Operationalize Data Sharing at Scale
With a centralized data platform, financial services and insurance organizations can unlock the type of insights they need to succeed. But legacy, on-premises infrastructures need to be augmented or even replaced with a centralized data platform in the cloud in order to make it happen.
Data centralization brings together critical data sources into one place so it can be more effectively managed and accessed.
Data Platforms for Financial Services
Data Platforms for Financial Services
Top Use Cases
Top IT Challenges
Data Platform Accelerators from NorthBay and AWS
When it comes to using data to drive growth and profitability, many established financial services and insurance providers know they need to keep up with their younger fintech counterparts. As a result, line-of-business are leaning on IT to deliver the insights they need from an ever-growing number of data sources.
Data Platform Accelerators from NorthBay and AWS are a unique and proven set of processes and best practices that are purpose-built to speed up time-to-value.
With NorthBay Data Platform accelerators, IT teams can:
Watch this 3-minute video to learn more about NorthBay and AWS Data Platform Accelerators.
Top 10 Questions from FinServe CTOs NorthBay
Top 10 Questions from FinServe CTOs NorthBay
- How can I enable our line-of-business leaders to leverage AI/ML and other innovative technologies to optimize customer retention?
- What are the best financial services datasets for solving business challenges using analytics and machine learning?
- How can I support enterprise-wide data sharing while enforcing data governance?
- How can my IT team that has limited cloud and AI skills build and run a data platform?
- Is modernize in-place a short-term fix or a longer-term solution?
- Which is the right approach for analytics modernization: A centralized data lake or decentralized data mesh?
- Why are so many financial services CIOs/CTOs choosing the cloud, especially AWS Cloud, for their data platform?
- Are there proven reference architectures to accelerate my modernization journey?
- Have fintech innovators found new ways to solve the data quality conundrum?
- What are the top use cases for generative AI in the financial services industry?
NorthBay Accelerators for Planning, Building, and Scaling Data Platforms
NorthBay Accelerators for Planning, Building, and Scaling Data Platforms
How to Solve the Data Quality Conundrum
How to Solve the Data Quality Conundrum
Adding new data sources to legacy data warehouses is a complex and time-consuming process (six-plus months, on average, for each new source). Purpose-built for Amazon Web Services analytics environments, the NorthBay Data Quality Engine streamlines the data ingestion process by:
- Automatically auditing existing datasets
- Identifying relationships between different datasets
- Implementing custom rules for data consistency
- Ensuring easy integration with AWS analytics
- Detecting anomalies in real-time
AWS: The Foundation for Analytics and Innovation
AWS offers more data services than any other cloud provider, making it easier to aggregate and centralize data, maintain data lineage, and store any type of data at scale.
NorthBay has been 100% AWS-focused for nearly a decade, and we’ve worked hand-in-hand with AWS Professional Services to implement enterprise-wide data lakes and analytics solutions for leading financial services firms.
JAM Sessions By Use Case
JAM Sessions By Use Case
NorthBay JAM Sessions are 10-day, no-cost or low-cost immersive workshops where your team deep dives with our AWS experts. We work hand-in-hand to stand up a live AWS instance with your data and your use case.
By planning, architecting, and developing a rapid prototype solution together with NorthBay, you can fast-forward your data platform initiative to tackle the most in-demand use cases.