Guide
Licensing raw data vs prepared datasets
Quick answer
AI labs and data buyers usually prefer prepared datasets that are cleaned and structured, but businesses in the U.S., Canada, the EU, the UK, Australia, New Zealand, India, Singapore, South Africa, Latin America, the UAE and Saudi Arabia often find it easier to export raw records. The choice depends on whether the business wants to invest in data engineering to capture a higher price or use a managed licensing service to handle the preparation and transaction layer.
The facts, from public sources#
- Prepared datasets are cleaned and formatted, often resulting in higher pricing.
- Raw data is faster to export but requires more processing by the buyer.
- AI labs use their own pipelines to process data, so clean organization is often more important than final labeling.
The value of preparation#
Prepared data has been cleaned of duplicates, formatted consistently, and often includes metadata that makes it easier for AI models to ingest. Because this work saves the buyer time, prepared datasets typically command higher licensing fees.
However, preparation requires significant effort. A business must decide if they have the internal expertise to do this well or if they should provide raw records to a partner who can handle the processing.
Raw data advantages#
Raw data is the most direct export from a company's systems. While it may require more work from the buyer, it ensures that no valuable information is lost in the cleaning process. Some AI labs actually prefer raw data so they can apply their own specific preprocessing techniques.
The primary challenge with raw data is ensuring that personal details are removed before the data leaves the company's control.
The middle ground#
Many successful deals involve 'semi-prepared' data. The business provides organized exports, and a managed licensing service handles the PII removal and final formatting to meet the buyer's specific requirements.
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