Our client, one of the United State’s top 10 banking technology providers had a mission to transform everyday transactions into meaningful relationships for more than 500 financial services client companies and 18 million (and counting) individual consumers in our increasingly connected digital world. 

More than ever, financial institutions must create unforgettable customer experiences at every touchpoint - and data is key for this. 

Our client’s customers in mid-sized banks and credit unions struggled to get a 360° view of their entire businesses, unable to understand the whole puzzle of data and customer experience. They were missing out on driving bottom-line results and discovering the most efficient ways to create a more profitable business while delighting customers and earning long-term loyalty. 

There was an opportunity to create a scalable, best-in-class business intelligence and data analysis platform that would give their clients access to superpowered, enterprise-quality data and analytics capabilities that had traditionally been out of reach.

Our client wanted to empower its customers to analyze information in brand-new ways to supercharge businesses and earn long-term loyalty. We knew that transforming data into understandable, meaningful insights that map to business goals was the answer. 

Together, we envisioned a transformative business intelligence and data analytics platform that would empower their massive roster of clients, seamlessly integrating our client’s proprietary data with each of its customers to create a business intelligence engine. 

After beginning with a pilot, this platform would deliver a dynamic new revenue driver for our client once established. They’d be able to further differentiate their standout suite of BI business tools with the capability to license it to any client, no matter their size or experience with data analysis, to give access to the capabilities of enterprise-level BI for the first time.

Governance and privacy

Anticipating hundreds of financial institution clients onboarding sensitive information, it was crucial for us to manage the entire data lifecycle efficiently. We were responsible for gathering the data from the source into a data warehouse and presenting it flawlessly to end business users.

Security was our top priority as we started creating data pipelines into a reporting and analytics schema. Handling incredibly sensitive personal and financial information was led by our data governance team to ensure all regulatory and compliance requirements were met or surpassed. 

Business intelligence and data engineering

Once safely and securely in the data warehouse, our engineering teams planned for the data to be translated, validated and verified into reporting standards that are simple for business partners to digest and strategically mapped to actionable insights. We worked closely with several of our client stakeholders to understand the actual business needs and opportunities within their data sources.  

We then developed a measurement framework of what data to include and the business-oriented takeaways our client most wanted to pull from it. 

Self-service analytics

One of the challenges of bringing in disparate data sources into the BigQuery data warehouse was that each of our client’s customers has different business needs. Leveraging Looker’s semantic modeling language, LookML, was crucial to allow each customer to configure their data models to their specific business needs.

Appnovation, in conjunction with our client and their pilot customer, developed customizable models to allow the customers themselves to directly modify, append and extend their specific business logic.

Data visualization

To present data experiences that improve innovation, productivity and decision-making, we used Google's Looker platform to create relevant, attractive and accessible data dashboards that are easy to understand.  The dashboards automatically aggregates and summarizes data, giving a consistent and governed real-time view of combined data.

Productization

After successfully implementing the data models and pipelines, the focus turned to how to productionize this data platform. This meant implementing automation and CICD using reusable code.

The Looker IDE integrated seamlessly with Git services for LookML code management, which was mandatory as many developers would be working on these data objects.  Working with our client’s Devops, we devised a deployment strategy using their internal Github environment for core product development.  As for customer self-service development, Cloud Source, a git repo linked to the customer’s Google Cloud account as setup to govern custom code changes or core code updates pushed from our client’s Github repositories.

Terraform was used to provision and manage the Google Cloud infrastructure.  Cloud Build was then used to replicate the core product.  This copies standardized data pipelines, BigQuery schemas, LookML code repositories, Looker visualizations and other objects into the new customer GCP environment. Replication and onboarding new customers onto the data platform is easy thanks to this approach.

Training and services

We provided training and knowledge transfer documentation for the Digital Banking platform to our client’s product team, as well as training materials for their customers who sign up for the program.

Ultimately, this brand-new platform has given our client and its customers the power to make better business decisions more easily. After expanding the pilot, users are now more meaningfully understanding how demographics use specific products, building nuanced profiles of customers' behavior and creating more intelligent strategies to boost business results.

With our client, we’re monitoring and adding new data sources to uncover even more valuable information. With a roster of new data sources and real-world feedback on how a client takes advantage of the platform, our next development phase will further evolve its capabilities as the platform rolls out to a growing list of customers.