capabilities.

Cognizant also took advantage of the Google Cloud Partner Success Services (PSS) program to ensure best practices were being followed throughout the project. PSS provided advisory services that guided Cognizant through the highly complex process of building the new data warehouse for Ocado Retail on Google Cloud.

Building the enterprise data platform in this way allows Ocado Retail to leverage the full power of cloud-based analytics while maintaining a lean team. It also allows the company to greatly scale up its personalization efforts.

Making customers feel valued at every touch

The work Ocado Retail has done with Google Cloud and Cognizant has positioned it to make its growing customer base feel valued, understood, and supported in every interaction. Before launching the project with Cognizant and Google Cloud, Ocado Retail was only able to run a couple of campaigns per week and knew it lacked optimal insight into each campaign’s efficacy. 

“We now run 10 times the number of campaigns we used to with the help of the data platform Cognizant built on Google Cloud,” says Kieren. “We run multiple campaigns every day for different customer segments, and all of that increased activity is entirely driven by data insights. The positive impacts on our marketing and customer service performance have been clear. We’re now working to expand what we do.”

Ocado Retail has enjoyed solid growth since the new data platform went live, including a 13% rise in active customers during fiscal year 2022, and has also seen a reduction in churn. It attributed these improvements to being better able to tailor products and communications to specific customer preferences.

Throughout the project, Cognizant supported data clean up while maximizing the scalable, flexible, and future-proofed data analytics infrastructure offered by Google Cloud.

Increasing data-driven actions

Ocado Retail plans to provide more data-driven insights to its commercial suppliers through a product called Beet Insights. So far, Beet Insights offers suppliers with intelligence about how their products are performing on the shelves. The result has been improving the role data plays throughout the supply chain, from production to purchase and beyond.

“By putting real-time insights about costs, marketing spend, and supply-funded activities into the hands of our commercial team and buyers, we are better positioned to improve our profits,” says Kieren. “At the same time, building data analytics into every part of our business will allow us to build on our personalization efforts.”

From the project’s inception, Ocado Retail ensured that the platform would be scalable and dynamic. Now, it is working to feed more data sources and streams into the warehouse to accelerate time to insights. Ocado Retail believes this next step in the evolution of the platform will unlock even more opportunities to initiate high-impact programs that transform personalization and every customer interaction.

Learn more about what Google Cloud and partners like Cognizant can do for your customer intelligence and personalization.