Last week, Silverline hosted a webinar about the future of integration and data as a part of our WFH series. Silverliners Jill Harrison, VP of Silverline Ventures, and Greg Grinberg, Senior Delivery Director and Certified Technical Architect, reflected on how integration strategy has evolved over the past decade across our Financial Services portfolio.
Evolution of integration pattern design
The early Salesforce integration landscape was much simpler and limited with one API- SOAP API. There was typically one integration pattern used which required nightly and hourly batches to get that data into the system. This limited scope posed challenges of transporting data from point A to point B to achieve improved business operations.
Salesforce has prioritized investments in new technology to accelerate the pace of innovation primarily through data virtualization such as Salesforce Connect, Heroku Connect, and Salesforce Canvas. This shift allows for handling large data volumes more effectively, in real time, and provides a shortcut while remaining compliant with security and regulatory standards for data storage. Greg Grinberg dives into the strategies and use cases for implementing these data virtualization tools.
Current trends of integration strategy
For Financial Services companies who are investing in modernizing their integration strategy, there are two major trends: The first trend is the movement to an API driven integration architecture, which provides a shift from point to point integration into a central data warehouse to a modern web approach with decoupled systems that support different consumers of API. The second trend is leveraging streaming processing of data rather than a batch model which improves the timeliness of data, allows for surfacing data from many systems and continues to compound agility within the development workstream.
What does the future look like?
The future of technology for data integration allows Financial Services organizations to drive decisions through data insights. Data Visualization technology allows for external data feeds and Salesforce system data to come together for an analytics strategy that is actionable. It provides a more sophisticated user experience for extraction of key information to drive decision making.
Utilizing Machine learning is a key part of becoming a data driven organization. Overtime decision making based on data can be automated using AI algorithms such as Einstein Analytics. With this foundation in place and good data, it helps to derive value and operational efficiency.