Building a Data-Driven Investment Strategy Framework
A programmatic approach to M&A has consistently generated above-average returns. A data-driven investment strategy helps you prioritize your time, make better decisions, and create outlier long-term value.
Three spheres, one framework
Everyone has their own idea of what constitutes an investment strategy. While every industry has its own characteristics, and every investor has their own beliefs or preferences, we still believe there can be a standard approach to building a framework for investment strategy.
One approach relies on a system of spheres, slices, and parameters. Each sphere represents a broader category of data. Each slice permeates multiple spheres.
The seller is the inner sphere. The company is the middle. The market is the outer. Data flows between them to inform decision-making at every level.
For simplicity, this framework assumes software companies where the seller is the CEO (not PE-owned).
Interconnected data layers
Seller
Inner sphere — motivations and background of decision makers
Personal
Motivations
Company
Middle sphere — operational, financial, and product data
Entity
People
Offering
Finances
Market
Outer sphere — aggregated insights across the industry
Entity
People
Offering
Finance
Getting the data
Each sphere draws from different source types. Seller data comes from relationships and networks. Company data comes from documents, websites, and financial databases. Market data is aggregated from structured intelligence across your full entity model.
From ad-hoc to compounding
The framework only works if the infrastructure underneath can hold all three spheres in a single model. Habitat provides this — your entities, your documents, your market intelligence, and your AI all share one data model and one security boundary.
• Ad-hoc data collection for each deal
• Inconsistent evaluation criteria
• Manual research and analysis
• Knowledge lost between deals
• Difficulty comparing opportunities
• Every data source feeds one shared entity model instead of isolated tool-specific databases
• Evaluation criteria applied consistently and automatically across all deals
• Intelligence compounds across engagements — every deal makes the next one faster
• Cross-deal comparison through structured analytics and natural language queries
• Configurable entity model spanning all three spheres
• Document intelligence with full provenance
• Sourcing engine — 30M+ companies
• Prophet — taxonomy and competitive positioning
• Governed AI across a unified security model
• Every deal enriches the intelligence layer
• Consistent scoring rubrics applied automatically
• Cross-deal pattern recognition
• Institutional knowledge in the model, not in heads
• IC materials with provenance to source documents
More insights
From CRM Chaos to Investment Intelligence
How a billion-dollar software investor transformed their M&A operations in 30 days
CRM
Sourcing at Scale
From classification algorithms to a 30M+ company sourcing pipeline
Sourcing
Data for M&A Operations
Building data infrastructure across the full investment lifecycle
DataBuild your investment strategy framework
We design custom frameworks tailored to your investment thesis, integrated with the data infrastructure to support them.