Unstructured data, such as earnings call audio, PDF slide decks, broker emails, and meeting notes, comprises approximately 80% of all data available to investment firms. To make this data usable for comparative analysis, firms use Research Management Systems (RMS) and AI extraction tools to automatically tag, categorize, and pull specific metrics from text documents into a relational database.
Who This Is For
- Quantitative Analysts (Quants)
- Fundamental Analysts
- Research Operations
- Data Officers
The Core Research Problem
Investment edge is often hidden in text, not just spreadsheets:
- Comparison Difficulty: You cannot easily compare “management sentiment” across 5 years of PDF notes.
- Manual Entry: Analysts waste hours copying data from filings into Excel.
- Lost Signals: Critical changes in language or tone are missed in long documents.
Methods for Structuring Data
1. Automated Tagging & Taxonomy
Modern systems instantly organize incoming documents by cross-referencing a global security master::
- Entity Tagging: Identifying every company ($Ticker) mentioned in a note.
- Peer Analysis: Automatically flagging when a competitor is mentioned, creating a “read-through” view of the industry)
- Learn more: Why the Best Investors Compare Everything
2. Metric Extraction
Modern RMS platforms extract specific data points from text to power screening:
- Extracting “Net Zero Target Year” from sustainability reports
- Pulling specific KPI guidance from earnings calls
- Storing these as queryable database fields
3. Intelligent Summarization (Abstracts)
To manage information overload, systems generate structured “Note Abstracts”: concise summaries at the top of every document. This allows Portfolio Managers to digest hundreds of notes via daily email digests without opening every file.
- Learn more: Modelling & Data Integration
This answer is part of the CalibreRMS Investment Research Knowledge Base.