The customer is a professional services firm with a focus on financial auditing and advisory services.
The engaged department would be supporting business operations with data-driven intelligence around their client base.
Intenda’s Fraxses solution provides unparalleled features for aggregating and blending data from various sources, while simultaneously implementing a delivery system through which application developers can consume this data.
Professional service firms require support from ancillary operations to make up-to-date business decisions and mitigate risk when maintaining revenue streams and efficiently pursuing new ones.
The hypothesis is that the customer’s account teams will benefit by having the most up to date market information on their clients.
By aggregating financial performance metrics and market sentiment and then benchmarking those results against those of their peers, teams will be able to make better business decisions, faster.
The challenge in the past has been that this data comes from multiple sources. It takes many development hours to build logical communication, and delivery between these sources and the application becomes overwhelming.
Building those communication layers with multiple developers, spanning multiple disciplines, also means that maintaining the application over time will become increasingly challenging. This is where data virtualization comes to the rescue.
We used Fraxses to federate to over 40 REST API endpoints, the enterprise data warehouse, HDFS, and several application databases (SSMS). Essentially, Fraxses was used as the data engine for this business intelligence application.
Notably, Intenda delivered this solution while working alongside the client’s software engineering team.
Fraxses developers can be placed into the agile development framework with ease, and since it is a low-code/no-code solution, businesses can audit the entire data lineage of any data object, and code review becomes metadata review.
With Fraxses being a low-code/no-code, metadata-driven platform, data source authentication, connection logic and data manipulation are a configuration exercise.
REST API data can be consumed in a few ways. One integration might retrieve third-party data to present to a charting library, while another endpoint would surface data to be stored and processed on a schedule.
Virtual data objects allow for this third-party data to be stored, linked to homegrown data warehouses, and effectively blended in real-time.