Our client's investment banking division spent a significant amount of time on manual work for internal deal analysis. The existing data warehouse (DWH) solution lacked flexibility, automation, and 360-degree visibility of required information. This hindered team productivity and led to data loss and miscommunication between industrial and product analysts.
SoftServe's mission? Provide a flexible and powerful solution for improved collaboration, business intelligence, and streamlined data analysis and interpretation. Speed data analysis and improve crossteam collaboration with ML-powered solutions.
During the discovery phase, SoftServe performed an in-depth gap analysis and defined a future solution state's clear vision. The steps included:
- Development and integration of ML models for industry and deal analysis processes
- Remastering the existing DWH to satisfy ML model needs and provide more data slicing flexibility
- Development of a new front-end application with collaborative capabilities
- Integration of an analytics solution
SoftServe provided several PoCs to test solution options with real users and select an optimal combination of "Lego bricks" to address primary business needs. Our data science experts developed several ML models to automate opportunity definition, synergy analysis, and market analysis.
The implemented solution was based on Microsoft Azure stack, including Azure Cloud, Azure Data Factory, Azure ML Studio, PowerApps, PowerBI, and Tableau.
With the introduction of a new ML-powered DWH solution, the client can:
- Reduce manual efforts for analysts' operations
- Accelerate data analysis and interpretation
- Improve data visibility and collaboration between analysts
- Standardize processes for the industry, market, and deal analysis
- Extend deal analysis data depth
- Ensure consistency of data and research results
LET’S TALK about how the SoftServe data science team can empower you with comprehensive ML solutions tailored to your specific business needs.