Modernize your data architecture using Amazon RDS for PostgreSQL and Amazon QuickSight

The difference between success and stagnation often lies in how businesses analyze and act upon their data. A leading captive leasing business, offering financing programs for a range of offerings, was challenged to garner insights due to a disjointed legacy data system, which hindered its business growth. The company’s offerings include equipment, IT services, software, and solutions for over 150,000 customers in 18 countries. To modernize its leasing solutions and adapt to the evolving needs of its customers, the company needed an agile, scalable technology stack for its data system.

Using AWS services, they embarked on a comprehensive overhaul of their data infrastructure. This strategic transformation not only enhanced the company’s data processing capabilities but also set the foundation for advances in performance, productivity, and business velocity. This post discusses how this business transformed their growth trajectory and scaled business intelligence (BI) using Amazon QuickSight and Amazon Relational Database Service (Amazon RDS) for PostgreSQL on a modern data architecture on AWS.

Using AWS to build a modern data technology stack

Formed in 1984, this company helps clients lease and finance services and equipment, including IT support, security hardware, 3D printers, and more. With close to $3 billion in financial assets, over 700,000 leases, and more than 1,000 dealers, they generate massive amounts of data—which was managed using a mix of legacy technologies.

As they grew over time, data was managed in different ways by different teams. As a result, the company needed more visibility into its enterprise data. The infrastructure also lacked scalability, which made it difficult to keep pace with the company’s growth. They wanted to expand their business to encompass third-party equipment, but to facilitate this expansion, a complete digital transformation was necessary.

“Having a centralized environment where we could mine data and look at it thoughtfully using new tools, reporting, and analytics capabilities was really important for us as we moved forward,” says the company’s Chief Information Officer.

Beginning in 2021, they began a multiyear modernization journey, including a new, comprehensive data architecture on AWS.

Improving data visibility and increasing business velocity

Their modular data architecture is underpinned by Amazon RDS for PostgreSQL, a service that makes it straightforward to set up, operate, and scale a relational database in the cloud. This service acts as the main data storage solution, consolidating enterprise information in a unified place. Eight software as a service (SaaS) products are integrated with the architecture and are used to support their leasing and financing applications. The following diagram illustrates this architecture.

“On AWS, we have built a hub that records all the data transfers and exchanges that occur between these software as a service systems,” says the company’s Director of Enterprise Architecture, Data, and Analytics. “We also bring this data into AWS, using Amazon RDS for PostgreSQL as a data store. This has become the repository for a lot of reporting that we do for our customers and our dealers.”

To facilitate their reporting capabilities, they adopted the Enterprise edition of Amazon QuickSight, a service that powers data-driven organizations with BI at hyperscale. With the intuitive interface from QuickSight, users can transform their raw data into meaningful insights through interactive dashboards and analyses. This ability empowers decision-makers to make data-driven decisions based on near real-time information, thereby driving faster responses to market changes and propelling business velocity.

The AWS Lambda functions help ingest the data coming from various sources into the RDS for PostgreSQL database. Data is streamed into the database via Kafka, so the Lambda functions help prevent duplication and make sure data integrity is maintained. The Lambda function in the Storage and Processing section of the preceding architecture, which is sandwiched between Amazon Simple Storage Service (Amazon S3) buckets and AWS Transfer Family instances, is used for listing objects. Other postprocessing Lambda functions are named accordingly to identify their purpose.

“We’re able to take latency out of the system by providing near real-time reports through Amazon QuickSight,” says the company’s Chief Information Officer. “The interfaces and the usability around data visibility were well received, and people were very happy to have that kind of experience versus the legacy static reporting.”

With unified data from the customer’s RDS for PostgreSQL data store instance, they are able to automate processes that, in their legacy environment, required manual intervention. Previously, the customer used direct query, where they had limited people using dashboards and the data was refreshed in a scheduled format. With AWS, the manual intervention part was eliminated. Over 70% of their leasing deals are approved automatically, and for the remaining 30%, the company has developed algorithms to assess factors such as credit risk. This automation has vastly improved the speed and accuracy of the process, freeing their employees to focus on other tasks.

To facilitate data access for customers and dealers, they also built an external-facing portal using Amazon OpenSearch Service, which makes it straightforward to perform near real-time search, monitoring, and analysis of business and operational data. With these capabilities, users can access critical data, make informed decisions, process transactions, and even extract relevant reports. Dealers can also use the portal to build customer journeys and financing solutions that are branded independent, which has helped the company expand into private-label equipment.

“The dealers can log on and get reporting at a more detailed level than they could before,” says the company’s Chief Information Officer. “Our customers can log on and get statements and make payments. Internally, we have much more robust reporting that is available to our functional teams, which includes credit risk, finance, operations, and collections.”

Working toward a self-service data environment on AWS

By modernizing their data architecture on AWS, this finance company successfully upgraded to a more efficient and agile business model. They also want to integrate advanced technologies to further enhance their capabilities, including machine learning and natural language processing.

As they navigate their next steps, their data architecture on AWS will remain a critical component to shaping their future success.

“Everything about our business starts and ends with data,” says the company’s Chief Information Officer. “The velocity of our business has increased because we have this new technology on AWS.”

To learn more, visit Amazon QuickSight.

About the authors

Mrunal Daftari is an Enterprise Senior Solutions Architect at Amazon Web Services. He is based in Boston, MA. He is a cloud enthusiast and very passionate about finding solutions for customers that are simple and address their business outcomes. He loves working with cloud technologies, providing simple, scalable solutions that drive positive business outcomes, cloud adoption strategy, and design innovative solutions and drive operational excellence.

Aditi Singh is an Enterprise Technical Account Manager for the Hi-Tech and Semiconductor industry at AWS. She is a trusted advisor to customers on their cloud transformation journeys, focusing on security, cost, performance, reliability, and operational efficiency. Outside of work, she enjoys traveling, playing board games, and watching movies.