Goes beyond the bank's relational data warehouse and Hadoop.
Proactively identifies Opportunities and Risks as actionable items.
Reduces dependence on Data Scientists and IT for Analytics.
The Cordiant Graph Data Lake(™) goes beyond the rigid relational data warehouse and the unwieldy Hadoop Big Data family Data Lake in managing and analyzing data at enterprise scale with flexibility, insight and speed.
The Graph Data Lake will integrate both structured and unstructured data of the bank in the Operational Data Layer, the common data layer serving the bank’s transformative applications for the always-on economy.
The Cordiant Graph Data Lake brings together potentially all the data silo-ed in multiple subsystems in the bank into a single, coherent, connected data store.
The Cordiant Graph Data Lake overlays on the bank’s CBS as an Operational Data Layer and helps meet the distributed, real-time, always-on demands of the 21st century banking enterprise.
The Operational Data Layer ensures that transformative banking applications that score high on speed, availability, consistency, scalability and security can be built and rolled out with zero disruption to the Core Banking System of the bank.
With the Graph Data Lake in place and data consolidated in the connected data store, patterns emerge from the data. A multitude of opportunities and risks, which were hitherto hidden due to the silo-ed nature of bank’s legacy relational database systems are unravelled in this connected data.
Automated robotic processes run 24/7 through the Graph Data Lake to intelligently uncover, identify and flag every single opportunity and risk as they emerge, into actionable items. These are then auto-assigned as tasks through the Cordiant Task Manager to designated groups and individuals within the bank for fulfillment of opportunities and for mitigation of risks so identified.
The Graph Data Lake allows users to explore and analyze their data intuitively without specialized data analytics or query language skills.
With the Graph Data Lake in place, the entire data of the bank will be coherently connected for any authorized person to run queries on the graph and get the results straight-away instead of having to depend on data scientists and IT to deliver analytics.
The Graph Data Lake democratizes big data analytics and creates “citizen data scientists” in the bank. Every employee becomes a potential data scientist as the graph can be queried easily for the information they are looking for, subject to access permissions based on the specific role of the employee.
We use Apache Spark, which is integrated with DataStax Enterprise to update data into DSE Graph from the bank’s Line of Business applications and sub-systems. These updates will happen first with the historical data and then happen in intervals as short as milliseconds based on the specific nature of the data and how the bank wants their data to be reflected in the Graph Data Lake.
The bank may start small and extend the graph over time to widen the scope of their Graph Data Lake.
The Graph Data Lake being a single, self serviceable repository of all data will support a variety of use cases across departments.
For instance, in a 360 degree view of customers that evolves from the Graph Data Lake, different departments can go to the same repository of data to determine each point of interaction the customer had with the bank and its various services and identify opportunities for improvement of the customer experience, for cross-selling, for providing better customer service and lots more.
Traditional KYC processes are built on structured customer data by applying fixed rule sets embedded in application program code to identify outliers.
Instead, the Cordiant Graph Data Lake provides a flexible infrastructure for connecting customer information from the bank’s bouquet of disparate legacy systems by combining both structured and unstructured data into a single, coherent data store for KYC and other regulatory audit and compliance purposes.
Regulatory compliance requirements vary across international jurisdictions. They also evolve at quite a rapid pace, with new regulatory requirements being put in place almost every week in multiple jurisdictions where the bank operates.
Cordiant Graph Data Lake enables the bank to quickly map new rules as they evolve into its Operational Data Layer and at lower costs with little or no changes in the application code.
Several automated robotic processes which run as microservices do most of the heavy lifting to identify and flag compliance outliers.
And compliance auditors can explore and analyze the connected data intuitively without specialized data analytics and query language skills.
With the Cordiant Graph Data Lake in place, the bank can solve analysis problems at hitherto impossible scale, efficiency and time to value.
The Graph Data Lake overrides the rigid relational Data Warehouse approach as well as the unwieldy Hadoop Big Data family Data Lake approach to solving analytical problems in the bank.
End users can now perform self-service data discovery on the bank’s Graph Data Lake instead of having to wait through the long cycles of iterative data preparation and extraction.
All Cordiant solutions run from an Operational Data Layer that sits on top of the bank’s core banking data store.
The Operational Data Layer built on DataStax Enterprise scores on speed, availability, consistency, scalability and security while accessing and serving critical financial services data.
DataStax Enterprise(™) is the data management platform of choice of 9 of the top 15 global banks for building and rolling out transformative banking applications for the always-on economy.