At a glance
An investment banking division's deal-analysis process depended on manual work and a rigid, disconnected data warehouse. Analysts had no unified view of deal data, which slowed decisions and caused miscommunication between industrial and product teams.
SoftServe redesigned the data architecture and added machine learning (ML) to automate opportunity, synergy, and market analysis. The solution combined six Microsoft Azure technologies, including Azure Data Factory, Azure ML Studio, PowerApps, Power BI, and Tableau. The result was faster data analysis, reduced manual effort, and standardized processes across industry, market, and deal analysis.
Client background
The client is the investment banking division of a global financial institution, responsible for deal origination, industry analysis, and market research. Its analysts relied on manual processes to evaluate opportunities, synergies, and market conditions before pursuing a deal, working from a data warehouse (DWH) that offered little flexibility or automation. SoftServe set out to reduce that manual effort with a flexible, ML-powered data solution built for faster analysis and stronger cross-team collaboration.
Business challenges
The division's existing data infrastructure could not keep pace with the complexity of deal analysis. Manual workflows slowed decision-making and made it difficult for industrial and product analysts to work from the same information.
These gaps showed up in four ways:
- The data warehouse lacked flexibility and automation for deal analysis
- Analysts had no 360-degree view of deal-related information
- Manual handoffs led to data loss between analyst teams
- Miscommunication between teams reduced overall analyst productivity
Activities & Solutions
SoftServe rebuilt the division's data platform around machine learning, replacing manual analysis with automated models and a shared collaborative workspace. Data scientists built the models after a discovery phase mapped the target architecture and validated each option through proofs of concept (PoCs) with real users.
The work broke down into six concrete steps:
- Conducted a gap analysis to define the future solution state
- Built ML models to automate opportunity, synergy, and market analysis
- Remastered the data warehouse for ML compatibility and flexible data slicing
- Developed a new front-end application with collaborative analyst capabilities
- Tested solution options with real users through multiple PoCs
- Integrated a Microsoft Azure-based technology stack to power the solution
Technology stack
The solution was built entirely on Microsoft Azure, combining cloud infrastructure, machine learning tooling, and business intelligence reporting into a single environment.
- Azure Cloud – core cloud infrastructure hosting the solution
- Azure Data Factory – data integration and pipeline orchestration
- Azure ML Studio – development and deployment of ML models
- PowerApps – front-end application for analyst collaboration
- Power BI – dashboarding and business intelligence reporting
- Tableau – additional data visualization and analysis
Value delivered
The engagement gave the division a single platform that analysts across teams now rely on for deal work.
That shift delivered distinct gains:
- Reduced manual effort in analysts' day-to-day deal operations
- Accelerated data analysis and interpretation across deal teams
- Improved data visibility and collaboration between analyst groups
- Standardized processes for industry, market, and deal analysis
- Extended the depth of data available for deal analysis
- Ensured consistency of data and research results division-wide
SoftServe's data science team continues to extend the platform, applying the same ML models to new analysis use cases as they arise.
