Challenge: Enabling Transformation to a “Cloud-First” Business
One of the ten largest bank holding companies in the US was investing heavily in digital transformation. Infinitive was selected to facilitate an enterprise migration to cloud and the creation of a new streaming data ecosystem that enables real-time insights, decisions and machine learning. Infinitive provided project and program management support while defining and executing strategic program roadmap initiatives across five different workstreams.
Solution: Strategy and Application Design and Deployment to Drive Business Goals
Infinitive established a scalable stakeholder engagement model and continues to partner with business and technical stakeholders to onboard top streaming initiatives and enable a seamless user experience for data producers and consumers. Specifically, the Infinitive team:
- Developed a robust front-end/back-end application to support cloud migrations. The application was built in the cloud to support tracking, monitoring, and modeling application to support our client’s objective to move all applications to AWS.
- Managed testing and deployment of application into production and oversaw first run data management and collection
- Defined intake and onboarding procedures while managing increased demand as developers scaled the foundational streaming infrastructure
- Partnered with business owners and data ecosystem teams to establish and enforce clear standards, data management procedures and consistent governance processes
Program Results & Business Benefits: Clearer Insights and More Informed Decision-Making
In moving critical parts of their business to the cloud and using the tools developed by Infinitive, the bank benefitted from:
- Clearer insights into the costs and risks of the migration to the cloud
- More efficient and lower-risk migration processes, as different parts of the organization used the Infinitive tools to prepare data, applications and others assets for migration
- Connections among thousands of data producers and consumers to fully understand data lineage and relationships, deprecation and decommissioning dates and platform migration plans
- Identification of true-source raw data to enable better analytical model performance and machine learning
- Full transparency for timeline and risk metrics for data producers, consumers and program resources to support prioritization and planning efforts
- Cost savings from rationalization of under- and un-utilized data assets for retirement or archiving