Building a Scalable, Cost-Effective Prospect 360 Platform for a Leading Private Equity Firm

Business Objective

Our client is a mid-market Private Equity firm that invests in growth-oriented, innovative companies operating in the Business Services and Consumer Sectors. The client wanted to build a Prospect 360 platform customized as per their focus areas and investment philosophy over cloud.

They wanted to

  • Identify gaps in the current data and build data acquisition strategy including paid data products, data extraction frameworks from documents and public sites
  • Design and build data infrastructure on the cloud with end-to-end data management, automated data ingestion, storage, transformation, quality checks, and governance
  • Very limited data available to make a good assessment in the early stages of evaluation
  • Disjointed systems, data spread across multiple excel, PDF files, etc. Extremely difficult to retrieve data/analysis from prior deals
  • Manual data extraction from company documents and spreadsheet-based models/reports, resulting in longer analysis time, and loss of information

Solution Methodology
  • Analyzed current data on coverage and accuracy and identified new data types required e.g., digital information, triggers, social sentiments, etc.
  • Analyzed vendor universe for individual data types based on coverage, accuracy, ease of collection, and cost. Recommended final set of vendors/products for purchase.
  • Designed and built a cost-efficient and scalable AWS based data platform
  • Set up automated data extraction pipelines for externally sourced paid and public data, documents, and stored in an easy-to-use format.
  • Built data transformation and quality checks rules, periodic health reports, and governance structure for effective data management
  • Set up a cost-effective maintenance process for smooth operations
Business Impact
  • Custom Prospect 360 with more than 500 raw data elements for watchlist firms

  • ~10x increase in the prospect database

  • Enabled downstream BI and data science use cases

  • Scalable, cost-effective, and highly customizable platform

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