The traditional delivery model for large scale transformation efforts among financial services (FS) institutions often reveals significant data-related issues too late in the testing phase of the project life cycle, when the costs of changes are generally much higher and the ability to change the design is very limited. Organizations subsequently find themselves mitigating missed or bad data requirements, making reactive decisions and delivering results that fall short of goal, run late, exceed budget, or all of the above.
To help achieve transformations with shorter time frames and improved efficiency, we propose FS organizations adopt a data-first approach. This approach requires the identification and resolution of data gaps during the business and functional requirements phase, as compared to the traditional method, which typically focuses on identifying and resolving gaps after requirements are fully defined.
By leveraging a data-first approach, organizations may benefit by: