The financial services and banking industry is rapidly transforming with advances in cloud technology, data analytics, and AI. Demand for analytics is ever increasing, making scalability key. Improved customer experiences rely on real-time insights and predictive analytics enabling banks to better predict future outcomes and discover opportunities for customers.
In this integration a bank that is using Vault Core can stream all of its raw data into BigQuery for any purpose that this data could serve. For example, a bank may want to aggregate some of this data or filter it to create reports, or conduct real-time analytics and build insights.
Improved data reconciliation
A common proposed usage for a Vault Core and BigQuery integration is to facilitate the reconciliation of data in Vault Core against the stored data in the bank. Without the need to build out a full reconciliation engine, the released integration code provides two simple proof-of-concept scripts to demonstrate how the data could be connected on both sides. This demonstrates how the data model used for BigQuery storage is reconcilable against Vault Core data.
Flexibility to manage any data into BigQuery
BigQuery can accommodate data from multiple sources for seamless analysis. Banks pursuing a multi-core strategy can easily upload data files from local sources, cloud storage buckets or Google Drive by taking advantage of the BigQuery data transfer service, data fusion plug-ins or leverage Google’s other industry-leading data integration partnerships.
A wide range of analytics
Our integration opens up the possibility for banks to leverage BigQuery’s built-in machine learning or BI capabilities and can further query data either in BigQuery or SQL by using additional dashboards like Looker or QlikView.
Thought Machine has built an integration to BigQuery which currently focuses on moving data for account creation, updates and postings in BigQuery. The integration service consumes separate Kafka topics for these event types, which are in turn transformed and loaded into BigQuery tables via the BigQuery client directly.
