Learn how a Financial Services leader successfully transitioned, modernized, and advanced their analytics capabilities during a time of significant change.
When migrating from a particular servicing system to another — such as ERP, billing and payment systems, and CRM — organizations must often rebuild their critical analytics and reporting, while struggling to integrate content from the current and former applications.
Utilizing Eliassen Group’s data integration methodology and business intelligence expertise, Eliassen Group’s client—a leader in Commercial Auto Lending—established an integrated data environment to ensure core analytics would not be impacted by system changes or migrations, increased user adoption, and advanced its overall analytics capabilities.
Typically, organizations employ a variety of systems to operate their business and service customers (e.g. billing, payment processing, loan collection, etc.). These systems are often purchased from technology vendors, who, to improve performance and keep pace with new business practices, introduce significant changes to content. Similarly, organizations may, over time, replace their legacy servicing systems with newer, more flexible applications from other vendors.
In either case, the resulting change in content and data structures may break existing analyses and reports. How could the Commercial Auto Lending leader ensure its operational analytics would not be handicapped by significant changes to its operational systems?
By implementing standardized, master data structures to support analytics, the company’s IT team insulated business users from content volatility and ensured ongoing, integrated analysis of current and legacy system content.
The Commercial Auto Lender utilized a third-party system to service its dealer floor plan portfolio, which was experiencing explosive growth. To minimize credit losses and ensure continued expansion, the company was becoming increasingly reliant upon a growing number of analyses.
A significant, compulsory change in the servicing application would be introduced within the next 12 months. The company recognized that the analyses on which it was becoming reliant would likely need to be rebuilt once the new system was introduced.
The emerging analyses were being designed to consume data directly from the servicing system. Therefore, any changes in the system’s data elements, structure names, and/or structure compositions would render their existing analytics inoperable.
Analysts would need to stitch together from results multiple analyses.
As the company’s inventory of analytics increased, so too did the amount remediation that would be required. The company sought Eliassen Group assistance to preserve their analytics, mitigate rework, and improve their resilience to ongoing system changes.
The company sought an efficient, durable method of insulating its analysts from potential instability within its source system environment. They chose the Eliassen Group Data Analytics Team to implement the solution because of their deep experience with credit data, their automated generation of critical data architecture, and their ability to quickly propose a proven data model to support best-practice credit analytics.
Eliassen Group promptly proposed the master data structures that would properly support broad portfolio analysis. Eliassen Group then designed and deployed the architecture to collect, document, transform, and model data from the two instances of the servicing system.
Eliassen Group remediated the company’s initial analytics, converting them to a suite of modern, interactive dashboards. Eliassen Group supported the development of follow-on analyses with a variety of online, interactive metadata resources, training curricula, and hands-on development.
Delivered comfortably prior to the introduction of the new servicing system, the Eliassen Group solution delivered updated system content to users via their familiar, established reports and analyses. The resilience and agility of the company’s enhanced environment has been incredible.