Transformation & Integration Leading To 87% Reduction In Data Processing Time
Problem Statement:
At Stilbon, we understand that businesses generate revenue through various sales pipelines involving a large variety of products. This requires the analysis of data from multiple complex sources to estimate pipeline health and forecast demand. Additionally, the data must be seamlessly integrated into downstream systems with appropriate access rights for real-time viewing and consumption.
Solution:
To address this challenge, the Stilbon team brainstormed a comprehensive solution that enables the analysis of leads, opportunities, and other sales data from multiple sources. Our solution involves funneling the data through Google Cloud’s BigQuery pipelines into Google Cloud Storage. We then use Google Cloud Dataproc to transform the data using Python, Scala, or R, applying business logic as needed. The transformed data is pushed into Google Cloud Storage and then into the production BigQuery tables to which downstream systems integrate for their business needs.
Stilbon team implemented data integration from BigQuery to Looker dashboards using which business intelligence reports are generated for the customers to visualize data trends and anomalies.
Our solution provides various process improvements and regular notifications cautioning about data anomalies that have created a strong impact in maintaining data consistency and healthy pipeline.
Outcome:
Our solution has resulted in an 87% improvement in performance and reduced overall data processing time from 7.5 hours to 1.5 hours. Our notifications help in addressing unhealthy pipeline and data issues promptly, thereby ensuring seamless data integration across systems. With our solution, businesses can gain deep insights into their sales pipelines, accurately forecast demand, and make data-driven decisions with confidence.